2017 Summer Abstract of Undergraduate Research

Swanson School of Engineering

Undergraduate Research Program 2018

Welcome to the 2018 Issue of the Swanson School of Engineering (SSOE) Summer Research Abstracts! Every year, the SSOE invites undergraduates to propose a research topic of interest to study for the summer and to identify a faculty mentor willing to serve as a mentor and sponsor for their project. Students work on innovative research with leading scientists and engineers while spending their summer at Pitt, other institutions or abroad! Five students spent their internship in Singapore at the National University of Singapore and four students at Ort Braude College of Engineering in Israel. Within the Pitt community, several departments outside of SSOE hosted summer students: Anesthesiology, Business Administration, Mathematics, Medicine, Neuroscience, Ophthalmology, Orthopedic Surgery, and Pharmacy. It was exciting to see the broad range of substantial fields that engineering students became involved in! There are multiple programs that offer summer research opportunities to the SSOE undergraduates, the largest of these being the Summer Internship Program jointly sponsored by the Swanson School and the Provost. This year, the program funded nearly 70 students, with generous support from both the SSOE and the Office of the Provost. Additional support was provided by a gift from the Kennametal Foundation and the Covestro Foundation, both companies work closely with SSOE students and faculty. Further, the Swanson School study abroad program assisted students who participated the international internships listed in the previous paragraph. The following individual investigators also provided support: Steven Abramowitch, Howard Aizenstein, Bryan Brown, Andrew Bunger, Youngjae Chun, Xinyan Tracy Cui, Morgan Fedorchak, Mark Gartner, Alan George, Karl Johnson, Thomas Lozito, Kacey Marra, Partha Roy, Warren Ruder, Marc Simon, Gwendolyn Sowa, Jonathan Vande Geest, David Vorp, and Courtney Sparacino-Watkins. We are grateful for their support and to each of the faculty mentors who opened their laboratory to these undergrads this summer. We would also like to acknowledge the faculty reviewers from each of the six SSOE departments for their assistance in reviewing the proposals. Thank you for your time in this valuable program! As required of the internship, students submitted poster abstracts to a professional conference. A primary conference submission is Science 2018, hosted at the University of Pittsburgh every October. Bioengineering students, in particular, often submit their work to the Biomedical Engineering Society (BMES); and all students were encouraged to submit their work to any professional conference(s) that their respective mentor(s) suggests. Interns and other summer students are also invited to submit abstracts for consideration for a full manuscript in Ingenium: Undergraduate Research in the Swanson School of Engineering. Ingenium provides undergraduates with the experience of writing manuscripts and graduate students, who form the Editorial Board, with experience in peer-review and editing. We hope you enjoy this compilation of the innovative, intellectually challenging research that our undergraduates took part in during their tenure at SSOE. In presenting this work, we want to acknowledge and thank those faculty mentors who made available their laboratories, their staff, and their personal time to assist the students and further spark their interest in research. David Vorp, Associate Dean for Research Mary Besterfield-Sacre, Associate Dean for Academic Affairs

Mentor Primary Department(s)

Student

Student Department

Mentor(s)

Conrad Li

Bioengineering

James Ibison, Pitt and Hongliang Ren, Anesthesiology NUS

Madeline Preece

Bioengineering

Steven Abramowitch

Bioengineering

Nishita Muppidi

Bioengineering

Howard J. Aizenstein

Bioengineering

Timothy Wroge

Bioengineering

Aaron Batista, Pitt and Hongliang Ren, Bioengineering NUS

All mentors are faculty at the University of Pittsburgh unless otherwise noted.

Bioengineering

Title (*abstract witheld) 3D CONVOLUTIONAL NEURAL NETWORKS FOR GLIOMA PROGNOSIS ASSESSING THE ABILITY TO QUANTIFY COLLAGEN ARCHITECTURE IN THE VAGINA USING X-RAY MICROTOMOGRAPHY ASSESSING BRAIN RESERVE USING A MACHINE-LEARNING BRAIN AGE PREDICTION MODEL REINFORCEMENT LEARNING MOTION CONTROL PARADIGM FOR MINIMALLY INVASIVE ROBOTIC SURGERY DEVELOPMENT OF A HIGH THROUGHPUT BIOREACTOR COMPRESSION CHAMBER FOR RODENT MECHANOBIOLOGY

Nicholas Gabriel

Bioengineering

Kevin Bell

Tyler J. Bray

Bioengineering

Harvey Borovetz, Pitt and Orit Braun Bioengineering Benyamin, Ort Braude College

DEVELOPMENT OF THE HANDLE GRIP OF A WALKER FOR HEMIPLEGIC POST-STROKE PATIENTS

Natasha A. Gilbert

Bioengineering

Harvey Borovetz, Pitt and Orit Braun Bioengineering Benyamin, Ort Braude College

BIOMECHANICAL REHABILITATION: ACCESSIBILITY OF MUSICAL SYSTEMS-DRAWING MUSIC

Hannah C. Geisler

Bioengineering

Bryan N. Brown

Bioengineering

ANGIOGENIC RESPONSE TO IL4 ELUTING COATINGS IN MESH TISSUE EXPLANTS

Tyler Martin

Bioengineering

Bryan N. Brown

Bioengineering

A PERIPHERAL NERVE EXTRACELLULAR MATRIX HYDROGEL ENHANCES RETURN TO RUNSTION AFTER SCIATIC NERVE GAP AND CRUSH INJURY

McKenzie Sicke

Bioengineering

Bryan N. Brown

Bioengineering

ANGIOGENIC RESPONSE TO ABDOMINAL AND VAGINAL POLYPROPYLENE MESH IMPLANTS IN A RABBIT MODEL

Bioengineering

ASCORBATE PRE-OXIDIZATION FOR ENHANCED DOPAMINE DETECTION WITH SQUAREWAVE VOLTAMMETRY AT PEDOT/fCNT CARBON FIBER ELECTRODES*

Noah C. Freedman

Bioengineering

Xinyan Tracy Cui

All mentors are faculty at the University of Pittsburgh unless otherwise noted *Denotes abstract withheld to protect intellectual property

Student

Jacob Meadows

Kelsey Toplak

Student Department

Bioengineering

Malik Snowden

Lauren Grice

Bioengineering

SYSTEM TO RECORD THERAPUTIC DELIVERY WINDOW OF STOCHASTIC VIBROTACTILE STIMULATIONS IN INFANTS WITH NEONATAL ABSTINANCE SYNDROME

Bioengineering

Tamer Ibrahim, Pitt and Sarit Sara Sivan and Marcela Bioengineering Viviana Karpuj, Ort Braude College

A qPCR BASED ASSAY FOR DETECTING MYCOPLASMA CONTAMINATION IN CELL LINES

Christi L. Kolarcik

Bioengineering

INVESTIGATION INTO HINDLIMB MUSCLE NEURAL CIRCUITRY IN THE MOUSE

Bioengineering

REVERSAL OF CHONDROCYTE AGING TO AUGMENT CARTILAGE REGENERATION IN THE BODY

Bioengineering

OPTIMIZATION OF DECELLULARIZATION OF SKELETAL MUSCLE VIA INFUSION FOR MUSCLE RETENTION FOLLOWING PERIPHERAL NERVE INJURY

Bioengineering

INFUSION OF BECELLULARIZED SKELETAL MUSCLE FOR INTERVENTION IN MUSCLE ATROPHY FOLLOWING PERIPHERAL NERVE INJURY

Bioengineering

DIFFUSION TENSOR IMAGE ANALYSIS REVEALS IMPROVED MICROSTRUCTURAL INTEGRITY IN STROKE DAMAGED BRAINS TREATED WITH EITHER NEURAL STEM CELL OR PHYSICAL THERAPY

Katherine R. Rohde Bioengineering

Kristen Byrd

Title (*abstract witheld)

Mark Gartner

All mentors are faculty at the University of Pittsburgh unless otherwise noted.

Bioengineering

Zachary Fritts

Sreyas Ravi

Mark Gartner

Mentor Primary Department(s)

INFANT FEED THICKENING CHARACTERIZATION AT CHILDREN'S HOSPITAL OF PITTSBURGH

Mentor(s)

Bioengineering

Hang Lin

Kacey Marra

Michael Modo

Nikhita Perry

Undeclared

Michael Modo

Bioengineering

Aidan Dadey

Bioengineering

Partha Roy

Bioengineering

Claire E. Kraft

Bioengineering

Warren C. Ruder

Bioengineering

All mentors are faculty at the University of Pittsburgh unless otherwise noted *Denotes abstract withheld to protect intellectual property

COMBINED NEURAL STEM CELLS AND PHYSICAL THERAPY IMPROVE SOMATOSENSORY CORTEX ACTIVITY AFTER STROKE MYOCARDIN RELATED TRANSCRIPTION FACTOR'S ROLE IN CELL MIGRATION A MECHATRONIC SYSTEM FOR THE MAGNETIC MANIPULATION OF BIOMEDICAL SPECIMENS

Student

Student Department

Mentor(s)

Mentor Primary Department(s)

Janet Canady

Bioengineering

George Stetten

Bioengineering

Jake Donovan

Bioengineering

George Stetten

Bioengineering

Jonathan Vande Geest

Amy Hill

All mentors are faculty at the University of Pittsburgh unless otherwise noted.

Title (*abstract witheld) VALIDATING FINGERSIGHT WITH A 3D INFRARED TRACKING SYSTEM MICRO-BLIP: A NEW TOOL FOR INSTRUMENTATION EDUCATION

Bioengineering

INVESTIGATION OF MULTIPLE STRATEGIES IN MODELING AN EXPANDABLE STENT BY FINITE ELEMENT METHOD

Oreoluwa Odeniyi

Undeclared

Jonathan Vande Geest

Bioengineering

ACCURACY OF TWO-PHOTON POLYMERIZATION WITH VARYING OBJECTIVES AND POWER

Angela Cinaglia

Bioengineering

David A. Vorp and Bioengineering Timothy K. Chung

A MACHINE LEARNING TOOL FOR PREDICTING ABDOMINAL AORTIC ANEURYSM RISK: A PILOT STUDY*

Mechanical Trevor M. Kickliter Engineering and David A. Vorp Materials Science

Meara Sedlak

Bioengineering

Katherine Stevenson Bioengineering

Bioengineering

ADVENTITIAL DELIVERY OF THERAPEUTIC CELLS TO LARGE ANIMAL AORTAS

Justin S. Weinbaum Bioengineering

COMPARISON OF CELL SEEDING QUALITY OF POROUS, BIOMIMETIC, TUBULAR SCAFFOLDS FOR VASCAULAR TISSUE ENGINEERING FABRICATED BY TWO DIFFERENT METHODS

William J. Federspiel

Bioengineering

EFFECT OF PIVOT-BEARING SURFACE ROUGHNESS ON THROMBUS FORMATION

Trevor YoungHyman

Business Administration

TRANSITIONING TO A DEMOCRATIC ORGANIZATIONAL STRUCTURE: AN ETHNOGRAPHY OF A COOPERATIVE CO-WORKING SPACE LOCALLY INDUCED SEMICONDUCTOR-TO-METAL TRANSITION IN TWODIMENSIONAL CRYSTALS USING AN IONOMER

Jacquelyn N. Barbush

Industrial Engineering

Aaron Woeppel

Chemical and Petroleum Engineering

Susan Fullerton

Chemical and Petroleum Engineering

Zheng Guo

Chemical and Petroleum Engineering

Karl Johnson

Chemical and Petroleum Engineering

EVALUATION OF THE ACCURACY OF DUH-HAYMETHENDERSON THEORY

Angela Leo

Chemical and Petroleum Engineering

John A. Keith

Chemical and Petroleum Engineering

UNLOCKING ENERGETICALLY EFFICIENT WATER OXIDATION FOR OZONE DISINFECTANTS WITH COMPUTATIONAL CHEMISTRY*

All mentors are faculty at the University of Pittsburgh unless otherwise noted *Denotes abstract withheld to protect intellectual property

Mentor Primary Department(s)

Student

Student Department

Mentor(s)

Joseph Hamm

Chemical and Petroleum Engineering

Sachin Velankar

Chemical and Petroleum Engineering

DELAMINATION OF SOFT THIM FILMS FROM DYNAMIC WRINKLING SUBSTRATES

Nina S. Chang

Civil and Environmental Engineering

Andrew P. Bunger

Civil and Environmental Engineering

EXPERIMENTAL STUDY OF THERMO-HYDRO-MECHANOCHEMICAL (THMC) BEHAVIOR OF GEOLOGICALLY ACTIVATED CEMENTING MATERIALS

Jiangnan Zheng

Civil and Environmental Engineering

Andrew P. Bunger

Civil and Environmental Engineering

CHEMICAL WEAKENING OF GRANITE AND SANDSTONE

Elaine Yates

Civil and Environmental Engineering

Vikas Khanna, Pitt Civil and and Eric Ben Dvaid Environmental and Isam Sabbah, Engineering Ort Braude College

MICROPLASTIC ADSORPTION AND ANALYZATION

Ava Chong

Murat Akcakaya, Pitt and Tank Kok Zuea, NUS

VISION FIELD TESTING WITH VIRTUAL REALITY

Ronen Orland

Shaoming Zheng

Undeclared

Siddharth Balakrisninan

Electrical and Computer Engineering

Alan D. George

Electrical and Computer Engineering

Dekwuan Stokes

Electrical and Computer Engineering

Robert Kerestes

Electrical and Computer Engineering

Gordon Bryson

Bioengineering

Youngjae Chun

Industrial Engineering

Electrical and Computer Engineering Electrical and Computer Engineering

Samuel Dickerson Wei Gao, Pitt and Ge SS, NUS

All mentors are faculty at the University of Pittsburgh unless otherwise noted.

Electrical and Computer Engineering Electrical and Computer Engineering Electrical and Computer Engineering

Title (*abstract witheld)

DESIGNING A DIELECTROPHORESIS SIMULATOR MODEL AND ANALYSIS OF MASK-RCNN DEEP LEARNING FOR HYPERSPECTRAL IMAGE CLASSIFICATION ON EMBEDDED PLATFORMS LOAD DETECTION ALGORITHM FOR SMART GRID APPLICATIONS EVALUATING OCCLUSION SUCCESS OF ESOPHOCCLUDE PROTOTYPES IN COMPARISON TO DIAMETER AND RADIAL FORCE

Jack Hastings

Bioengineering

Youngjae Chun

Industrial Engineering

IN VITRO VIABILITY TESTING OF PH SENSOR INCORPORATION IN TONGUE PROSTHETIC ASSIST DEVICE FOR TREATING DYSPHAGIA

Teressa Chambers

Bioengineering

David Swigon

Mathematics

EARLY-STAGE SEPSIS IN PIGS AS A BASIS FOR MATHEMATICAL MODELING*

Ryan Black

Mechanical Engineering and Hessam Babee Materials Science

Mechanical Engineering and Materials Science

MODAL ANALYSIS OF HUMAN BRAIN DYNAMICS AFTER HEAD IMPACT

Ian Piper

Mechanical Engineering and Hessam Babaee Materials Science

Mechanical Engineering and Materials Science

MODELING FLOW OVER AN AIRFOIL USING PROPER ORTHOGONAL DECOMPOSITION

All mentors are faculty at the University of Pittsburgh unless otherwise noted *Denotes abstract withheld to protect intellectual property

Mentor Primary Department(s)

Student

Student Department

Audrey Chester

Mechanical Engineering and William Clark Materials Science

Mechanical Engineering and Materials Science

XPROJECT CURRICULUM RESEARCH

Emelyn Jaros

Mechanical Engineering and William Clark Materials Science

Mechanical Engineering and Materials Science

X-PROJECTS AND MAKERSPACES

Daniel Yates

Mechanical Engineering and William Clark Materials Science

Mechanical Engineering and Materials Science

X PROJECTS: HANDS-ON STUDENT-LED PROJECTS

Alexandra Beebout

Mechanical Jung-Kun Lee, Pitt Engineering and and Tan Swee Materials Science Ching, NUS

Mechanical Engineering and Materials Science (Pitt)

PURPLE BACTERIAL PROTEINSEMICONDUCTOR HYBRID PHOTOELECTROCHEMICAL CELLS AND QUANTUM DOT SOLAR CELLS

Phillip A. Williamson

Mechanical Engineering and Wissam S. Saidi Materials Science

Mechanical Engineering and Materials Science

MODELING AND ENERGY CALCULATIONS OF PEROVSKITE METHYLAMMONIUM LEAD IODIDE GRAIN BOUNDARIES

Zachary E. Egolf

Mechanical Engineering and Nitin Sharma Materials Science

Mechanical Engineering and Materials Science

WEARABLE UPPER LIMB ELBOW EXOSKELETON

Thomas Hinds

Mechanical Engineering and Nitin Sharma Materials Science

Mechanical Engineering and Materials Science

DESIGN AND FABRICATION OF A FLEXIBLE ELECTRODE ARRAY FOR FUNCTIONAL ELECTRICAL STIMULATION OF THE CALF

Zihao Huang

Mechanical Jeffrey S. Engineering and Vipperman Materials Science

Mechanical Engineering and Materials Science

HEARING AMPLIFIER WITH BUILT-IN INDEX SELECTABLE FILTERS

Lydia Kuebler

Mechanical Engineering and Guofeng Wang Materials Science

Mechanical Engineering and Materials Science

INFLUENCE OF NITROGEN DOPING ON ELECTROCATALYTIC ACTIVITY OF FeN4 EMBEDDED GRAPHENE

Claire Tushak

Undeclared

Medicine

ANALYZING RIGHT VENTRICULAR RESPONSE TO SACUBITRIL/VALSARTAN IN PULMONARY HYPERTENSION

Medicine

METFORMIN SUPPRESSES PROINFLAMMATORY AND CATABOLIC GENE EXPRESSION IN RAT ANNULUS FIBROSUS

Mentor(s)

Marc Simon

All mentors are faculty at the University of Pittsburgh unless otherwise noted.

Title (*abstract witheld)

Rahul Ramanathan Bioengineering

Gwendolyn Sowa

Eric Cecco

Bioengineering

Courtney SparacinoMedicine Watkins

KINETIC CHARACTERIZATION OF NITRITE REDUCTION TO NO BY THE MOLYBDOPTERIN ENZYME MARC2

Jimmy Zhang

Chemical and Petroleum Engineering

Courtney SparacinoMedicine Watkins

THE PHYSIOLOGICAL ROLE OF MITOCHONDRIAL AMIDOXIME REDUCING COMPONENT 2

All mentors are faculty at the University of Pittsburgh unless otherwise noted *Denotes abstract withheld to protect intellectual property

Student Department

Student

Mentor(s)

Mentor Primary Department(s) All mentors are faculty at the University of Pittsburgh unless otherwise noted.

Title (*abstract witheld) UTILIZING MINIATURE FLUORESCENT MICROSCOPY FOR IN VIVO CALCIUM IMAGING IN THE NUCLEUS ACCUMBENS CONTROLLED RELEASE OPTIMIZATION OF CEFTRIAXONE AND N-ACETYL CYSTEINE FOR TRANSTYMPANIC DELIVERY LIZARD VERSUS MOUSE PROLIFERATION OF NEURAL STEM CELLS IN THE SECONDARY NEURAL TUBE

Xhoni Pashaj

Bioengineering

Yan Dong

Neuroscience

Katherine Dunkelberger

Bioengineering

Morgan V. Fedorchak

Opthalmology

Danielle Danucalov Bioengineering

Thomas P. Lozito

Orthopaedic Surgery

INHIBITION OF BIOCHEMICAL SIGNALS AFFECTS TAIL REGENERATION IN LEPIDODACTYLUS LUGUBRIS

Christian De Moya

Bioengineering

Thomas P. Lozito

Orthopaedic Surgery

Sara Kenes

Bioengineering

Thomas P. Lozito

Orthopaedic Surgery

MAMMALIAN CELLS INJECTED INTO LIZARD TAILS SURVIVE AND RECONSTITUTE REGENRATED TISSUES

Thomas P. Lozito

Orthopaedic Surgery

ISOLATION AND COMPARISON OF SUPER-HEALING MOUSE AND LIZARD MACROPAHGE PHAGOCYTIC CAPABILITY

Pharmacy

KINETICS OF MALIGNANT AND BENIGN MINERAL DEPOSITION IN COLLAGEN-MIMETIC HYDROGEL MATRICES

Sean P. Tighe

Bioengineering

Nithya Narayanan

Bioengineering

Shilpa Sant

All mentors are faculty at the University of Pittsburgh unless otherwise noted *Denotes abstract withheld to protect intellectual property

3D CONVOLUTIONAL NEURAL NETWORKS FOR GLIOMA PROGNOSIS Conrad Li, Mobarakol Islam, Nicolas Kon Kam King, and Hongliang Ren Department of Bioengineering, University of Pittsburgh Department of Biomedical Engineering, National University of Singapore Department of Neurosurgery, Singapore General Hospital Email: col24@pitt.edu INTRODUCTION Gliomas are the most common primary brain malignancies with highly variable degrees of aggressiveness. Low-grade glioma (LGG) are the most prevalent benign brain tumors in adults and high-grade glioma (HGG) or glioblastoma multiforme (GBM) are the primary adult malignant brain tumors1. Recent studies have shown that optimal utilization of pre- and intraoperative aids can maximize glioma resection, directly correlating to significant improvements for patients in: overall survival (OS), progression-free survival, and quality of life. Currently, preop assessment of tumor progression is difficult because of the intrinsic molecular, immunohistochemical, and genetic heterogeneity in the tumor core sub-regions in addition to the many shapes and locations the tumors develop in1. The lack of commonality between different cases of glioma poses a significant challenge for prognosis and removal. Multi-modal 3D convolutional neural networks (CNNs) can improve glioma resection outcomes by providing more effective preop OS predictions. LITERATURE REVIEW Artificial intelligence has an ensemble of effective algorithms and models ideal for classification tasks similar to OS prediction. Support vector machines (SVMs), random forests (RFs), and neural networks are the most heavily implemented and have shown tremendous success2. SVMs and RFs have yielded promising results on OS prediction. In a niche case, an SVM achieved 100.0% classification accuracy on the BraTS â&#x20AC;&#x2122;17 data3. However, scarcity of MRI data limits partitioning of training and validation sets suggesting that this accuracy is not replicable

over a larger and more diverse dataset. This is supported by an RF classifier achieving 60-70% accuracy on an older BraTS dataset3. Regardless, both studies show that the task of OS prediction with reasonable accuracy is achievable using AI. SVMs and RFs have good accuracy but do not utilize the high dimensional features from the inherent 3D structure of MRI data2. CNNs are shift invariant and are easily applicable for high dimensional feature extraction via 3D convolutions. Therefore, development of a fully 3D classifier for OS prediction may potentially be a robust model compared to other methods. METHODS Multimodal Brain Tumor Segmentation (BraTS) â&#x20AC;&#x2DC;17 benchmark MRI data was used in combination with the subject age parameter to train a 3D CNN classifier for OS predictions. Each patient had MRI in four modalities: T1, T1c (gadolinium contrast agent), T2, and FLAIR with shape of 240x240x155 voxels. Voxel dimensions were 1x1x1 mm3. Total HGG/GBM patients with OS data were n=163, split into n=150 for the training set and n=13 for the validation set. The validation set was used to evaluate predictor performance for three demographics as visualized in Figure 1: short-term survivors (<6 months), mid-survivors, and longterm survivors (>18 months). Single patient multimodal MRI data and corresponding OS labels were fed into a basic sequential 3D CNN classifier using batch normalization, fully connected layers, and dropout.

Figure 1: OS subject data: long-term (left), mid (middle), short-term (right) survivors. Green represents edema, yellow non-enhancing core, and red necrotic core. RESULTS Successful OS prediction using even a simple model would show that 3D CNNs are a viable method for MRI classification tasks. Using features from multiple image modalities and high-level abstractions from the intact 3D structure of the MRI data can yield more substantial OS predictions. Model refinement can improve the effectiveness of implementing 3D CNNs for prognosis, improving accuracy and efficiency in comparison to current methods, allowing AI can be a practical tool for glioma prognosis. DISCUSSION Even with a lack of developed 3D CNN models and computational limitations, there is tremendous potential for 3D CNNs in medical image classification and analysis due to the abundance of intrinsic high- dimensional spatial features in MR data. CNNs to improve current clinical routine in treating both high-grade glioma (HGG), including glioblastoma multiforme (GBM), and low-grade glioma (LGG). Practical implementation of AI can

facilitate tumor prognosis and lead to maximal gross resection to improve patient overall survival and quality of life. REFERENCES 1. Havaei et al. Medical Image Analysis 35, 18-31, 2015. 2. Menze et al. IEEE Transactions on Medical Imaging 34, 10-22, 2015. 3. Osman, MICCAI Quebec, 2017. ACKNOWLEDGEMENTS Special thanks to Mobarakol Islam and Rajiv for providing a guidance and assistance at every phase of the project. Thank you to my principal investigator, Hongliang Ren, for facilitating the entire process, as well as Dr. Nicolas Kon Kam King and Angela See An Qi for assisting with the operating room setup. Thank you to NUS and the Department of Biomedical Engineering for providing the resources to make this project possible.

Assessing the Ability to Quantify Collagen Architecture in the Vagina Using X-ray Microtomography Madeline Preece1; Katrina Knight1, PhD; Spandan Maiti1, PhD; Raffaella De Vita2, PhD; Pamela Moalli3, MD, PhD; Steven Abramowitch1, PhD 1 University of Pittsburgh, Pittsburgh, PA, 2Virgina Tech, Blacksburg, VA, 3Magee-Womens Research Institute, Pittsburgh, PA Email: mep118@pitt.edu INTRODUCTION X-ray microtomography (microCT) can be a useful tool in visualizing collagen architecture in soft tissue structures. The architecture can later be quantified and used to understand the mechanics of soft tissues. In addition, microCT can be used to visualize whole organs, eliminating the need for the slicing required for microscopy and histology techniques which could change the stress distribution within the tissue. However, soft tissue is not radio-opaque and proper staining methods must be identified to visualize collagen with microCT. Here, the capacity of iodine potassium iodide (I2KI), phosphotungstic acid (PTA), and phosphomolybdic acid (PMA) to increase the contrast of collagen structures in the vagina using microCT is investigated and the identity of those structures is postulated. METHODS For the first trial, vaginas from three rats were dissected within 24 hours of sacrifice and cut into three ring sections (proximal, mid, distal), totaling 9 samples. The samples were added to a 10% w/v formalin solution and stored until staining. I2KI was added to 3 samples for the following concentrations and durations (3.75% for 7 days, 10% for 12 hours and 4 days, respectively). PMA and PTA were each added to 3 samples at 1% for 12 hours, 10% for 4 days, and 50% for 1 day, respectively. 5mL of stain was used for staining each tissue sample. After staining, the samples were scanned using a microCT using the following parameters: full rotation, 360 projections, high magnification, no binning, 12µm

voxel size, 60kV, 250µA, and an exposure time of 6 seconds. For the second trial, vaginas from two additional rats were utilized to provide 6 samples. All procedures were the same as the first trial except that only PTA and PMA were utilized based on the results from the first trial. For the second trial, 5% w/v, 10% w/v, and 20% w/v concentrations of both PTA and PMA were added to the samples for 4 days. In addition, samples were rinsed with water prior to scanning. DATA PROCESSING The resulting images from the sample with the most visible features that resembled collagen fascicles were segmented using Seg3D2 (University of Utah, Salt Lake City, Utah, USA) and the diameter was measured at 3 points (proximal, middle, and distal) along the length of 3 features using the measuring tool in MeshLab 2016 (ISTI_CNR Research Center, Pisa, Italy). Then, the diameter measurements were averaged together for each feature. RESULTS The tissue with the most visible features that resembled collagen fascicles was the sample stained with 20% w/v PTA for 4 days. The diameters measured along three features of this sample are described in Table 1. Figure 1 demonstrates the cut ends of the tissue samples due to sectioning appear to result in more stain being collected near the distal cut end, occluding the observed features on that end. Figure 2 demonstrates the deformation of the tissue stained with PMA.

Table 1: Average diameters of three features Features 1 2 3

Proximal Diameter (mm) 0.88 0.15 0.72

Middle Diameter (mm) 0.16 0.09 0.28

Distal Diameter (mm) 0.09 0.13 0.18

Average Diameter (mm) 0.38 ± 0.44 0.12 ± 0.03 0.39 ± 0.29

Figure 1: Segmentation of proximal tissue sample

DISCUSSION In this preliminary study, PTA was the most effective stain to visualize features resembling collagen fascicles in the vagina using microCT. I2KI proved to work well to visualize the entire tissue sampleâ&#x20AC;&#x2122;s geometry, but it was not ideal for visualizing features resembling distinct fascicles which is consistent with previous findings [2]. Although it is known for its high affinity to collagen, PMA was observed to deform the thin vaginal tissue samples by causing the edges to flare out and shrinking which is shown in samples 3 and 1 in Figure 1 [1,2]. This shrinking caused wrinkling within the tissue samples that interfered with identifying the features which is shown in sample 2 in Figure 1. PTA staining resulted in the least deformation of the sample and allowed for distinct features to be observed. Features were primarily observed to be oriented longitudinally with fewer features oriented circumferentially. While the feature diameters are consistent with the diameter of collagen fascicles, comparative histological techniques are required to determine if the features are indeed fascicles. In the future, staining will be performed on nonsectioned vaginas to minimize the collection of stain

Figure 2: Tissue samples stained with PMA

at the cut end and to explore the native architecture of the features. Additional PTA concentrations and staining durations will be used to further optimize the protocol. REFERENCES 1. Balint, R., T. Lowe, and T. Shearer. "Optimal Contrast Agent Staining of Ligaments and Tendons for X-Ray Computed Tomography." PLoS ONE, vol. 11, no. 4, 2016. 2. Disney, C. M., et al. "Visualising the 3D Microstructure of Stained and Native Intervertebral Discs using X-Ray Microtomography." Sci Rep, vol. 7, no. 1, 2017.

ACKNOWLEDGEMENTS Thank you to the University of Pittsburgh Swanson School of Engineering, Office of the Provost, Department of Bioengineering, The John G. Rangos Sr. Research Center at UPMC Childrenâ&#x20AC;&#x2122;s Hospital of Pittsburgh, Dr. Abramowitch and NSF Award #1511504 for making this project possible.

ASSESSING BRAIN RESERVE USING A MACHINE-LEARNING BRAIN AGE PREDICTION MODEL Nishita Muppidi1, Maria J Ly2,3, Helmet T Karim2, Akiko Mizuko2, William E Klunk2, Howard J Aizenstein1,2,3 1 Department of Bioengineering, 2Psychiatry and 3Neuroscience, University of Pittsburgh, PA, USA Email: nrm45@pitt.edu, Web: http://www.gpn.pitt.edu/ INTRODUCTION Alzheimer's disease (AD) is an age-related neurodegenerative disease and is the sixth leading cause of death in the United States affecting approximately 5.5 million people. However, despite the prevalence of the disease, there are no available interventions to treat or delay the progression of AD. Amyloid deposition is one of the first detectable biomarkers used to predict an individual’s risk for developing AD. However, there is significant individual variability in clinical presentation with respect to amyloid burden. Brain reserve, a measure of brain structural and functional integrity, may be a possible explanation since greater brain reserve may provide resilience to pathological processes such as amyloid. As such, we propose to perform machine-learning based brain age prediction to estimate brain reserve. Discrepancies between an individual’s chronological age and the predicted brain age may be representative of the resilience or vulnerability experienced by the individual to pathology. Individuals with a predicted brain age greater than their chronological age would be expected to have a lower brain reserve than those with a predicted brain age less than their chronological age, thereby making those individuals more vulnerable to AD pathology. Therefore, individuals with a lower brain reserve would be expected to experience a faster progression of AD and have worse cognition than those with a higher brain reserve. Previous brain age prediction models have been significantly limited by their probable inclusion of amyloid-positive older adults. Our brain age prediction model will only include imaging data from cognitively normal, amyloid-negative adults in order to ensure that the brain age can be accurately correlated to the subject’s chronological age without the confounding effects of AD pathology. Therefore, our primary goal was to create a

machine-learning model of brain age by utilizing structural MRI data collected from cognitively normal, amyloid-negative older adults. MATERIALS AND METHODS The structural T1 images from 143 cognitively healthy older adults (60-85 years) were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using the Statistical Parametric Mapping (SPM12) software package, structural images were segmented into multiple tissue classes. This process outputs gray matter density in a standard anatomical space. Greater gray matter density can indicate greater volume and/or cortical thickness. An Ordinary Least Squares (OLS) regression model and a Support Vector Regression (SVM) model were generated to predict brain age from grey matter density images. Cross validation was performed with 3 repeats, 10 folds, and 1000 permutations to assess significance. We then estimated the relative contribution of each individual voxel to the prediction model. RESULTS The OLS regression model predicted brain age with a correlation of 0.55 ± 0.03 for r and 0.28 ± 0.02 for r2, mean squared error of 78.91 ± 1.20, and mean absolute error of 7.74 ± 0.04. The SVM regression model predicted brain age with a correlation of 0.57 ± 0.02 for r and 0.32 ± 0.02 for r2, mean squared error of 76.62 ± 0.38, and mean absolute error of 7.62 ± 0.03. DISCUSSION This research proposes creating a machine-learning model of brain age by using structural MRI data collected from cognitively normal, amyloidnegative older individuals to use a measure for brain reserve. Brain reserve, a measure of brain structural and functional integrity, may be a possible explanation for the individual variability in clinical presentation of AD with respect to amyloid burden.

Our preliminary data demonstrates the feasibility in creating a brain age model in a highly restricted population. Our OLS and SVM models show predictions with a mean absolute error comparable to existing brain age prediction models. We aim to improve the model by including other imaging modalities (i.e. T2 FLAIR and resting state functional MRI), optimizing hyperparameters such as kernel scale and box constraint for the SVM regression model, and expanding the data set by including other cohorts. REFERENCES 1. Liem F. et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage. 2017; 148:179-88.

2. Cole J.H. et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017; 163:115-24. ACKNOWLEDGEMENTS We would like to thank the Swanson School of Engineering, the Office of the Provost, Dr. Aizenstein, 5P01AG025204, 2T32MH019986, and 5T32AG021885, Alzheimerâ&#x20AC;&#x2122;s Disease Neuroimaging Initiative (ADNI), University of Pittsburgh Alzheimerâ&#x20AC;&#x2122;s Disease Research Center (ADRC), University of Pittsburgh Medical Scientist Training Program (MSTP), and the University of Pittsburgh Center for Neuroscience (CNUP) for their support of this project.

REINFORCEMENT LEARNING MOTION CONTROL PARADIGM FOR MINIMALLY INVASIVE ROBOTIC SURGERY Timothy Wroge1,2, Mobarakol Islam2, Xiao Xiao2, Hongliang Ren2 Department of Bioengineering, University of Pittsburgh, Pittsburgh PA 2 Department of Biomedical Engineering, National University of Singapore, Singapore Email: timothy.wroge@pitt.edu 1

INTRODUCTION State of the art Reinforcement Learning (RL) algorithms were applied to a simulated continuous control robotic environment to demonstrate the application of RL in surgery. RL has shown remarkable improvements in recent years over traditional programming methods for creating human level control in simulated environments, games, and robotics. This research provides a paradigm for the introduction of RL applications in the future so that other novel tasks can be taught to robotic devices to augment surgical tasks and consequently, allow surgeons to perform operations safely.

displacement. The robotic control slider design shown in Figure 1 can reach any point within its circumference of the Âą some tolerance assumed to be 0.1mm in this experiment. The actual agent trained in this experiment used an artificial neural network to generate the Q values known as a Deep Q Network (DQN)

RL is based on a Markov decision process that describes how the next state of the environment is based on the current information about the environment that is passed to the agent and the action the agent performs. A reward or punishment is passed to the agent after an action. The goal is to optimize the time discounted reward over a training iteration. A branch of RL known as Q Learning, that is leveraged in this research, makes the agent learn the values of current actions given a current state. METHODS All experiments were executed in python and within the physics simulation environment MuJoCo [1] provided using a student license. The approach taken in this research is an application of Q Learning called Hindsight Experience Replay (HER) drawn from OpenAI research and their GitHub repository [2], [3]. HER is an algorithm where the agent learns to complete a task by watching the same task being performed and redefines the goal to learn in sparse rewards spaces. This greatly reduces the training time for robotic control environments which allows for more flexibility. The task assigned to the agent was to learn to reach a point in 3D space. The robotic system used in this experiment in later iterations will be exploiting the 2 degrees of freedom (DOFs) given by motor 1 for rotation and motor 2 for

Figure 1: Figure 1 Robotic Control Paradigm. The agent is initialized with a target. The agent will send a rotation motor voltage and displacement motor voltage to the robotic surgical instrument (bottom of figure). The position of the slider will be sent from the electromagnetic tracker to the RL agent and the RL algorithm will iterate. The voltages are then passed to the surgical instrument and the method repeats.

DATA PROCESSING The Q Learning algorithm leveraged in this experiment is derived from the Bellman Equations which show that the value of the current state denoted V (s) can be written recursively in terms of the value of the current state and all future states and actions. This is shown in Equation 1, below. This

simply shows that the value of the current state is the maximum over all the actions, a, with reward, R, added to the discounted sum of the values of the next states times the probability of getting there, shown by đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;(đ?&#x2018; đ?&#x2018; , đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;, đ?&#x2018; đ?&#x2018; â&#x20AC;˛) [4]. đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;(đ?&#x2018; đ?&#x2018; ) = max ďż˝đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;(đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;, đ?&#x2018; đ?&#x2018; ) + đ?&#x203A;žđ?&#x203A;ž ďż˝ đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;(đ?&#x2018; đ?&#x2018; , đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;, đ?&#x2018; đ?&#x2018; â&#x20AC;˛ )đ?&#x2018;&#x2030;đ?&#x2018;&#x2030;(đ?&#x2018; đ?&#x2018; â&#x20AC;˛ ) ďż˝ (1) a

đ?&#x2018; đ?&#x2018; â&#x20AC;˛

The Q Learning portion is defined by the nonmaxed version shown in Equation 2, below. đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;(đ?&#x2018; đ?&#x2018; ) = đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;(đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;, đ?&#x2018; đ?&#x2018; ) + đ?&#x203A;žđ?&#x203A;ž ďż˝ đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;(đ?&#x2018; đ?&#x2018; , đ?&#x2018;&#x17D;đ?&#x2018;&#x17D;, đ?&#x2018; đ?&#x2018; â&#x20AC;˛ )đ?&#x2018;&#x201E;đ?&#x2018;&#x201E;(đ?&#x2018; đ?&#x2018; â&#x20AC;˛ ) đ?&#x2018; đ?&#x2018; â&#x20AC;˛

(2)

This Q function tends to be very useful in off policy algorithms because it can provide the values of all future states and can be approximated through direct experience of the environment, shown in Equation 2. In this sense, the transition functions T and reward functions R do not need to be directly known in order to satisfy this relationship and directly learn the Q values. Interestingly, the values of V can also be intuited from the values of Q by just taking the max of the Q value of the next state [4]. RESULTS The results of this algorithm are very promising as shown in Figure 2. Despite the experiment

being completed in a simulated environment, the complexity of the task and the proficiency observed by the agent gives validation to RL being used in continuous control environment for robotic surgical tasks. DISCUSSION By leveraging the advancement of artificial intelligence in the realm of RL, it is possible to create fine automated robotic control that aims to assist surgeons during specific motor tasks. Future research will implement this control paradigm in the surgical slider within the lab in the National University of Singapore. There is hope to incorporate different modalities to be used as features for the model such as force sensors, and video input to guide the robot. REFERENCES 1. Todorov et al. IEEE International Conference on Intelligent Robots and Systems (IROS), 5026â&#x20AC;&#x201C; 5033, 2012. 2. Andrychowicz et al. Advances in Neural Information Processing Systems, 5048â&#x20AC;&#x201C;5058, 2017. 3. Dhariwal et al. GitHub, https://github.com/openai/baselines, 2017. 4. Bellman et al. Dynamic programming. 2013. ACKNOWLEDGEMENTS A huge thank you to Dr. Ren for assisting me in developing this project. I would also like to thank Dr. Xiao and Mobarakol for their expertise and patience with me as I worked through this project. This project was funded by the Swanson School of Engineering Summer Research Internship and in part through the National University of Singapore.

Figure 2. Results of the Hindsight Experience Replay Algorithm. These lines show the data observed over all 50 training and testing epochs. The red trend shows that the test success rate went from 40% to 100% in 6 epochs of training.

Development of a High Throughput Bioreactor Compression Chamber for Rodent Mechanobiology Nicholas Gabriel, Rahul Ramanathan, Kevin Bell, Nam Vo, Gwendolyn Sowa Ferguson Laboratory for Orthopaedic and Spine Research, Department of Bioengineering University of Pittsburgh, PA, USA Email: nag64@pitt.edu INTRODUCTION Low back pain (LBP) is a common ailment that more than 80% of Americans will experience some form during their lifetime, and it is also the single leading cause of disability worldwide [1]. Intervertebral disc (IVD) degeneration contributes to this, partly due to the different mechanical loads experienced in the spine. A better understanding of this phenomenon can be gained through an approach using mechanobiology. To achieve this, lower back disc degeneration must be simulated in an ex vivo model to observe the cellular responses to load burden through the application of mechanobiology on rat functional spinal unit (FSU). Our previous mechanobiology work has demonstrated that IVDs demonstrate different responses to mechanical loading, with lower magnitudes and durations demonstrating beneficial effects, and higher magnitudes and durations impairing matrix homeostasis [2]. Therefore, the objective of this study is to design and develop a high throughput mechanobiology compression chamber, which can sustain physiological conditions, to place mechanical loads on bioactive rat FSU. METHODS Design Constraints: The bioreactor system must maintain physiological conditions of 37℃ and 5% CO2/O2. The components of the system need to withstand the conditions of 37℃ and 5% CO2/O2 as well as have a method of sterilization. The motor and loadcell chosen must be sensitive enough to apply loads to a rat FSU (dimensions best approximated at 4.5mm x 2.5mm x 6mm). A method of supporting the FSU while in the chamber must also be established. The system must be able to evaluate (at minimum) four treatments and four control samples per experiment. The container of the device was designed on SolidWorks 2017. The different loading profiles for the device are coded

through MATLAB (i.e. ramp and hold, cyclic, sinusoidal). Mechanical Validation: To assess the accuracy and reliability of the mechanical system, a ramp and hold profile was performed to a target voltage of 1.144V (20N). The sample FSU was used for this test strictly for mechanical purposes. Cell Viability: We have begun practicing cell viability assays in preparation for future tests to ensure proper sterile techniques as well as improving our ability to keep the FSUs alive in 12 well plates. The FSUs were cultured in F12 media in a Heracell 150i incubator. The viability assay that we chose to use were the MTT assay which assesses cell metabolic activity, and the DAPI assay which is a fluorescent stain for DNA. A total of 6 samples will be tested (2 each day) on day 1, day 3, and day 5 after being dissected from the rat. RESULTS To maintain physiological conditions the device was configured to fit into a Heracell 150i incubator. The motor and load cell chosen for this system are the PI M-229.26s and the Sentran PC3-50-000 respectively. The step resolution of the motor is 1 µm, and the loading accuracy of the load cell is ≤ 0.15% FSO and ≤ 0.05% of the load for nonlinearity and non-repeatability respectively. These values for resolution and accuracy are acceptable for this device’s purposes since the rat FSUs will undergo small loads ranging from 0 N to 100 N, which are based off previous literature values [3]. The components can also be sterilized properly to avoid contamination. The controller that is being used in the setup is the Galil DMC 4183, which is an 8-axis controller. The final dimensions of the container (127mm x 158.75mm x 101.6mm) fit comfortably inside the Heracell incubator and has ports for up to 4 motors (Figure 1). The inner dimensions of the chamber are based off the

dimensions of standard 12 well plates, which will be used to culture the FSUs during incubation periods.

calibration curve. The mean and standard deviation of the voltage during the hold were -1.143V Âą0.005 (Figure 3). Cell Viability: Issues arose during the analysis portion of the viability tests, due to limitations of the lab equipment, so results for these tests have been postponed.

Figure 1: FSU bioreactor setup including load cell, step motor, and aluminum container.

The material chosen for the container is aluminum, which can be sterilized using an autoclave. To support the FSU in a 12 well plate, we developed a technique to pot the FSU into 2 mL centrifuge tube lids using a 2-part orthodontic resin epoxy (Figure 2).

Figure 2: Potted FSU in 2 mL centrifuge lid using 2-part epoxy resin.

Mechanical Validation: A calibration curve was generated for the load cell and it was found that a change of roughly .2 V is equivalent to 1 N. Since most tests will use increments of 1 N and the loading accuracy of the load cell is â&#x2030;¤ 0.15% FSO and â&#x2030;¤ 0.05% of the load for non-linearity and nonrepeatability respectively, this further justifies the implementation of the chosen load cell. When testing for mechanical validation, a ramp and hold loading profile was used. The MATLAB coded included a creep approach to the target value and corrected for over and undershoot during the duration of the trial. The forces were recorded in volts, which could later be converted into newtons or pounds later by using the previously generated

Figure 3: Mechanical validation using a ramp and hold load profile. The y-axis displays the voltage output recorded by the load cell during the trial. The x-axis displays the number of counts (in seconds) from the beginning to the end of the trial.

CONCLUSION All the design constraints for this project were successfully met and accuracy and reliability of the mechanical system was established. We are currently working to resolve our technical issues in the cell viability process. Future work is necessary to validate that the system can maintain cell viability with and without application of mechanical loading. Upon validation, this high throughput mechanobiology compression chamber will contribute to the understanding of how aged discs respond to varying mechanical loads, which can provide the necessary data for improved treatments and techniques for lower back pain and disc degeneration. REFERENCES 1. Hoy, D., et al., Arthritis Rheum, 2012. 64(6): p. 2028-37. 2. Sowa et al., J Omarthop Res. 2011; 29(8):1275-83. 3. Brouwers, J. E. M., et al., U.S. National Library of Medicine, Aug 2009.

+Development

of the Handle Grip of a Walker for Hemiplegic Post-Stroke Patients

Tyler J. Bray 1, Ben Emergi, BPT 2, Orit Braun Benyamin, PhD.2 1 University of Pittsburgh Department of Bioengineering, Pittsburgh, PA, USA 2 Ort Braude College Department of Mechanical Engineering, Karmiel, Israel Email: tjb100@pitt.edu INTRODUCTION A stroke is one of many health complications which can have a negative impact on an individual’s gait and balance [1]. Hemiparesis is a neurological condition that impacts nearly 80% of the 796,000 stroke survivors in the US every year. Hemiparesis decreases the strength and/or sensation on one side of the body and is dependent on which side of the brain is impacted by the stroke [2, 3]. This lack of sensation, and therefore reliable biological feedback, can cause a hemiplegic post-stroke patient to not push their walker with symmetric force while relearning to walk. This asymmetrically applied force directly impacts gait and stability, ultimately affecting safety and comfort. Recent technological developments have drastically changed the approach to stroke treatment and therapy [4]. Although many existing solutions harness this new technology, they can be expensive to purchase, inaccessible to large populations, and/or require careful, constant monitoring by the physical therapist during therapy sessions [5]. It has been shown that “dynamic visual kinematic feedback from wireless pressure and motion sensors had similar, positive effects as verbal, therapist feedback” [6]. One study used a cane with a pressure sensor and audible beeps as feedback and found that it was “beneficial and effective in improving…muscle activation, and gait in stroke patients” [7]. These results, paired with inquiries from local physiotherapists, have driven the design of this device. METHODS Our team developed a system which compares the force applied to the walker handles by each hand and delivers visual and auditory feedback to the user. The goal of this system, which can be observed in Figure 1, is to improve the gait of hemiplegic post-stroke patients while relieving some of the burden of observation from physiotherapists during rehabilitation sessions. Although stroke patients suffering from hemiplegia see a decrease in grip strength in both hands, the decrease in finger

extensor and wrist strength are markedly more significant in the hand of the affected side [8, 9]. This is why the system has one handle with the grip of a traditional walker handle (for the "unaffected" hand), and an open-hand design which allows the affected hand to be strapped in place. This design concentrates the pushing force at the distal end of the arm, the base of the palm, instead of at the weakened fingers. Both handles and the electronics box are affixed to the walker handles by adjustable clamps. These clamps allow the system to be transferred to another walker or to change the orientation of the handles, which are individually compatible with both hands. A strip of LEDs mounted on the walker helps the patient initially understand the system. Red and blue lights on the left and right of the strip indicate when unequal forces are applied to the left versus right handles, and a green light in the middle of the strip turns on when identical forces are being applied. To allow patients to look ahead instead of down while walking, the LEDs can be covered. This forces the patient to rely on the auditory feedback, which is given by beeping tones emitted from a speaker. Medium, slow tones indicate matching forces by the handles, and higher or lower (depending on the side), fast tones let the patient know they are applying asymmetric forces to the handles.

Figure 1: Model of modified walker handle system DATA PROCESSING Data regarding the effectiveness of this device will be gathered by physical and occupational therapists at Galilee Medical Center. The first of three tests to

be conducted is the Fugl-Meyer Assessment (FMA). The FMA analyzes upper and lower extremity capabilities by determining levels of performance in hemiplegic post-stroke patients in areas concerning motor function, balance, sensation and joint function [10]. Secondly, the Timed Up and Go (TUG) test looks at the time it takes a patient to stand up from a chair, walk a measured distance, and return to the chair. A trial comparing TUG results of patients using the modified walker versus the traditional walker with verbal feedback from the therapist can help analyze the device’s efficacy. Before each trial using the modified walker, the patients will be given a training on how to use the device and learn what actions cause specific lights and sounds to be emitted. Thirdly, a Six Minute Walk Test (6MWT) evaluates how far a patient can walk during a sixminute session. Testing this twice per therapy session, once with and without the modified walker, and rotating the order of use, can be a way to determine efficacy of the device. The patients will be given questionnaires with subjective topics related to their comfort and satisfaction with the device, as well as their confidence in walking ability. RESULTS Once initial-stage prototyping and testing was completed, the device was brought to the physical therapy unit at Galilee Medical Center in Nahariya, Israel. After being demoed to staff, several of the physiotherapists and even a hemiplegic post-stroke patient utilized the device. Initial reactions to the system's feedback and ease of use were very positive. Requests for improvement included amplifying the auditory feedback, having the option to hide the visual feedback, and including straps to hold the affected hand in place. These design alterations are currently underway. Ultimately, project success will be assessed based on the patient’s gait using the traditional walker versus the modified walker and the results of subjective questionnaires completed by the patients. DISCUSSION Thousands of patients every year must undergo physical therapy to relearn to walk after surviving a stroke. Currently, hemiplegic stroke survivors rely on therapists to watch their movements to identify issues with gait symmetry. The goal of this device is to partially remove this burden of observation from the therapist by allowing the user to make corrections themselves. The low production cost and simplicity

of this device opens up the possibility of being used during at-home rehabilitation exercises and not just in the in-patient setting [11]. REFERENCES [1] G. Chamorro-Moriana, et al. "Technology-Based Feedback and Its Efficacy in Improving Gait Parameters in Patients with Abnormal Gait: A Systematic Review". Sensors. (2017, October 11). [2] National Stroke Association. “Post-Stroke Conditions: Hemiparesis”. (2018, June 18). [3] The Internet Stroke Center. “Stroke Statistics”. (2018, June 21). [4] C. Zerna, et al. "Evolving Treatments for Acute Ischemic Stroke". Thrombosis CompendiumAmerican Heart Association Journals. (2016). [5] G. Morone, et al. "Robot- assisted gait training for stroke patients: current state of the art and perspectives of robotics". Neuropsychiatric Disease and Treatment. (2017). [6] N. Byl, et al. "Clinical impact of gait training enhanced with visual kinematic biofeedback: Patients with Parkinsons disease and patients stable post stroke." Neuropsychologia. (2015, December). [7] K. Jung, et al. "Effects of gait training with a cane and an augmented pressure sensor for enhancement of weight bearing over the affected lower limb in patients with stroke: a randomized controlled pilot study". Clinical Rehabilitation. (2015, February). [8] S. Park, et al. "Grip strength in post-stroke hemiplegia". Journal of Physical therapy Science. (2016, February). [9] J. Xu, et al. "Motor Control of the Hand Before and After Stroke". Clinical Systems Neuroscience. (2015). [10] L. Zeltzer, MSc OT. "Fugl-Meyer Assessment of Sensorimotor Recovery after Stroke (FMA)". Heart & Stroke Foundation. (2010, July 11). [11] S.I. Lee, et al. "Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training." IEEE Journal of Translational Engineering in Health and Medicine. (2018, May 2). ACKNOWLEDGEMENTS We would like to thank the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh for providing the funding for this project. Additionally, we would like to thank the physical therapy unit at the Galilee Medical Center for their cooperation and expertise.

BIOMECHANICAL REHABILITATION: ACCESSIBILITY OF MUSICAL SYSTEMSDRAWING MUSIC Natasha A. Gilbert1, Navit Roth2, Orit Braun Benyamin, PhD. 2 University of Pittsburgh Department of Bioengineering, Pittsburgh, PA 2 Ort Braude College Department of Mechanical Engineering, Karmiel, Israel Email: nag65@pitt.edu 1

INTRODUCTION A glass vase falls and hits the ground- shattering the glass on impact. In the moment the glass hits the ground, our brain receives both auditory and visual signals from the single source. The correlation of the auditory and visual signals leads us to understand the situation more clearly because, without both signals, we could misinterpret the situation [1]. It is these combined sensory inputs that children learn from most during the first two stages of development [2, 3]. Therapists hope to expedite rehabilitation processes for people with motoric disabilities by mimicking the most effective way children learn- using multiple sensory inputs. Although a lack of development or use of motor skills stems from a broad spectrum of disabilities, music and art therapy have successfully played a role in the rehabilitation setting [4].

personalized calibration phase. The system accomplishes this by identifying the userâ&#x20AC;&#x2122;s range of motion, maximum pen tip pressure and maximum pen speed. Since these values are unique to each patient, the calibration will scale the canvas screen size and scale all pen movements controlling the music played.

There is currently a lack of therapeutic devices which combine music and drawing for multiple sensory stimulation. In the Biomechanics Lab of Ort Braude College Accessibility of Musical Systems is an ongoing project to create musical devices for a range of disabilities. Drawing Music is a proposed device for the combination of visual and auditory therapy.

Most importantly, on any setting the system records data for the therapist and user to help track progress of, for example, the userâ&#x20AC;&#x2122;s range of motion.

METHODS Through the relation of visual and auditory therapy, the proposed device will use visual representations of music, or in other words, will allow patients to draw music. The program uses specific drawing movements as inputs in order to output specific chords or notes which are produced while using any one of three stages of the device. The program allows for a user with any range of upper-limb mobility to use the deviceâ&#x20AC;&#x2122;s smart pen and associated tablet. In the current stages of the project, Matlab has been used to design a

The device will then offer the user three different levels to choose from. The first level of the program has preloaded music which is controlled as the user moves the pen in specific motions. The second level of the device produces a picture for the user to trace and then plays chords which correspond to the lines the patient traces. The third level gives complete autotomy to the user where the music is controlled by the user through each specific pen input (i.e. applied pressure, stroke length).

DATA PROCESSING The goal of Drawing Music is to process pen input from the patient into data that is easily understood by the therapist. Currently the data input which can be read from the pen incudes: pressure, tilt of pen, absolute location, relative location, and speed. Through calculation and patient trials, the proposed device will correlate the above parameters into dexterity, grip strength and range of motion improvement. Furthermore, the proposed device will be able to predict and target needed movements to further improve therapy. RESULTS As the device is in the early stages of development, it will need to be tested on targeted user groups including patients with both fine and gross motoric disabilities. Project success will be determined

based on ease-of-use scores and compliance of both patients and therapists. Furthermore, success of the project can be measured through an increase in fine and gross motor ability of a patient compared to other similar devices in the field. DISCUSSION Lack of motor skill development or use of motor skills stems from a broad spectrum of disabilities. By evaluating the necessary components of sensory learning and rehabilitation for each specific disability, the project aims to engineer a device that can cater to the specific needs of each patient. REFERENCES [1] Mossbridge, J., Grabowecky, M. and Suzuki, S. Seeing the Song: Left Auditory Structures May Track Auditory-Visual Dynamic Alignment. (2013). PLoS ONE, 8(10), p.e77201. [2] Cratty, B. Perceptual & Motor Development in Infants & Children (3rd Edition). (1987). Canadian Journal of Occupational Therapy, 54(2), pp.88-89. [3] Davis, T., Columna, L., Abdo, A. and Russo, N. SENSORY MOTOR ACTIVITIES TRAINING FOR FAMILIES OF CHILDREN WITH AUTISM SPECTRUM DISORDERS. (2017). Palaestra, 31(4), pp.35 - 40. [4] Ninds.nih.gov. (2017). Motor Neuron Diseases Fact Sheet | National Institute of Neurological Disorders and Stroke. ACKNOWLEDGEMENTS We would like to thank the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh for providing funding for this project. Additionally, we would like to thank Romi Marienberg, B.O.T, for her advice and expertise.

ANGIOGENIC RESPONSE TO IL-4 ELUTING COATINGS IN MESH TISSUE EXPLANTS Hannah C. Geisler, Alexis L. Nolfi, Aimon I. Iftikhar, and Bryan N. Brown McGowan Institute for Regenerative Medicine, Department of Bioengineering University of Pittsburgh, PA, USA Email: hcg14@pitt.edu, Web: http://www.mirm.pitt.edu/ INTRODUCTION Pelvic organ prolapse is characterized as the descent of the pelvic organs due to diminishing support. Susceptibility to developing prolapse increases as a woman ages, as well as with other associated factors such as weight, prior hysterectomy, and vaginal birthing. Surgical repair to compromised tissue is possible, but recurrence rates of the condition are high, sometimes even requiring reoperation. While the use of native tissue for restoration of support is ideal, native tissue lacks the mechanical durability present in synthetic implants. Polypropylene mesh (PPM) implants remain the most promising approach for sustainable repair. However, studies have shown that the PPM implant can behave like an internal chronic wound with longterm inflammation, particularly associated with proinflammatory macrophage populations. Prior investigations in our laboratory have addressed this controversy via implementation of a cytokinedelivery system used to induce an early shift in macrophage polarization at the host-implant interface, enhancing implant integration downstream [1]. As macrophage-implant interactions have been identified as a determining factor of downstream outcome, early shifts to anti-inflammatory phenotypes serve to mitigate acute host-immune response. The present study serves to quantify vasculature, an important trademark of healing tissue and implant integration, present at the host-implant interface via immunolabeling of the CD31 marker for endothelial cells to investigate effects of the cytokine-delivery system on angiogenesis. As previous studies have confirmed the role of anti-inflammatory macrophages in angiogenesis, particularly as an essential aid in stabilizing fresh vasculature otherwise prone to regression [2], expected results include increased vasculature present in IL-4 eluting groups.

METHODS Young C57Bl/6J female mice (8-10 weeks) were used for subcutaneous abdominal implantation of one of three types of polypropylene mesh (n=3-5): unmodified “pristine” PPM, polymer-coated PPM, and IL-4 releasing PPM. Gynemesh® PS (Ethicon, Somerville, NJ) was used as the prototypical mesh device. PPM implants and surrounding tissue complex were harvested at 7 and 14-day time points and fixed for 72 hrs. in neutral buffered formalin. Fixed tissues were paraffin embedded and crosssections were used for histological studies. Paraffin embedded tissue sections were deparaffinized and hydrated in a series of xylene/alcohol/water baths, and then incubated in proteinase K (1x) for 5 min at RT for antigen retrieval. After 3 washes in phosphate buffered saline (PBS), samples were immersed in citric acid buffer (pH=6) at 95°C for 30 min to reverse molecular cross-linkage. Slides were cooled and washed again in PBS before incubation in 3% hydrogen peroxide in methanol for 30 min. Samples were washed twice in TBST (25 mM Tris buffer and 0.1% tween-20). Following the addition of pap pen borders, a 5% donkey serum + 2% BSA + 0.1% triton + 0.1% tween-20 solution was used as a blocking agent for 2 hours at RT. To immunolabel endothelial cells, CD31 primary antibodies (1:150) were utilized (overnight at 4°C), followed by CD31-antirabbit (1:200) secondary antibodies for 30 min at RT in blocking solution. Samples underwent a round of PBS washes before incubation in Vectastain ABC reagent (30 min at RT) and 4% Diaminobenzene-Peroxidase (DAB) Substrate Solution (20-30s). Following the DAB solution, slides were immersed in a water bath and counterstained with Hematoxylin QS (45s). Slides were then reverse-deparaffinized in a series of xylene/alcohol/water baths, mounted, and cover slipped.

DATA PROCESSING Mesh fibers were centered within each 20x field (Figure 1) and processed using a colour deconvolution macro on image analysis software, ImageJ. Total area was calculated and adjusted for area taken up by mesh fiber and areas of pre-existing tissue. Blood vessels were identified by positive CD31 staining. Two trained investigators blinded to study groups counted the number of blood vessels present within each 20x field. Results of the two individual counts were averaged and normalized to area. Significance was calculated using two-way ANOVA with pairwise post-hoc comparisons and Bonferroni adjusted alpha level with p< 0.01.

Figure 1. PPM mesh fiber imaged on Brightfield Nikon Eclipse E600 at 20x. Asterisks indicate examples of

Figure 2. Number of blood vessels present at host-implant

interface at 7 days and 14 days post-implantation. Unmodified pristine PPM displayed the fewest vessels at 14 days while cytokine-delivering PPM displayed the highest vessel count at 14 days.

DISCUSSION Modified cytokine-releasing PPM implants increased vasculature present at the host-implant interface after 14 days, suggesting enhanced angiogenic activity. As the promotion of blood vessel formation correlates to the delivery of vital oxygen and nutrients to tissue and removal of detrimental wastes, findings of the present study further reinstate that IL-4 eluting PPM implants display enhanced implant integration into the host immune response.

positively stained blood vessels. Not all vessels marked.

RESULTS Results are represented below as the number of blood vessels present at the host-implant interface normalized to area. No significant differences were detected in vasculature present at the host-implant interface between the three treatment groups at 7 days (Figure 2). However, all three treatment groups did present significant differences in vasculature present at the 14-day time point. The unmodified pristine PPM contained the fewest blood vessels while the IL-4 eluting PPM contained the highest number of vessels. Additionally, there was a significant increase in vasculature present at the hostimplant interface in the IL-4 eluting mesh between the 7-day and 14-day time points.

As previously stated, cytokine elution shifts early polarization of macrophage phenotype toward an anti-inflammatory phenotype. Results suggest that this polarization provides some sort of influence over the later acute vasculature, possibly due to macrophage-induced recruitment of pericytes or enhanced stabilization of vasculature prone to regression via recruitment of mesenchymal stem cells. These results display strong evidence for the implementation of similar cytokine-releasing systems in other applications of soft-tissue reconstruction and overall improved integration of non-degradable biomaterials. REFERENCES 1.Hachim et. al. Biomaterials 112, 95-107, 2017. 2.Spiller et. al. Biomaterials 35, 4477-4488, 2014. ACKNOWLEDGEMENTS Special thanks to Daniel Hachim, Clint Skillen. Funding provided by University of Pittsburghâ&#x20AC;&#x2122;s Swanson School of Engineering.

A Peripheral Nerve Extracellular Matrix Hydrogel Enhances Return To Function After Sciatic Nerve Gap and Crush Injury 1 1,2 Martin T , Prest TA , Marchal L2, Skillen CD2, Meder T2, Brown BN1,2,3 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA; 2. McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA; 3. Department of Biomedical Engineering, Cornell University, Ithaca, NY. Email: tjm110@pitt.edu INTRODUCTION Injuries to the peripheral nervous system (PNS) have drastic implications to an individual’s well-being, and to society. There are over 600,000 surgical procedures performed in response to such injuries in the United States alone [1]. As a direct result of significantly impacting patients’ quality of life, these injuries create an economic burden on the US due to healthcare costs and limitations on the patients ability to remain a member of the workforce [2.]. Because of the serious impacts that PNS damage has on society, there arises an immediate desire to bring about a solution to this issue. While current surgical practices have advanced over the last several decades, recovery after peripheral nerve injury is still limited. An off

the shelf solution that would speed up and advance recovery could have potential to improve patient’s lives. One potential solution is the use of a peripheral nerve extracellular matrix (PNM). PNM, derived through decellularization, contains a host of nervespecific proteins that play a beneficial role on regeneration. Because the ultimate goal is the return to normal function, experiments prioritize the assessment of downstream functional metrics by looking at the health and function of downstream muscle. We tested the effectiveness of a PNM hydrogel in recovery following nerve injury by examining the atrophy values and kinematic performance. METHODS A rat model of sciatic nerve gap injury was examined by producing a grade five injury on the Sunderland scale through sharp resection of 8mm of the nerve. A silicone conduit was placed into the nerve to bridge the nerve gap

which was either left empty as a negative control or injected with PMN hydrogels of concentrations, 10, 20 and 40 mg/ml. Additionally, the area of the nerve gap was injected with an autograft as a positive control. Eight animals per group were sacrificed after 180 days at which time the nerve and gastrocnemius were excised. Muscle tissue was fixed, paraffin embedded, sliced and placed onto slides, and the slides were stained with Hematoxylin and Eosin (H&E). The slides were imaged using a microscope/NES software, and the muscle fiber diameters measured with ImageJ software. The average muscle fiber diameters for injured and uninjured legs were then compared. Atrophy was calculated by dividing the value of the injured leg by that of the uninjured leg. For kinematic function, animals were evaluated at weeks 0, 1, 2, 3, 4, and every even week following. Three videos were captured per timepoint. Simi Motion movement analysis software was used to place tracking dots at various joints of the body and at key phases of gait. Ankle angle was captured at toe-off to assess the function of the gastrocnemius muscle. Standard metrics for Sciatic Functional Index (SFI) were captured at mid-stance phase. Each measurement was captured 3 times per video. Functional assessment was perform using MotoRater. Animals were evaluated at weeks 0, 1, 2, 3, 4, and every even week following. Three videos were captured per timepoint. Simi Motion movement analysis software was used to place tracking dots at various joints of the body and at key phases of gait. Ankle angle was captured at toe-off to assess the function of the gastrocnemius muscle. Standard metrics for Sciatic Functional Index (SFI) was captured at mid-stance phase. Each measurement was captured 3 times per video.

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RESULTS Upon completing the kinematic analysis, the averages of the three measurements per video for ankle height, ankle angle and SFI were recorded. For SFI, our controls matched expectations of this metric, with conduit alone (LGCOS) remaining low through the 180 days and autograft (LGA) begins to recover some function (Figure 1). Results of 24 weeks for an injection of 10 mg/ml (LGNG10) show little difference when compared with LGCOS. Early results appear to show a dose dependent effect of the PNM hydrogel and suggest that PNM may have similar recovery to autograft at 20 and 40 mg/ml (LGNG20 and LGNG40, respectively). For ankle height and ankle angle, there appears to be little to no significant difference between the positive and negative controls. Upon compiling atrophy values, nearly half of the rat muscles are shown to have an atrophy value above 100%, four of which are above 110%. Only the two negative control rats had atrophy values below the 80% threshold. Results will continue to be monitored until they reach 24 weeks.

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Figure 2. Column graph of atrophy values, organized by group

DISCUSSION LGNG10 does not appear to be significantly different from the negative control; while these results suggest LGNG10 is not useful for recovery, they might be due to a dose dependent effect. The heavier injections, LGNG20 and LGNG40, are tracking closer to the autograft than negative control, suggesting they might perform similarly to the autograft. The results from the ankle height and ankle angle do not appear to be conclusive and they will need to continue to be monitored. While all of the calculated atrophy values are above that of the negative controls, they will need to be revisited due to how unexpectedly high their values were. Future studies will continue to develop and evaluate the effectiveness of PNM hydrogel in various peripheral nerve injury models.

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REFERENCES [1] Noble J, et al., “Analysis of Upper and Lower Extremity Peripheral Nerve Injuries in a Population of Patients with Multiple Injuries” 1998, Journal of Trauma, Vol 45, 116-122 [2] Drake, Richard L,: Vogl Wayne; Tibbitts, Adam W.M. Mitchell (2005). Gray’s anatomy for students. Philadephia:Elsevier/Churchill Livingstone.

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Figure 1. (Top) Plot of sciatic functional index (SFI) values. SFI is a well cited metric for measuring function after sciatic nerve injury. (Middle) Plot of ankle angle. (Bottom) Plot of ankle height.

ACKNOWLEDGEMENTS I would like to thank the University of Pittsburgh Swanson School of Engineering, for funding this internship.

Angiogenic Response to Abdominal and Vaginal Polypropylene Mesh Implants in a Rabbit Model McKenzie Sicke, Aimon Iftikhar, Alexis Nolfi, Hannah Geisler and Bryan Brown, PhD McGowan Institute of Regenerative Medicine University of Pittsburgh, PA, USA Email: mms223@pitt.edu INTRODUCTION Over a million women in the United States each year are affected by pelvic organ prolapse, a condition characterized by the prolonged weakening of the pelvic floor muscles [1]. Reinforcement can be implemented via a surgical reconstruction using polypropylene mesh (PPM), however synthetic mesh can often cause complications such as fibrous tissue encapsulation, erosion, or mesh degradation due to foreign body reaction [2]. The overall host response to the PPM is the sum of several factors, including the polarization of macrophages present, cellularity of the tissue at the wound site, and whether the tissue being remodeled is healthy tissue or fibrous encapsulation. The polarized macrophages that mediate this response can be categorized broadly into subtypes M1 (proinflammatory) or M2 (proremodeling) [3]. Mesh-tissue response can be improved with a limited M2 predominant response, but excess M2 can cause complications such as fibrous encapsulation as well [3]. Modulating the inflammatory response to implanted surgical mesh can improve the long term outcome of reconstructive treatment for those with pelvic organ prolapse. Encouraging the M2 phenotype in macrophages in the implant environment can be achieved with release of interleukin-4 (IL-4), an immunomodulatory cytokine. Current methods in this lab incorporate IL-4 into a nanoscale coating. This is achieved by using an adapted radio frequency glow discharge method to establish a negative charge on the mesh, then adding polycationic and polyanionic polymers to create the stable coating. A key component of tissue remodeling is the angiogenic response in the developing tissue because increased vasculature can provide oxygen and nutrients to the implant site. These factors could

improve the growth of remodeled tissue rather than fibrotic tissue that can potentially cause complications, and identifying trends in angiogenesis could aid the development of better implant methods. PPM is also used in abdominal hernia repair, and the anatomical differences between abdominal tissue and vaginal tissue may lead to differing host responses. The initial focus of this study is to explore the angiogenic response at the host-implant interface of abdominal and vaginal mesh implants in a rabbit model, with the long term goal of incorporating the controlled release of IL-4 into the study. METHODS A New Zealand white rabbit model was used for PPM implants in both a subcutaneous abdominal implant and a vaginal implant that mimics the abdominal sacrocolpopexy procedure, the current gold standard treatment for prolapse repair. At 14 days post-implantation, the rabbits were sacrificed and samples of the tissue-mesh complex were extracted for histological analysis. The tissue was mounted in paraffin and sectioned at 7 Âľm. The slides were immunolabeled with the CD31 marker for endothelial cells using a 4% diaminobenzidine (DAB) substrate solution and a Hematoxylin QS counterstain. The slides were also stained using hematoxylin and eosin (H&E) for qualitative comparison of the tissues. Images for blood vessel analysis were taken at a 20x objective of the hostimplant interface centered on a mesh fiber. DATA PROCESSING The blood vessels in the region of interest were hand counted by a trained investigator with the aid of an ImageJ colour deconvolution macro to identify vessels. A Studentâ&#x20AC;&#x2122;s independent-samples ttest was used to compare the average vessel density between vaginal and abdominal tissue.

RESULTS Figure 1 demonstrates positive immunolabeling of the endothelial cells that line blood vessels, identifying them for analysis. Additionally, the purple nuclei stained in the H&E images such as Figure 2 provide information on the infiltration of cells during the immune response. Represented in Figure 3 are the average blood vessel count normalized to area with standard deviation. It was found that there is no significant difference between the angiogenic response in abdominal and vaginal mesh implants at 14 days (p > .05). Figure 1: A representative image is shown for the immunolabeling of the CD31 marker for endothelial cells to identify blood vessels in a 20x field of the host-implant interface in a vaginal tissue sample (scale bar=100Îźm).

Figure 2: An H&E stain of the host-implant interface of the same vaginal tissue sample as Figure 1 is shown at a 20x objective. These images were used for qualitative observation of the differences in cell infiltration (scale bar=100Îźm).

Figure 3: The number of blood vessels normalized to the area of the region of interest at the host-implant interface is plotted for both vaginal and abdominal implants with standard deviation. There is no significant difference found.

DISCUSSION These results may suggest that the early host response is similar at the two sites considered in the rabbit model. This may also suggest that the macrophage polarity ratio is similar at the interface of abdominal and vaginal implants, however it is not certain that angiogenesis is directly affected by this. Going forward we will be analyzing multiple time points under different mesh conditions including a group coated with the nanoscale coating, and a group with the coating and loaded IL4. This will give a better picture of the relationship between macrophages and angiogenesis during the early immune response. REFERENCES 1. Iftikhar, A., Nolfi, A., Moalli, P., Brown, B., A Clinically Relevant Rabbit Surgical Model of Pelvic Reconstruction to Evaluate the Immune Response to Novel Surgical Materials, 2018. 2. Udpa, N., Iyer, S., Rajoria, R., Breyer, K., Valentine, H., Singh, B., McDonough, S., Brown, B., Bonassar, L., Gao, Y., Effects of Chitosan Coatings on Polypropylene Mesh for Implantation in a Rat Abdominal Wall Model, 2013, vol. 19: no. 23 and 24. 3. Nolfi, A., Brown, B., Liang, R., Palcsey, S., Bonidie, M., Abramowitch, S., Moalli, P., Host response to synthetic mesh in women with mesh complications, AJOG 2016. ACKNOWLEDGEMENTS Studies conducted at the McGowan Institute for Regenerative Medicine under Dr. Bryan Brown. Funding was provided by the Swanson School of Engineering.

SYSTEM TO RECORD THERAPUTIC DELIVERY WINDOW OF STOCHASTIC VIBROTACTILE STIMULATIONS IN INFANTS WITH NEONATAL ABSTINANCE SYNDROME Jacob Meadows, Elisabeth Salisbury, and Mark Gartner Gartner Design Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: jmm367@pitt.edu INTRODUCTION Neonatal Abstinence Syndrome (NAS) is the manifestation of multi-system withdrawal symptoms in newborns resulting from the sudden termination of maternal drugs that the infant has become dependent on in utero. From 2009 to 2012 hospitalization costs associated with NAS rose from $7.3 million to $1.5 billion as part of the nationwide opioid epidemic (Tolia, 2015). Treatment of this growing and costly public health problem is currently based on pharmacological therapies. These pharmacologic therapies cause developmental issues and require extended hospitalizations and long weaning periods. Therefore, a critical unmet need exists for the development of a non-pharmacological treatment for NAS in opioid-exposed newborns. One promising non-pharmacological intervention is the application of gentle stochastic vibrotactile stimulation (SVS). Previous studies show that stochastic tactile sensory stimulation at low levels can enhance stability of function and brain maturation during critical periods of early development (Ardiel, 2010). It has also been recently demonstrated that brief periods of SVS significantly improved cardio-respiratory function in premature infants with immature cardiovascular systems (Bloch-Salisbury et al., 2009). A clinical study is currently being conducted at both the University of Pittsburgh and University of Massachusetts to evaluate SVS in infants with NAS using a mattress designed to deliver specific stochastic vibrations (Bloch-Salisbury et al., 2017). This trial is examining the efficacy of SVS as a novel non-pharmacological intervention complementary to standard clinical care in comparison with standard clinical care alone. As part of this clinical trial, SVS treatment is administered during defined time periods and

requires clinicians to manually record whether the infant is on the SVS mattress and receiving treatment or off the mattress for another reason such as during a bottle feeding. Manual recording introduces inconsistencies into the data based on human error and is cumbersome. Therefore, to both simplify recording and increase the reliability and precision of the clinical trial data, a system was designed to automatically detect when the infant is present on a mattress and receiving treatment. MATERIALS AND METHODS Detection was accomplished using pressuresensitive conductive material paired with electronics to accurately assess changes in pressure from the weight of an infant on top of an SVS treatment mattress. Other design requirements included simple operation, low-cost, and broad clinic-friendliness considerations such as liquid resistance, ease of cleaning, and compactness. The weight-detection mat was constructed using Velostat, a pressure-sensitive conductive material, in combination with copper foil tape that has a conductive adhesive backing.

Figure 1: Velostat sheet pressure sensor diagram. When pressed down, velostat decreases in resistance, allowing more current to flow between the two strips of conductive copper tape.

Two strips of copper tape were placed in parallel with a Velostat sheet in between. This weightdetection mat was connected to a breadboard-based ohmmeter to measure the change in resistance of the Velostat with changes in pressure. The resistance between the parallel strips of copper tape was monitored with an Arduino Uno microcontroller. Tolerance to liquids was achieved with a polypropylene plastic film encasing the Velostat and copper foil tape mat. The open-source Arduino integrated development environment (IDE) was used to create the program that determines the presence or absence of an infant as well as to write this data with timestamps in a user-friendly format. RESULTS The prototype system can detect the presence of the infants exceeding three pounds, which includes all candidates for the clinical trial. In addition, the system is integrated with the Arduino IDE for serial data communication, allowing the presence of an infant to be recorded and timestamped accordingly. This timestamping enables infant location data to be seamlessly integrated into the existing clinical trial data collection and analysis. Further, the system design is thin, lightweight, and waterproof.

DISCUSSION The thin, lightweight, and water resistant design aligns with the clinical environment requirements that include exposure to bodily fluids. This also allows for easy cleaning and sterilization both between uses and between patients. In addition, the materials used in this system are also relatively lowcost (<$50 per device) to facilitate expanded implementation in the ongoing clinical trial. The automated data collection from this system will allow for more accurate recording of therapeutic delivery window as well as improve the reliability of previous, manually-recorded data. CONCLUSIONS The primary objective of this project was to develop a system that could automatically detect and record whether an infant with NAS was receiving SVS treatment as part of an ongoing clinical trial. This objective was achieved using a pressure-sensitive material in combination with electronic components and a microcontroller to detect changes in weight on top of the infant mattress, thereby detecting the presence of the infant. The prototype system design is thin, low-cost, lightweight, and resistant to liquids for easy cleaning. All these design features enable the device to be clinic-friendly and unobtrusive to patient care.

The system was run continuously for eight hours both with no weight and a mock-infant weight on the mattress to test performance. This simulated test run showed no false-signals and timestamped all delivery window changes accurately with ten second temporal resolution. Further, preliminary tests of water resistance were conducted that showed no signs of damage to the electronics that may be exposed to liquids.

Potential future improvements to this system include wireless data transmission, an enhanced clinicianside user interface, and integration into the SVS mattress instead of a sensor package underneath.

Figure 2: System diagram of information flow to record the therapeutic delivery window of stochastic vibrotactile stimulations. An infant on the mattress is detected by a change in sensor resistance, calculations are completed on the Arduino microcontroller, and a timestamped delivery window is exported onto the laptop computer.

ACKNOWLEDGEMENTS I would like to acknowledge the University of Pittsburgh Swanson School of Engineering, the Office of the Provost, the Department of Bioengineering, and Drs. Mark Gartner and Elisabeth Salisbury.

REFERENCES 1. Tolia V, New England Journal of Medicine (2015) May; Vol. 372, no. 22, pp. 2118–2126. 2. Ardiel E, Pediatrics & Child Health., U.S. National Library of Medicine (2010) March; Vol 15(3), 153–156. 3. Bloch-Salisbury E, Journal of Applied Physiology (2009) July Vol. 107, no. 4, pp. 1017–1027. 4. Bloch-Salisbury E, PLoS ONE (2017) April, 12 (4): e0175981

INFANT FEED THICKENING STUDY AT CHILDREN’S HOSPITAL OF PITTSBURGH Kelsey Toplak and Mark Gartner Gartner Design Lab, Department of Bioengineering University of Pittsburgh, PA, USA Email: kat103@pitt.edu INTRODUCTION Children’s Hospital of Pittsburgh currently uses oatmeal flakes to thicken formula for dysphagic infants age zero to twelve months to reduce risk of aspiration and subsequent oxygen depletion. Formula is thickened to either a 1:1 or 1:2 oatmealto-formula ratio based on results from a barium swallow test. This work both quantified the baseline viscosity of several thickened formulas and assessed changes in viscosity over time with the goal to determining the optimal viscosity and feeding period length to reduce risks of aspiration. Secondary goals of this work included investigating a potential difference in viscosity in the thickened formula used in treatment and the thickened diluted Varibar used in diagnosis. As a significant discrepancy between these two viscosities could pose a threat to patient safety in the form of a higher risk of aspiration due to the feeding of a thinner formula than was diagnosed. METHODS A Brookfield DV2T viscometer (Brookfield Engineering, Middleboro, MA) was used to measure the viscosity of Similak formula (Abbott Nutrition, Chicago, IL) thickened with Gerber Ironinfused Oatmeal flakes (Nestle Infant Nutrition, Florham Park, NJ) over a one-hour test period. The viscosity of both a 1:1 and 1:2 oatmeal-to-formula ratios were measured in a series of three one-hour trials each. Similak formula was prepared according to the manufacturer’s instruction and was thickened with one tablespoon of oatmeal per one (1:1) or two (1:2) ounces of formula. Each trial was conducted for 60 minutes, the viscosity was taken every five minutes during this time at 100 RPM. Viscosity measurements were taken every five minutes for 60 minutes at 100 RPM. Viscometer spindle size was determined by a preliminary viscosity test in which different spindles were used measure viscosity from 0 to 200 RPM in increments of 10 RPM. The 1:1 testing utilized a thinner diameter LV-3 (63) spindle

and the 1:2 testing was conducted using a thicker diameter LV-1 (61) spindle. Varibar (Bracco Imaging, Milan, Italy), the fluid used to diagnose and treat dysphagic infants by allowing visualization of swallowing activity during a Barium Swallow test, was prepared according to the manufacturer’s instructions and then diluted with tap water such that the mixture maintained a 2/3 Varibar 1/3 water ratio in accordance with CHP protocol. The viscosity of 5 mL of diluted Varibar was taken every 5 minutes at 200 RPM with a small sample adapter spindle (SPC-27). Viscosity was measured for a total of 60 minutes. Diluted Varibar was then thickened in the same fashion as the Similak infant formula. Varibar was first thickened in a 1:2 oatmeal-to-diluted Varibar ratio. Viscosity of 1:2 thickened diluted Varibar was measured every 5 minutes at 50 RPM with the LV-3 (63) spindle for a total time of one hour. A 1:1 oatmeal-to-diluted Varibar mixture was created in the same fashion. The viscosity of the 1:1 ratio was measured every 5 minutes at 150 RPM with the LV4 (64) spindle. The total test time of the 1:1 ratio was 20 minutes due to both time and Varibar volume constraints as Varibar has a 72-hour shelf life once made. RESULTS The 1:1 thickened formula increased from an initial viscosity of 358.4 cP to 405.2 cP over 60 minutes (Figure 1). The 1:2 thickened formula decreased from an initial viscosity of 24.8 cp to 10.6 cP over the same time period (Figure 2). A steady asymptotic viscosity of approximately 10 cP was reached for the 1:2 ratio at approximately 40 minutes after mixing. The maximum percent change in viscosity in the 1:1 mixture was 15.3% where the maximum percent change in the 1:2 mixture was 59.3%. These results suggest that the thicker

formula oatmeal mixture has more consistent viscosity and behavior over the test period. The diluted Varibar had an initial viscosity of 26.72 cP and a final viscosity of 27.66 cP. As such, the diluted Varibar maintained a steady state viscosity over the one-hour test period. This data serves as a reference point across all other tests.

The viscosity of the 1:2 oatmeal-to-diluted Varibar decreased from 344.8 cP to 171.6 cP over one hour. A steady asymptotic viscosity of approximately 170 cP was reached for the 1:2 ratio at approximately minutes after mixing. The viscosity of the 1:1

viscosity of 1:2 oatmeal-to-formula over 60 minutes was 16.7 cP. A 330% difference in viscosity between the minimum oatmeal-to-Varibar and maximum oatmeal-to-formula was found. The average viscosity of 1:1 oatmeal-to-diluted Varibar over 20 minutes was 896.8 cP. The average viscosity of 1:1 oatmeal-to-formula over 60 minutes was 380.4 cP. DISCUSSION The primary goal of this study was to quantify two oatmeal flake thickened formula preparations used to feed dysphagic infants under 12 months at Childrenâ&#x20AC;&#x2122;s Hospital Pittsburgh. The 1:1 ratio demonstrated a smaller percent change in viscosity after mixing suggesting more flexible feeding time periods may be possible. In contrast, the 1:2 formula-to-oatmeal mixture demonstrated shear thinning reaching an asymptotic viscosity after 40 minutes suggesting that there may be a finite time in which feeding should occur after mixing. In the future, characterization of thickened formula can may be extended to barium-based swallow test formulas to improve the consistency between testing conditions and clinical feeding treatments. The secondary goal of this study was to assess a potentially significant difference between the thickened formula used to treat infants and the thickened Varibar used to diagnose them. The 1:2 thickened Varibar was on average 11.5 times more viscous than the 1:2 formula, where the 1:1 thickened Varibar was on average 2.4 times more viscous than the 1:1 thickened formula. While more Varibar trials would help further support these results, it is apparent that there exists a notable difference in viscosity between thickened Varibar and formula. Because the Varibar mixtures are more viscous than the thickened formula being fed to the patient there is an increased chance of aspiration with the thinner fluid (formula).

oatmeal-to-diluted Varibar decreased from 968 cP to 860 cP over 20 minutes. Due to the decreased time of this trial, it cannot be concluded if a steady state viscosity was achieved. The average viscosity of 1:2 oatmeal-to-diluted Varibar over 60 minutes was 192.5 cP. The average

ACKNOWLEDGEMENTS This research was conducted in conjunction with Occupational Therapy Department of the Childrenâ&#x20AC;&#x2122;s Hospital of Pittsburgh. Kelsey received funding from the Swanson School of Engineering at the University of Pittsburgh and from Dr. Mark Gartner in the Department of Bioengineering.

A qPCR BASED ASSAY FOR DETECTING MYCOPLASMA CONTAMINATION IN CELL LINES Zachary Fritts1; Sarit Sara Sivan2; Marcela Viviana Karpuj 2 1 Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 2 Department of Biotechnology Engineering, ORT Braude of Engineering, Karmiel, Israel. Email: zsf2@pitt.edu INTRODUCTION: Mycoplasma is a group of bacteria that belongs to the class Mollicutes (meaning soft skin since they lack a cell wall) with a typical diameter of 0.30.8μm, making them the smallest currently known bacteria [1]. They are common contaminants of cell lines, and their small size often allows them to escape visual detection by the presence of turbid media. Mycoplasmas have the ability to alter the physiology of the host cell in many ways and may affect cell proliferation, gene expression profiles, cell metabolism, and rates of apoptosis. They can also cause chromosomal translocations and DNA degradation [2]. Thus, their presence can greatly decrease experimental reproducibility and lead to unreliable results. Despite these high risks, not all scientists test their cell lines for Mycoplasmas, and contamination remains a serious problem. A 2015 investigation of NCBI’s RNA-seq archive found that 11% of rodent and primate samples contained sequences mapping to Mycoplasmas [3]. Among the most sensitive and specific tests for Mycoplasma are those relying on PCR. However, commercial kits are often expensive, and published protocols for Mycoplasma detection are either time consuming or lack sensitivity. Thus, we have attempted to design a PCR-based test for Mycoplasma that is cost-effective, rapid, and appropriately sensitive. METHODS: The developed assay relies on qPCR with meltcurve analysis. SensiFast SYBR® Hi-ROX Kit by Bioline, was used for the qPCR reactions, combined with the primers described by Uphoff and Drexler (Thermo-Fisher) [4]. Our forward and reverse primer mixes each had a 0.4μM working concentration in the reaction.

The reaction mix contains 10μL of the SensiFast reagent, 0.8μL of both the forward and reverse primers, 2μL of prepared sample, and 6.4μL of DNase free water. The samples were prepared by centrifuging 1mL of media from each cell line to be tested at 20,000xg for 10 minutes, carefully pipetting off the supernatant, resuspending the pellet in 25μL of DNase free water, and then heating the mixture to 95ᵒC for 15 minutes. The forward and reverse primers were heated to 72ᵒC for 5 minutes with the DNase free water before cooling them to 4ᵒC and adding them to the reaction mix. Reaction conditions were as follows: an initial heating to 96ᵒC for 2 minutes, followed by a cycling stage of 95ᵒC for 4 seconds, 65ᵒC for 8 seconds, and 72ᵒC for 16 seconds + 1 second for each of the 35 cycles. Following the cycling stage, we added a melt curve stage (machine default settings), a holding stage of 72ᵒC for 5 minutes, and an infinite holding stage of 4ᵒC. To validate our test, we verified the absence of PCR inhibition using a standard curve, tested the sensitivity of the reaction by running the test with a series of dilutions of a template of known concentration, tested a HeLa cell line that had been deliberately infected with Mycoplasma to see how long it takes before sufficient Mycoplasma had accumulated for a positive test result, and cross-validated our results using the commercially available EZ-PCR Mycoplasma Test Kit (provided by Biological Industries). DATA PROCESSING: Ct values from technical wells were averaged and the standard deviation of this average was calculated to make sure it was below 0.3 as per the MIQE guidelines. Samples were recorded as positive or negative for Mycoplasma contamination based on visual comparison of their

melt curves with overlaid melt curves from positive and negative controls. RESULTS:

DISCUSSION: The maximum sensitivity we have reached so far seems to be more than adequate for the purpose of detecting Mycoplasmal DNA in contaminated cell cultures. 2.5x10-7 ng/μL of Mycoplasma DNA in our processed sample corresponds to roughly 940 copies of the Mycoplasma genome from 1mL of media. The fact that clean cultures inoculated with Mycoplasma took 4 days to register positive for Mycoplasma suggests that it is best to incubate newly thawed cell lines for at least that long before testing them for Mycoplasma contamination, if our test is to be applied. Because the test uses of a mix of multiple forward and reverse primers, it can detect a wide range of Mycoplasma species, and if combined with sequencing, the exact species of Mycoplasma that is contaminating a cell line could be determined. We hope to verify in the future that the test works on all of the most common species of Mycoplasma found in the laboratory and to compare its sensitivity to commercially available tests.

Figure 1: typical melt curves from a test. The dark blue and purple curves are NTCs (no template controls), while the curves that show a strong, narrow peak at 84.4ᵒC are positive for Mycoplasma.

Current results indicate that the maximum sensitivity of our test is a Mycoplasma DNA concentration of 2.5x10-7 ng/μL in the processed sample, which is diluted by a factor of 3200 relative to most of the contaminated media we tested (note that this concentration is valid for DNA of length 517bp, and is not meant to be interpreted as a concentration of full length Mycoplasma genomes). The test was able to detect Mycoplasma in a HeLa cell line four days after it had been inoculated with live Mycoplasma. The slope of a standard curve from serial dilutions of a Mycoplasma target was -3.165 (r2 = 0.9917), which puts the efficiency of our PCR reaction at 107%. This is within the accepted range of 90%110%.

The total time needed to complete the test is 2.5 hours, mostly owing to the time the test is in the thermocycler. The qPCR-based test we have utilized is both specific and sensitive enough to be of practical use in the lab at a lower cost than commercially available kits for detecting Mycoplasma contamination. We are continuing to validate the test, and, in the future, sensitivity may be improved if a universal primer pair amplifying a shorter segment of the Mycoplasma 16S ribosomal subunit could be found. AKNOWLEDGEMENTS: We thank Chen Yacobsohn for the technical help and the Swanson School of Engineering for jointly funding the project. REFERENCES: [1] Razin, S. The Prokaryotes. 2006. Page 836. [2] Nikfarjam, L. Cell J. 2012; 13(4). Page 204206. [3] Olarerin-George, A. Nucleic Acids Res. 2015; 43(5). Page 2535-2542. [4] Uphoff, C. Basis Cell Culture Protocols. 2005; 7. Page 371.

INVESTIGATION INTO HINDLIMB MUSCLE NEURAL CIRCUITRY IN THE MOUSE Katherine R. Rohde, Christi L. Kolarcik Systems Neuroscience Center, Department of Bioengineering University of Pittsburgh, PA, USA Email: krr74@pitt.edu INTRODUCTION Motor neurons, the cells responsible for the control of voluntary muscle movement, degenerate and die in amyotrophic lateral sclerosis (ALS). This progressive and fatal condition affects more than 20,000 people in the U.S., with 5,000 new cases diagnosed each year [1]. Although some cases are inherited, the majority of cases (90â&#x20AC;&#x201C;95%) are considered sporadic. The motor neurons located in the spinal cord, brainstem and brain are all affected; however, it is unclear where the disease-initiating event(s) occurs and how the neural circuitry mediates disease spread. Furthermore, it is not known whether the denervation pattern for fasttwitch muscles differs from that of slow-twitch muscles. Several hypotheses regarding the underlying pathophysiology of ALS exist. In some inherited cases of ALS, mutations in the gene encoding Cu/Zn superoxide dismutase (SOD1) have been implicated. Other hypotheses include abnormal RNA metabolism and glutamate excitotoxicity. A growing number of hypotheses suggest that abnormal synaptic communication and changes in synaptic function play a pivotal role in disease progression [2, 3]. Such hypotheses require systems-level investigations in order to assess their validity as a method of treatment for ALS. However, these investigations will first require a working knowledge of the neural network. We are working to catalog how and when synaptic connections are affected as ALS progresses in the G93A SOD1 mouse model. The model is the most widely used and well-characterized model for ALS used in laboratory research; the transgenic mice express the human gene for mutant SOD1 and share many important pathological features with human ALS cases. Before we can begin to catalog innervation changes as a result of ALS in this wellestablished mouse model, we must establish a basis

for comparison via a neural network map for normal mouse. In the G93A SOD1 mouse model, symptoms begin in the hindlimb muscles; thus, the atlas must focus on hindlimb neural circuitry. Additionally, the G93A SOD1 mouse model exhibits preferential loss of fast-twitch muscle fibers [4, 5]. Therefore, we must investigate both fast and slow-twitch muscles to compare the denervation patterns for different muscle fiber types. Cumulatively, such an atlas will allow for future investigations into how and when synaptic connections are affected as ALS progresses and to determine at which locations the network is most vulnerable. METHODS Mapping of neural circuitry was accomplished via injection of rabies virus (RV) into mouse hindlimb muscles. As an established retrograde transneuronal viral tracer, RV can cross the neuromuscular junction to be taken up by the motor neurons responsible for direct innervation. The virus then replicates and continues to move trans-synaptically to neurons directly and then indirectly connected to these motor neurons. Rabies virus is uniquely suited for this tracing given that it does not destroy infected neurons, does not cause symptoms in infected animals even at prolonged survival times, and is capable of retrograde transport across multiple synapses [6]. Both tibialis anterior (fasttwitch) and soleus (slow-twitch) muscles have been investigated in control mice on the C57Bl/6 background. At the designated survival time, the animal was sacrificed via transcardial perfusion, and the brain and spinal cord were removed. After being embedded in a pig gelatin solution and cut at 50 Îźm, the tissue was stained for rabies-positive cellular proteins. The staining protocol entailed incubating the tissue in a hydrogen peroxide solution and then a blocking serum in order to reduce non-specific background

staining. The tissue was then incubated in a primary antibody solution overnight to allow for epitope binding. The next day, the tissue was incubated in the appropriate secondary antibody followed by an Avidin–Biotin Complex (ABC) solution, which amplified the target antigen signal. Lastly, the tissue was treated with chromagen (a 3,3'Diaminobenzidine (DAB) peroxidase solution), staining the rabies positive cellular proteins a dark brown. Brain sections were examined using bright-field microscopy and polarized light illumination. Using a computer-based charting system, the section outlines, rabies-labeled neurons, and gray-white matter boundaries were plotted. Nissl-stained sections were used to plot important neuroanatomical features and mark layer V of the cerebral cortex. DATA PROCESSING Custom laboratory software allowed for the aligning of individual sections. Neuroanatomical features were marked, and an “unfolding” line was drawn through the cortex along layer V. Upon unfolding, rabies-labeled neurons and neuroanatomical markers projected onto the line. Cumulatively, these lines were used to construct labeled cortical maps. These maps display the location of labeled neurons in a two-dimensional representation of the cortex and are being developed for both the tibialis anterior and soleus muscles. Density analysis of rabies infected cells is being performed. RESULTS We identified rabies-positive staining in spinal cord motor neurons, spinal cord interneurons, and layer V cortical neurons as well as neurons located in the brain stem. Staining in these anatomical locations represents three distinct stages of innervation in the mouse. First order labeling – spinal motor neurons – represent the direct innervation pathway to the muscle fiber. Second and third order labeling – including spinal interneurons and layer V cortical neurons, respectively – represent sequentially more complex and multi-synaptic pathways. Increased survival times consistently led to sequential higherorder labeling. Mapping and density analysis of the cortex following injection into tibialis anterior indicates a

high density of rabies-positive cells represented in the A24a, A24b, M1, and M2 cortical areas. These preliminary findings are consistent with previous electrophysiology studies of the mouse. We will compare maps associated with tibialis anterior and soleus to assess differences in innervation and then to assess whether these muscles are affected differently as ALS progresses. DISCUSSION This work is enabling comparison between the innervation of fast-twitch and slow-twitch hindlimb muscles in the mouse. Future studies will develop cortical maps for the tibialis anterior and soleus muscles in normal mice at additional survival times post-rabies injection. Additionally, by evaluating the same survival times in the G93A SOD1 mouse model, we will identify the timing and origin of transport deficits in ALS, aided by a basis for comparison via our established atlas of normal mouse circuitry. These studies will provide a unique opportunity to move studies of ALS beyond the molecular and cellular levels and into a systemslevel approach, representing a fundamental shift in our understanding of how and when the motor system is affected in disease. Furthermore, our completed mouse atlas may serve as a strong foundation for multi-level investigations into numerous other neuromuscular diseases. REFERENCES 1.Arthur et al. Nat Commun 7, 12408, 2016. 2. Vucic et al. Trends Neurosci 37(8), 433-442, 2014. 3. Robberecht et al. Nat Rev Neurosci 14(4), 24864, 2013. 4. Hegedus et al. Neurobiol Dis 28(2), 154-64, 2007. 5. Atkin et al. Neuromuscul Disord 15(5), 377-388, 2005. 6. Kelly et al. J Neurosci Methods 103(1), 63-71, 2000. ACKNOWLEDGEMENTS Funding for this project was provided by the Swanson School of Engineering (KR), the Office of the Provost (KR), and a Career Development Award from the Muscular Dystrophy Association and the American Association of Neuromuscular & Electrodiagnostic Medicine (CK).

REVERSAL OF CHONDROCYTE AGING TO AUGMENT CARTILAGE REGENERATION Sreyas Ravi, He Shen, Xinyu Li, Hang Lin Center of Cellular and Molecular Engineering, Department of Orthopedic Surgery, Bioengineering University of Pittsburgh, PA, USA Email: srr57@pitt.edu, Web: http://ccme.pitt.edu INTRODUCTION In the United States alone, over twenty-seven million people over the age of sixty-five have been diagnosed with osteoarthritis (OA) [1]. In one of the OA treatment methods, surgeons utilize a cell-based treatment known as autologous chondrocyte implantation (ACI), in which a patient’s own chondrocytes from a low weight-impact area are collected, expanded, and re-implanted into the injury cartilage. While there is success in this treatment with younger patients, the treatment has shown to be less effective for older adults above the age of forty-five. There are several possibilities to why this may happen, among which the differences in the reparative capacity between young and old chondrocytes may represent a major reason. However, the underlying mechanism is unknown. The cytoskeleton, a microscopic network of protein filaments and tubules in the cytoplasm, plays a large role in cell morphology as well as the cell functionality. Our previous study showed that cytoskeletal content in chondrocytes increased with age, and there are more rigid and prominent microfilaments in old chondrocytes than the young. Therefore, it was hypothesized that altering cytoskeletal structure and cell morphology would revert aged chondrocytes to a state similar to young chondrocytes, enhance their cartilage formation capacity, thus allowing ACI in older patients. In this study, a cytoskeleton-targeting method was tested to “rejuvenate” aged chondrocytes through a suspension culture on agarose, which mimics the developmental condensation process. The capacity of rejuvenated chondrocytes to make hyaline cartilage was also examined, with untreated and aged chondrocytes as the control [2].

Figure 1: An illustrated model to represent the “rejuvenation” of old chondrocytes.

METHODS With IRB approval, human chondrocytes were isolated from knee joint hyaline cartilage (patient donors between 68-70 years old). Cartilage was cut into approximately 1-mm2 pieces and then washed with medium. Chondrocytes were then isolated by digesting the cartilage with collagenase in an incubator (37oC). The cell suspension was then filtered with a 40-μm cell strainer and centrifuged for at 300 g for 5 minutes before resuspension in growth medium. Once the flasks were seeded and reached at least 70% confluency, the cell flasks were washed with HBSS, passaged with 4 mL of trypsin, and incubated for 5 minutes. After incubation, the flasks were removed, and the resulting trypsin solution was transferred into 15mL collecting tubes. Once centrifuged at 1.3 g (1300 rpg) for five minutes, the supernatant was removed, and the cells were resuspended with 10% FBS solution to break the pallet and count the cells. The cells were then partitioned and saved for analysis. To get a control for the cells without treatment, ~0.3 x 106 cells were collected, removed of supernatant, and immersed in Qiazol lysis reagent to remove fatty acids and standard tissues before RNA isolation. The remaining 10 million cells were divided into equal groups for the two different methods – an agarose suspension culture and a standard tissue culture plastic (TCP) culture. To create the agarose suspension culture, a solution of agarose was produced by dissolving one gram of agar in 50 mL of PBS with heat. Once the agar was dissolved, approximately 1 mL of solution was pipetted in each well of a 24-well plate. While the agarose solidified in each well, the cells were suspended in 2% FBS solution to have 25 mL of total solution. Once the agarose solidified, approximately 1 mL of cells was introduced to each well, balanced with growth medium. On the other hand, 12 mL of cells were introduced to two TCP cell culture flasks. Both culture systems were incubated at 37oC for three days.

After collected the cell from two cultures through trypsin treatment, the cells were lyzed in the TRIZOL reagent, and total RNA was extracted, followed by reverse transcription into complementary DNA (cDNA). Aged and rejuvenated chondrocytes were characterized using real-time reverse transcription polymerase chain reaction (RT-RCR), based on expression of chondrogenesis markers, including aggrecan (AGN), collagen type II (COL 2), collagen type I (COL 1), SOX9, and versican (VCAN). Ribosomal Protein L13a (RPL13A) was used as an endogenous housekeeping gene for the PCR. DATA PROCESSING To analyze the PCR results, Microsoft Excel was utilized to tabulate Ct values from the RT-RCR process of the Step-Wise Machine. The data was then manipulated to calculate delta Ct values as compared to the housekeeping gene to standardize the values and further used to calculate the amount of relative gene expression. The results were graphed through GraphPad PRISM 7, using bar graphs with standard deviation lines. RESULTS In comparing gene expression of crucial chondrogenic markers between young and old chondrocytes with PCR, the tables in Figure 2 plot relative gene expression to the treatment of the cells. All values above 1.5 standard deviations from the mean were excluded from calculations.

Figure 2: Results from PCR analysis of the three different treatment groups across the five genes. Each gene tested was triplicated to verify results and remove outliers. Values are mean + SD with a sample size of at least n = 6.

DISCUSSION From the PCR results, cells suspended on agarose displayed a higher level of COL 2 and VCAN

expression to a statistically significant degree as compared to cells cultured on TCP or without any treatment. An increase in COL 2 for the rejuvenated cells demonstrates the increase of young chondrocyte phenotype. Additionally, VCAN, expressing an anti-adhesion protein that has a crucial role in cell adhesion, migration, and proliferation, also demonstrates increased expression. Although it is an anti-adhesion protein, contradictory to properties of young and old chondrocytes, it may either be part of other pathways in chondrogenic differentiation or cell aging that are currently unknown. COL 1 is a protein that provides elasticity and rigidity to cells, which is a marker of fibroblasts. Higher expression of COL 1 indicated lower quality of chondrocytes. Therefore, it is positive to see a decrease in COL I in rejuvenated cells as compared to old cells suspended on TCP. Similarly, young chondrocytes tend to have strong chondrocyte-chondrocyte and chondrocyte-extracellular matrix interactions because of aggrecanâ&#x20AC;&#x2122;s ability to bind to hyaluronic acid of the cellâ&#x20AC;&#x2122;s surroundings, so an increase in AGG in rejuvenated cells is an indicator of young chondrocyte phenotype. Observing these patterns in the data analyzed is a positive sign that the suspension culture on agarose can reverse aged chondrocytes to a state similar to young chondrocytes. Further studies will delve into various conditions for the suspensions, such as the amount of time the cells are cultured for in suspension, the material the cells are suspended in, and potential pathways that can be targeted for the reversion of chondrocyte aging. We will also further examine the cartilage formation capacity, by introducing cell pellets into animals. REFERENCES 1. Buckwalter, J. A., et. al. Perspectives on Chondrocyte Mechanobiology and Osteoarthritis. 2006. 2. Hung, BP., et al. Quantitative characterization of mesenchyme stem cell adhesion to the articular cartilage surface. 2013. ACKNOWLEDGEMENTS The author would like to thank the Pepper Grant (P30AG024827), NIH (1R21AG056819), and the Swanson School of Engineering for providing opportunities and funding for this research endeavor.

OPTIMIZATION OF DECELLULARIZATION OF SKELETAL MUSCLE VIA INFUSION FOR MUSCLE RETENTION FOLLOWING PERIPHERAL NERVE INJURY Kristen Byrd, Benjamin Schilling, M.S., Jocelyn Baker, Malik Snowden, Kacey Marra, Ph.D. Department of Bioengineering, Department of Plastic Surgery, University of Pittsburgh, Pittsburgh, PA, USA

Email: krb116@pitt.edu INTRODUCTION Peripheral nerve injury (PNI) is an issue that affects many people in our society especially those who serve in our armed forces. After treatment and healing of PNI, muscle tends to atrophy, which leads to loss of muscle function. As a result, these individuals cannot contribute socioeconomically and spend thousands on therapy to regain the muscle function that is lost. In fact, approximately $150 billion is spent annually on peripheral nerve injury and its related treatments. Not only is the standard of care to treat muscle atrophy resulting from PNI inefficient, but the cost is as well. Decellularization1,2 is a useful tool to rid tissue of its cellular content while preserving the macrostructure and key proteins residing in muscular tissue. Recent studies show that ECM structure and retained endogenous growth factors provide myogenic growth. This is crucial for recovery of the musculature and has the potential to benefit these patient who have experienced a PNI. The objective of this project was to design a decellularized biomaterial that contains the necessary growth factors to promote muscle retention after PNI. This project was spent optimizing the methods of decellularization of porcine muscle via fluidic infusion inside bioreactor in order to efficiently remove cellular content from the muscle. The decellularized muscle is to be used to create a hydrogel, which will act as a scaffold containing growth factors.

METHODS Previous literature1,2 has suggested that using 1% sodium dodecyl sulfate (SDS) is sufficient for tissue decellularization, though it takes considerable time to do so, typically in excess of 5 days. In order to optimize decellularization, a design of experiments (DOE) was implemented. This DOE kept muscle mass, infusion time, needle type, solution volume and flow rate constant while varying the solution concentration. Porcine gastrocnemius was cut into approximately 1.30 gram chunks and placed onto a 3-pronged, 18-gauge needle. The varying solution concentrations used were 0.1% SDS, 0.3% SDS, and 1% SDS. This was then placed into

a bioreactor for approximately 22 hours. The bioreactor pumps the fluid through the needle and as a result, infuses through the muscle. During the infusion, a time lapse was setup to visually assess decellularization. After each infusion, 2 liters of DI water were infused throughout the muscle in order to remove residual SDS solution. After the DI water rinse, the remaining extracellular matrix (ECM) was frozen at -80oC. The frozen ECM as well as normal, frozen pig gastrocnemius was sectioned and stained using Hematoxylin and Eosin (H&E), PicoGreen, and dystrophin and insulin-like growth factor (IGF)-1 to characterize the structure, quantify any remaining DNA content, and evaluate protein retention, respectively.

RESULTS Following the histology of both regular porcine muscle and the ECM left after decellularization, each were imaged and the overall structure was assessed. For the Hematoxylin and Eosin staining, we evaluated proper decellularization based off of the amount of nuclei present. Remaining nuclei were depicted by the smaller bluish, black dots in the stain while ECM content appears pink. As seen in Figure 1, minimal nuclei were seen in the decellularized sample while in Figure 2 the regular porcine muscle contained several nuclei as depicted by the black specks. This shows that structurally, cellular content was successfully removed while the protein component, or structure, remained using our DOE. PicoGreen, Dystrophin, and IGF-1 data and images are pending.

leaving behind the ECM which will be used in vivo along with growth factors in order to maintain muscle mass during peripheral nerve injury recovery. Our results show that decellularization is a possible solution that would solve these problems. With further investigation and better optimization we will be able assess its full potential.

REFERENCES 1. Forcales et al. (2015) Potential of adipose-derived stem cells in muscular regenerative therapie.s 123. doi:10.3389/fnagi.2015.00123 2. McClure et al. (2018) Decellularized Muscle Supports New Muscle Fibers and Improves Function Following Volumetric Injury. doi: 10.1089/ten.tea.2017.0386

ACKNOWLEDGEMENTS CONCLUSION Peripheral nerve injury recovery leads to muscle atrophy. Current treatment is inefficient both anatomically and financially. Our optimized DOE for decellularization of muscle has shown to minimize cellular content in tissue

I would like to thank the Swanson School of Engineering for providing me with this opportunity. I would also like to thank Dr. Marra and Benjamin Schilling for the mentorship and guidance throughout the summer.

Infusion of Decellularized Skeletal Muscle for Intervention in Muscle Atrophy Following Peripheral Nerve Injury Malik Snowden1, Benjamin Schilling1, Jocelyn Baker1, Kristen Byrd1, Kacey Marra, Ph.D.1,2 Dept. of Bioengineering1, Dept. of Plastic Surgery2 University of Pittsburgh, Pittsburgh, PA 15213 Email: mjs243@pitt.edu Web: plasticsurgery.pitt.edu Introduction Peripheral nerve injury (PNI) is followed by a series of complex events that removes and repairs old, damaged tissue. After serious injury, connection with the target muscle is lost, leading to muscle atrophy and fibrosis. Regrowth of the injured nerve does not necessarily lead to muscle regeneration. This is especially true if combinative therapies to address both nerve injury and muscle atrophy are not employed. The primary aim of this project is to develop a tissue engineered therapy to address muscle atrophy and loss of function via the use of a biomaterial scaffold capable of providing the affected muscle with proteins and growth factors needed to delay atrophy. Foracles (2015) examined the creation of a muscle-derived extracellular matrix (mdECM) to be used in the development of a hydrogel drug delivery system. This study employs a 3D printed bioreactor capable of decellularizing muscle. The creation of an mdECM through the use of a bioreactor is a process that requires optimization, as decellularization and disinfection are both processes that are very deleterious in nature. It is important to ensure that protein content is retained while still removing DNA content to promote myocyte proliferation at the injury site. Methods In order to ensure that optimal DNA and protein content are retained in the mdECM hydrogel after decellularizing a portion of muscle from a porcine model, a Design of Experiments (DOE) was developed which

allowed for the control and examination of the effects of decellularization through variation of the SDS concentration. The first solution consists of sodium dodecyl sulfate (SDS), which although effective at decellularization, has been shown to cause protein denaturation. The second solution consists of SDS and ethanol (EtOH), as shown in Table 1 below. Following decellularization, the remaining ECM was sectioned and stained with Hematoxylin and Eosin (H&E) and against dystrophin to characterize remaining cell content.

Results and Discussion Stained sections of ECM were compared to stained sections of native porcine muscle to compare cell content after the decellularization process. Stained sections were also compared based on their decellularization conditions. Comparison of decellularized and native H&E samples shown in Figures 1 and 2 show that rinsing the tissue in the bioreactor with an SDS solution leads to a visually significant decrease in cell content, leaving behind original muscle structure and ECM.

mdECM scaffold would be able to provide denervated muscle with proteins and growth factors required to delay atrophy. The decellularization processes used in this study have been shown successfully remove the muscleâ&#x20AC;&#x2122;s cell content to allow for in vivo usage and to promote myocyte proliferation.

Figure 1. Section of native porcine gastrocnemius stained with Hematoxylin and Eosin.

Future Directions Due to time limitations, there were certain stains and experiments that were not performed. In the future, it will be beneficial to IHC-stain against dystrophin in order to quantify muscle fiber content following tissue decellularization. In addition, new infusion decellularization conditions will be examined to further decrease remaining cell content, including the use of new decellularization solutions and varying of flow rates. References 1. Mcclure, M et al. Tissue Engineering, 24, 15-16, 2018. 2. Foracles, S. Frontiers in Aging Neuroscience, 7, 1-12, 2015.

Figure 2. Section of decellularized porcine gastrocnemius stained with Hematoxylin and Eosin.

Conclusion Peripheral nerve injury has been shown to lead to distal muscle atrophy as the result muscle denervation. This provides a role for a new treatment designed to prevent atrophy. This proposed treatment which utilizes an

Acknowledgements I would like to thank the Swanson School of Engineering, the Office of the Provost and Dr. Kacey Marra for providing and funding this opportunity. I would also like to thank Dr. Kacey Marra and Benjamin Schilling for their mentorship and guidance.

Table 1. Description of bioreactor decellularization conditions

Condition Condition 1 Condition 2 Condition 3 Condition 4

Decellularization Solution 1.0x SDS Solution 1.0x SDS Solution 1.0x SDS, 30% EtOH Solution 1.0x SDS, 30% EtOH Solution

Count 4 4 4 4

DIFFUSION TENSOR IMAGE ANALYSIS OF STROKE DAMAGED BRAINS TREATED WITH COMBINED NEURAL STEM CELL AND PHYSICAL THERAPY Lauren Grice, Harman Ghuman, Franziska Nitzsche, Madeline Gerwig, Jeffrey Moorhead, Nikhita Perry, Alex Poplawsky, Brendon Wahlberg, Fabrisia Ambrosio, and Michel Modo Regenerative Imaging Laboratory, McGowan Institute for Regenerative Medicine University of Pittsburgh, PA, USA Email: leg68@pitt.edu, Web: http://www.radiology.pitt.edu/ril.html INTRODUCTION Stroke is the leading cause of adult disability in the United States and results in the physical and cognitive impairment of more than 795,000 Americans annually [1]. Currently, physical therapy (PT) is the only approved intervention after stroke. Although PT in the form of aerobic exercise has been shown to upregulate factors involved with neurogenesis and angiogenesis, it provides insufficient functional recovery. Therefore, Neural Stem Cells (NSCs) have become a promising option for stroke treatment because they can integrate into peri-infart tissue to replace lost cells. Additionally, NSCs secrete trophic factors that may underlie functions of tissue remodeling, such as dendritic plasticity and axonal rewiring. Therefore, it is hypothesized that the combination of human NSC injection and PT in a rat model of ischemic stroke may yield a more efficacious therapy than either treatment alone. In this study, T2-weighted, diffusion tensor imaging (DTI) was used to quantify neuronal connectivity changes in the brains of rats. DTI can detect various cellular changes and tissue abnormalities as it is sensitive to the magnitude and orientation of water movement [2]. In DTI, fractional anisotropy (FA) measures the anisotropy of water diffusion in tissue and is used to trace neuronal connections, represented as streamlines. Therefore, an increase of FA suggests improved microstructural integrity.

stressors from transport. After acclimatization, animals were randomly assigned to undergo middle cerebral artery occlusion (MCAo; n=91) or sham surgery (n=9). Rats subjected to MCAo were anesthetized using isoflurane and incised on the ventral side of the neck to expose the common carotid artery. Then, a 5-0 silicone rubber-coated monofilament was inserted and advanced to the ostium of the middle cerebral artery (MCA) in the circle of Willis until a slight resistance was felt. The filament remained in the MCA for 70 minutes before it was drawn back to the carotid bifurcation to allow reperfusion. Then, the wounds were closed and animals recovered from anesthesia. After surgery was completed, rats were given two weeks to recover during which extensive post-operative care was provided. To verify stroke success ten days after surgery, animals underwent magnetic resonance imaging (TurboSpin Echo sequence, TR=5891ms, TE=40ms, 10 Averages 60 slices, 0.3mm slice thickness, FOV=30x30, Matrix=192x192, Acquisition Time=25 min, on a 9.4T Bruker microimaging system). Rats with ischemic stroke lesions >10 mm3 (n=39) were randomly assigned into four groups (equivalent lesion volumes for all groups): MCAo only, NSC treatment, exercise treatment, and combined treatment. After ten weeks of treatment, animals underwent DTI (TR= 2500ms; TE=19ms; 4 Averages; number of directions= 6; 60 slices, slice thickness 0.3mm, FOV 30x30 mm; Matrix 96x96; Acquisition Time 85 min).

METHODS Throughout the entirety of the study, adult male Sprague Dawley rats were kept on a 12-hour light/dark schedule with constant access to food and water. Upon arrival, the rats (n=100) underwent one week of acclimatization to avoid

Rats in the MCAo+NSCs and combined treatment groups received a 4.5 ÎźL CTX0E03 NSC suspension at a rate of 1 ÎźL/minute (450,000 cells/rat). Cell viability was determined to be >86% prior to surgical implantation. The implantation coordinates of the cells were: 1, AP

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Figure 1: Graphs show percent change from 1 week after stroke to 1 1 weeks after with standard error of the mean. ROIs are from ipsilateral hemisphere.

-1.3 mm; L -3.5 mm; V -6.5; 2, AP -1.8 mm; L 40 mm; V -6 mm. If the implantation site was damaged, coordinates were modified so injection reached the peri-infarct area. Aerobic exercise on a treadmill was the means of physical therapy administered to the exercise and combined groups. Each rat ran at 80% of its maximum capacity (determined by using the Bruce protocol) for 30 minutes, 5 days/week. These parameters have previously been shown to optimize the effects of physical therapy by inducing positive oxidative physiological adaptations; opposed to 15 and 60 minutes of aerobic exercise, a 30 minute time interval improves maximum capacity by 80% [3]. DATA PROCESSING Using DSI Studio, a tractography software, three-dimensional maps of anatomical regions of interest (ROIs) were manually drawn on the MR images to delineate the motor cortex (MC), somatosensory cortex (SMC), thalamus, and striatum. Once the ROIs were drawn, scalar indices of FA and number of streamlines were recorded. RESULTS Exercise alone significantly increased FA in the MC, SMC, and striatum (p<0.05). Conversely, NSCs alone increased FA in the MC, SMC, and thalamus (p<0.05). As shown in figure 1 above, the combined group had no FA increase in any of the ROIs, suggesting the combination of NSCs and exercise may not greatly affect

microstructure organization. The streamline data show that transplantation of cells increased the streamline count in the MC (39.4%), while exercise increased streamlines in the thalamus (37.0%) and SMC (61.7%). DISCUSSION Exercise and NSCs have varying effects on tissue microstructure depending on brain region. NSCs appear to have the greatest influence in the MC, as both the FA and number of streamlines increased after treatment. The increase in both these measures suggests a corresponding increase in anatomical integrity of the MC. Using the same reasoning, exercise appears to be most effective in the SMC. Furthermore, compared to either NSC or exercise treatment alone, combined therapy has little effect on FA and streamline count. Therefore, it appears that exercise and NSCs do not have a greater therapeutic effect when combined, but rather their effects are sub-additive in regard to microstructural change. REFERENCES 1. Benjamin et al. Circulation 135.10, 146-203 2017. 2. Mori & Zhang. Neuron 51.5, 527-539, 2006. 3. Dalise et al. Scientific Reports 7.1, 2017. ACKNOWLEDGEMENTS Lauren Grice was supported by the University of Pittsburgh Swanson School of Engineering and Office of the Provost throughout the entirety of this research.

COMBINED NEURAL STEM CELLS AND PHYSICAL THERAPY IMPROVE SOMATOSENSORY CORTEX ACTIVITY AFTER STROKE Nikhita Perry, Harman Ghuman, Franziska Nitzsche, Madeline Gerwig, Jeffrey Moorhead, Lauren Grice, Alex Poplawsky, Brendon Wahlberg, Fabrisia Ambrosio, Michel Modo Department of Radiology, Department of Bioengineering, Department of Physical Medicine & Rehabilitation, Department of Neuroscience University of Pittsburgh, PA, USA Email: njp59@pitt.edu INTRODUCTION Stroke is the leading cause of adult disability, and one in every six people are affected worldwide. Stroke caused by middle cerebral artery occlusion (MCAO) can lead to long-lasting behavioral and functional impairment. The infarcts created by MCAO affect the striatum and the dorsolateral cortex. The loss of blood flow to these regions of the brain results in cell death [1]. Treatment options for stroke are limited, but two of the leading options are physical and stem cell therapy. In animal models of stroke, neural stem cells (NSCs) have improved functional recovery. Preclinical studies on the use of stem cells for the treatment of stroke have shown that the administration of various types and sources of stem cells can reduce neurological deficits by about 55% and in some cases, significantly reduce the size of the infarct by approximately 50%. Large numbers of active stem-cells can implement the regeneration of nerve tissue in the brain, and activate cells around the suffering brain tissue to catalyze rapid healing and to improve brain function [2]. Physical therapy (PT) promotes neuroplasticity, and allows the restoration of functional connectivity after damage from stroke [3]. Both stem-cell therapy and physical therapy have proven to be effective independently. However, a combination of the two therapies could produce combined therapeutic effects, greater than each treatment on its own. The aim of this project was to establish how PT affects NSCs, and to quantify the effect of each therapy independently. One region thought to experience a change in blood flow and activity with these therapies is the somatosensory cortex (SMC). Functional magnetic resonance imaging (fMRI) measures brain activity by detecting changes in blood flow. Cerebral blood volume (CBV) weighted MRI is a physiological indicator of tissue viability and vascularization. Both fMRI and CBV were used

to measure the effect of each therapy, and establish how cells promote neuroplasticity in the SMC. METHODS Adult, male, Sprague-Dawley rats underwent transient MCAO for the duration of an hour. T2weighted MRI determined success of MCAO, and animals were randomly assigned to the following groups: Controls, MCAO, MCAO+NSCs, MCAO+PT, and MCAO+combined. The animals that underwent NSC transplantation received a perilesional NSC graft (450,000 cells) 2 weeks poststroke. The PT consisted of daily treadmill running at 80% capacity in both groups receiving PT. T2, fMRI, and CBV MRI scans were collected using a 9.4T magnet at the 10-week time point. For CBV acquisition, each animal was scanned for 5 minutes before intra-arterial 15 mg/kg monocrystalline iron oxide nanoparticle (MION) injection to provide a baseline intensity, and then 5 minutes after injection (TR= 2500ms; TE= 19ms; 4 Averages; number of directions= 6; 12 slices, slice thickness 0.3mm, FOV 30x30 mm; Matrix 96x96). ROIs for the striatum, thalamus, motor cortex (MC), and SMC were drawn on both the pre and post MION scans in order to determine blood volume in each region. Immediately after CBV acquisition, an electrode was inserted subcutaneously in each forepaw for contrast-enhanced fMRI acquisition. Each paw was stimulated alternately for 5 minutes with a current of 1 mA over the duration of an hour, with short periods of no stimulation to provide a resting-state intensity. DATA PROCESSING The signal change between scans is directly related to the proton relaxation rate induced by MION (Î&#x201D;R*2;agent (s-1)). This rate can be found from the intensities of the pre and post MION CBV via the equation: Î&#x201D;R*2;agent (s-1) = ln(Spre/Spost)/TE

where Spre and Spost are the signal intensities before and after MION injection. For the condition in which static dephasing is dominant, the change in apparent transverse relaxation rate in tissue caused by MION, and in the absence of stimulation can be described as: ΔR*2;agent = (4/3)π (1 – Hct) fCBV Δχagent γB0 where fCBV is whole blood volume fraction (mL blood/mL brain), which includes plasma and blood cells, and γ is the gyromagnetic ratio, which is 2.675 x 108 rad/(sT). Assuming a cortical hematocrit (Hct) of 0.40 and Δχagent of 0.29 ppm, fCBV can be calculated from ΔR*2;agent. fCBV is then converted to baseline CBV (mL/g) by dividing by the blood density of 1.06 g/mL [4]. RESULTS CBV indicated that there was no significant difference (p>0.05) in blood volume across groups in the four regions analyzed (Fig. 1A).

Figure 1. (A) CBV for regions in ipsilateral hemisphere. (B) fMRI signal map of affected and unaffected hemisphere with a threshold of 0.05. (C) Number of active voxels, amplitude of signal, and time course of a single stimulation in the ipsilateral and contralateral hemisphere

The location of the fMRI signal was in the SMC for all treatment groups (Fig. 1B). fMRI revealed that the combined therapy group had a significantly higher

number of active voxels (p<0.05) in the ipsilateral hemisphere than the other groups. However, there was no significant difference in the amplitude of the signal in that hemisphere, as well as the number of voxels and amplitude of signal in the contralateral hemisphere (Fig. 1C). DISCUSSION CBV indicated that neither NSCs nor exercise had an effect on tissue viability in the ipsilateral hemisphere. fMRI revealed that a larger area of the brain was active in the combined group. However, the strength of this signal was not significantly different between treatment groups in the ipsilateral hemisphere. This suggests that exercise affected the NSC graft, and caused a larger volume of the brain to respond to the electrical stimulus than the group that received the transplanted cells without exercise. An explanation for this could be that more tissue needed to be recruited to meet the threshold in order to respond to the stimulus. Past studies have shown that NSC implantation results in an enhanced fMRI signal change in the treatment group in comparison to the control group [5]. fMRI has been used previously to monitor brain activity during a motor learning task, but little is known about the effects of a combined NSC and physical therapy. This data demonstrates that a combined therapy greatly promotes neuroplasticity after stroke, compared to NSCs and PT alone, therefore potentially offering a new “hybrid” treatment option for stroke patients. REFERENCES 1. Chiang et al. Journal of Visualized Experiments 48, 2761, 2011. 2. Kalladka et al. Stem Cells Cloning 7, 31-44, 2014. 3. Peppen et al. Clinical Rehabilitation 18, 833-862, 2004. 4. Seong-Ge et al. NMR in Biomedicine 26, 949-962, 2013. 5. Duffy et al. Department of Health and Human Services 28, 83-88, 2014. ACKNOWLEDGEMENTS The summer research fellowship award funded by the Swanson School of Engineering and the Office of the Provost supported Nikhita Perry during the course of this research.

Myocardin Related Transcription Factor’s Role in Cell Migration Aidan Dadey, Dave Gau and Partha Roy Cell Migration Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: aid11@pitt.edu, Web: https://www.engineering.pitt.edu/P_Roy INTRODUCTION Breast cancer (BC) is the second leading cause of cancer deaths in the United States for women, much of which is due to metastasis of the cancer. In particular, triple-negative breast cancer (TNBC) has a higher propensity to metastasize and reoccur posttreatment. Treatment of TNBC is difficult because of the tumor heterogeneity and lack of well-defined molecular targets. The five-year survival for patients with TNBC is less than 30% despite adjuvant chemotherapy treatment. Recurrence of metastatic tumors arises from a switch from “dormant” state single cells or non-growing micrometastasis into an active proliferative state. As such, keeping BC cells in a dormant state and/or slowing the metastatic growth of BC cells should prolong the survival of TNBC patients. The overall goal of this study is to identify new molecular targets and therapeutic agents for metastatic TNBC. Recent studies have highlighted the importance of actin cytoskeleton regulatory proteins and their upstream regulators in promoting metastatic outgrowth of disseminated BC cells. In response to actin polymerization, myocardin-related transcription factor (MRTF) promotes the activation of serum-response factor (SRF), and in turn SRFmediated transcription initiates for a wide range of actin cytoskeletal and adhesion regulatory proteins in addition to SRF itself. Loss of function of either MRTF or SRF dramatically impairs the development of pulmonary metastases from extravasated TNBC cells suggesting critical importance of MRTF/SRF signaling for metastatic colonization of TNBC cells. However, the mechanism by which MRTF regulates BC metastasis and whether MRTF depends on SRF function is still unknown. It is known that MRTF can also promote gene transcription in an SRFindependent manner utilizing its SAP-domain (a putative DNA-binding region of MRTF) function. There is also emerging evidence of the ability of MRTF to influence cancer cell migration and

proliferation through its SAP-domain function. Utilizing a proliferation assay that is known to successfully predict the post-extravasation pulmonary metastatic outgrowth competency of BC cells, we have interestingly found that depletion of MRTF but not SRF significantly retards the outgrowth of isolated TNBC cells suggesting that MRTF may also have SRF-independent function. Recently, we reported MRTF plays a key role in positively regulating the expression of actin-binding protein Profilin-1 (Pfn1 - an important regulator of actin cytoskeleton dynamics) through an indirect mechanism in an SRF-independent manner that in fact is linked to its SAP-domain function. We hypothesize that MRTF has both SRF-dependent and SRF-independent (SAP-domain dependent) functions promoting different aspects of metastatic colonization such as cell migration, and that Pfn1 is one of the important downstream mediator of SAPdomain-directed MRTF function on migration. To test this hypothesis, I will perform motility studies to examine the role of various constructs of MRTF (wild-type, ∆SAP, and 3pt(mut-b1) [a form of MRTF that is unable to interact with SRF]) on MDA-231 triple negative breast cancer cell migration. METHODS MRTFA/B siRNA (Thermo Fisher/Santa Cruz) or control siRNA (Thermo Fisher) was transfected using RNAiMAX (Invitrogen) at 25 nM each for some assays. In complementary experiments, MRTF-A overexpression was performed by transiently transfecting cells with flag-tagged MRTF-A constructs. Random cell migration assay was performed by time-lapse imaging of MDA-231 cells at one frame a minute for 120 minutes and tracking the centroid of the cells. Serum-induced chemotactic migration of MDA-231 cells was assessed in a transwell assay 6 hours after seeding of the cells. Actin cytoskeleton assay was performed by seeding cells on a collagen coated cover slip then later fixing and staining with

Rhodamine Phalloidin (Invitrogen). Western blot was performed to confirm the knockdown of MRTF if siRNA was used.

caused an increase among all the constructs (Figure 3).

DATA PROCESSING For the random cell migration assay the distance travelled by the centroid of the cell from frame to frame was tracked and obtained through ImageJ. Speed was determined by taking the distance travelled by dividing it by the time between frames using Excel (Microsoft). Statistical significance was determined with SPSS (IBM) for all assays. RESULTS The knockdown of MRTF-A and MRTF-B was successful as shown by Figure 1. MRTF-A

Figure 3: Transient transfection of MRTF-A constructs, wild-type (MKL WT), Î&#x201D;SAP (MKL SAP), and the 3-point mutant (MKL 3pt), in MDA-231 cells showed that MRTF overexpression increases migration, p-value of <0.05

MRTF-B

Tubulin

Figure 1: siRNA treatment of MDA-231 with either control, MRTF-A, MRTF-B, or both MRTF-A&B siRNA. Tubulin was used as a loading control.

Figure 4: (Left) siRNA treatment of MDA-231 with either control or MRTFA/B siRNA showed that MRTF knockdown reduces chemotactic motility of MDA-231 by almost 50%, p value <0.05 (Right) siRNA treatment of MDA-231 with either control or MRTFA/B siRNA showed that Actin intensity drops by about 50%, p-value <0.05

DISCUSSION Knockdown of MRTF showed to have a statistically significant decrease in random motility, chemotactic motility and actin intensity by about 50% from the proliferation, transwell and actin assays.

Figure 2: siRNA treatment of MDA-231 with either control, MRTF-A, MRTF-B, or both MRTF-A&B siRNA showed that MRTF knockdown reduces migration of MDA by almost 50%, p-value of <0.001

Overall, the knockdown of each isoform or the double knockdown of both isoforms of MRTF reduced random migration (Figure 2) by almost 50%. Conversely the overexpression of MRTF-A

In summary, our findings provide an initial proofof-concept of MRTF inhibition as a potential novel strategy to inhibit growth and migration of TNBC cells. ACKNOWLEDGEMENTS The author would like to acknowledge the support from the University of Pittsburgh Swanson School of Engineering, University of Pittsburgh Office of the Provost, and Dr. Partha Roy.

A MECHATRONIC SYSTEM FOR THE MAGNETIC MANIPULATION OF BIOMEDICAL SPECIMENS Claire E. Kraft, Michael R. Behrens and Warren C. Ruder Synthetic Biology and Biomimetics Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: clk111@pitt.edu, Web: https://www.warrenruder.com/ INTRODUCTION The use of magnetic materials is prevalent in biomedical applications. One key application of magnetic materials is the separation of biomedical samples for analysis. Here, we report a joystickcontrolled, rack-and-pinion system that uses permanent magnets actuated in two dimensions to dynamically alter the magnetic field around a specimen. METHODS A magnetic manipulator was built consisting of a two-axis, four actuator magnet-coupled, rack-andpinion system. Key parts of the system include permanent neodymium magnets, racks, gears, cclamps, a chip holder, and rack and motor casings. The 3D printed parts were designed and converted to a stereolithography (STL) format using Autodesk Inventor Professional 2019. Solid models of the parts were 3D printed after being converted into printing paths for a 3D printer (Zotrax M200) using accompanying software (Z-Suite). All designs were printed with acrylonitrile butadiene styrene (ABS). Four stepper motors (Sparkfun ROB-09238) were each connected to a gear and inserted into a rack and motor casing along with racks affixed to permanent magnetics. The 3D-printed c-clamps were used to secure the motors to the rack and motor casings to prevent slipping between the gears and racks. The system was controlled by a microcontroller (Arduino Uno) and a joystick (Sparkfun COM-090320). Next, a mass of E. coli cells coupled to magnetic particles was placed with phosphate buffered saline (PBS) in a container formed from polydimethylsiloxane (PDMS, Sylgard 184) and placed in the center of the system, as shown in Figure 1. The PDMS container encompassed three ABS beads, which were stuck to the coverslip bottom of the PDMS container and used as obstacles for targeted manipulation of the magnetic mass. Videos

were taken capturing the motion of the specimen following joystick-controlled magnetic manipulation.

Figure 1. Setup of the magnet-coupled rack-and-pinion system with PDMS container. A) Assembled 3D-printed and mechatronic parts of the magnet-coupled rack-and-pinion system. B) PDMS fluidic device filled with PBS, black magnetic mass, and blue, green and orange ABS beads.

RESULTS The movement of the magnets could be controlled by the joystick, in turn controlling the motion of the magnetic mass. The motors moved individually so that when one magnet moved toward the PDMS container, all other magnets remained at their starting positions. When the joystick was released to its stationary range, the most recently manipulated magnet returned to its starting position and control of that magnet and all other magnets was immediately enabled. Using this system, the specimen could be moved left, right, forward, and backward. Though only one magnet can be moved toward the PDMS container at a time, in separate motions two magnets could be moved near the container to produce a diagonal

motion in the specimen. The videos taken of the magnetic mass, screenshots of which appear in Figure 2, demonstrated that the rack-and-pinion system could be used to effectively manipulate a sample with permanent magnets.

materials. In the future this system will be used for precise motion control of magnetic biomedical samples, including bacteria-based microrobotic systems. ACKNOWLEDGEMENTS The authors gratefully acknowledge support from the Office of the Provost, Swanson School of Engineering, the Department of Bioengineering, and the National Science Foundation under Grant No. 1709238.

DISCUSSION The magnet-coupled rack-and-pinion system described here enables simple manipulation of the magnetic field in the center of the apparatus, giving the operator control over the movement of magnetic

Figure 2. Demonstration of magnet-coupled, rack-and-pinion system functionality. A) Position of a magnetic mass in a PDMS container before joystick-controlled manipulation. The specimen was moved, in order, B) up between two ABS beads, C) left to be centered between the ABS beads, D) up through the first set of ABS beads, E) down through the ABS beads, F) up through the ABS beads to be centered between the second set of ABS beads, G) left through the second set of ABS beads, and H) down to the side of the PDMS container. Red lines indicate the direction the magnetic mass moved between screenshots.

VALIDATING FINGERSIGHT WITH A 3D INFRARED TRACKING SYSTEM. Janet Canady, Shantanu Satpute, Roberta Klatzky and George Stetten Visualization and Image Analysis Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: jrc137@pitt.edu, Web: http://www.vialab.org INTRODUCTION Computer vision has long been effective on the factory floor, but it has recently been expanding to more unpredictable applications such as self-driving cars. Its potential for helping visually impaired individuals in real-life applications clearly holds great promise. Our laboratory has been developing FingerSight1,2, which relies on computer vision to recognize and locate specified targets, and then gives guidance via haptic feedback. It consists of a miniature camera and array of vibrators mounted on the finger. Controlled and rapid scanning of the visual environment is possible with the hand, providing the visually impaired user with the ability to “see” with their finger and use that information to navigate areas and find objects critical to their daily lives. To devise optimal control processes for this new human-machine system, we are developing a 3D tracking system for experiments with the FingerSight device, and report on our preliminary results here. METHODS The current FingerSight device is composed of four cell-phone vibrators, as well as a miniature camera mounted to the proximal phalanx of the index finger. Attached to the device is an array of four infrared LED markers to be detected by an Optotrak Certus (NDI, Inc.). In the current preliminary experiment, the participant is blindfolded and attempts to reach a target using feedback from the vibrators, which are arranged around the finger in 90° increments, oriented to the dorsal, lateral, medial, volar aspects of the finger. The target, a 5 mm diameter white spot on a black background, is automatically identified in real time in the camera image using the OpenCV software library, and this information is used to activate the four vibrators through one of four activation strategies: (1) Warmer vs. Cooler, in which all vibrators are activated when the target is seen moving closer to the center of the image, (2) Adjacent Pair, in which two adjacent vibrators are simultaneously activated

to indicate the proper direction of motion to better center the target in the image, (3) Worst Axis, in which the participant is guided along the axis with the greatest error, one axis at a time by a single vibrator, and (4) Both Axes, in which adjacent vibrators are alternately activated to indicate the proper direction of motion. 3D coordinates of each IR-LED are recorded by the Certus at 10 Hz and the centroid is computed. When the participant arrives at the target, the vibrators are all activated with a long pulse to indicate that the experiment is over. RESULTS 3D plots of the path of the centroid over time are shown below for a single subject and trial. All trials were successful in guiding the subject to reach the target, marked at the origin of each graph. The experiment using the Worst Axis (Figure 1) strategy took 16.9 seconds. Performing the task with the Warmer vs. Cooler vibrator activation (Figure 2) strategy took 87.2 seconds to complete. The Adjacent Pair strategy took 17.2 seconds (Figure 3) and the Both Axes (Figure 4) strategy took 16.1 seconds. DISCUSSION While the other three strategies took under 20 seconds each, the Warmer vs. Cooler strategy took over one minute, with no apparent correlation between X, Y and Z axes. The Warmer vs. Cooler strategy gives only permissive cues, giving the user feedback on movement, unlike the others which are instructive strategies, in that tell the user which way to go even when standing still. The presence of controlled feedback is demonstrated by the fact that the guidance becomes more refined as the subject approaches the target. The Both Axes strategy proved to be the fastest, possibly because it is easier to discriminate the individual vibrators but also better to combine them within a short period of time. The Adjacent Pair strategy was thought to have confused the user as to exactly which vibrator was active. The Worst Axis strategy was thought to

be a less efficient tactic because of misalignment of one axis during the alignment of the other.

Figure 1: The purple line indicates the plotted coordinates of the pathway for the Worst Axis strategy. The target is indicated by a blue asterisk (*).

In these trials the Both Axes strategy appeared to result in the most direct path, with the x and y range decreasing as the subject approached the target in the z-direction. CONCLUSION Figure 3: The green line indicates the plotted coordinates Using tracking system, we were ableThe to compare of thethe pathway for the Both Axes strategy. target is indicated by a blue asterisk (*). 4 control strategies. Future studies may include other strategies and record camera orientation as well as location using rigid body transformations. Experiments using multiple subjects will determine statistical significance of the differences between strategies. We expect to use these data to enhance the effectiveness of FingerSight devices used in various tasks.

Figure 2: The blue line indicates the plotted coordinates of the pathway for the Warmer vs. Cooler strategy. The target is indicated by a red asterisk (*).

REFERENCES 1. G. Stetten, R. Klatzky, Fingertip Visual Haptic Sensor Controller, U.S. Patent 9,024,874, May 5, 2015. 2.Figure S. Horvath, Galeotti, R.coordinates Klatzky, of M. 4: The redJ.line indicatesB. theWu, plotted Siegel, G. Stetten, FingerSight: Fingertip Haptic the pathway for the Adjacent Pair strategy. The target is indicated by a of blue the asterisk (*). Sensing Visual Environment, IEEE Journal of Translational Engineering in Health and Medicine, March 2014. ACKNOWLEDGEMENTS Funding provided by the Swanson School of Engineering and the Office of the Provost at the University of Pittsburgh, as well as the Pittsburgh Innovation Challenge (PInCh). We wish to thank Pitt EXCEL for guidance and support.

MICRO-BLIP: A NEW TOOL FOR INSTRUMENTATION EDUCATION Jake Donovan and George Stetten Visualization and Image Analysis, Department of Bioengineering University of Pittsburgh, PA, USA Email:jcd68@pitt.edu, Web: http://www.vialab.org INTRODUCTION For the past ten years, every student in the required undergraduate Bioengineering course in instrumentation at the University of Pittsburgh has constructed a custom microprocessor system we call the Breadboard Laboratory Interface Processor (BLIP) [1, 2]. The BLIP is programmed to function as a recording voltmeter, signal generator, frequency counter, duration timer, and digital event logger, and supplies 5V power for operational amplifier circuits. The BLIP has provided over 500 students with the experience of constructing their own instrumentation system, which they take with them after the course. Recent advances in microcontrollers and the growth of the Arduino community have inspired us to update our system to take advantage of these ubiquitous and user-friendly systems. Last year we designed a new BLIP to incorporate an Arduino Micro microcontroller while maintaining the instrumentation functions of the original BLIP, which we call the “Micro-BLIP” [3]. We present here the results of incorporating this device into the Spring 2018 instrumentation course, as well as our efforts, based on these results, to improve the system further for the upcoming Spring 2019 semester. METHODS An Arduino “Micro” microcontroller, an MCP4801 D/A converter, and a TC77662A charge pump were combined with male and female headers on a custom printed circuit board to plug directly into the student’s solderless breadboard as shown in Figure 1. The functions are navigated between using an interrupt-based button system. This new device appears to a host computer via USB as a human typing on a standard computer keyboard. Data is entered directly into a word processor or spreadsheet, requiring no specific software or operating system.

Figure 1: Micro-BLIP schematic and completed device

RESULTS As opposed to the original BLIP, in which the custom program was permanently programmed into the microcontroller, the Micro-BLIP is now easily reprogrammable by students using the Arduino software libraries and thus we expect it to be more useful for future projects after the course. All of the interface pins are passed through to the breadboard, where they are available for further development and experimentation. In Spring 2018, we introduced the Micro-BLIP into the instrumentation course, where ninety students were given the components and step by-step instructions detailing how to solder them into the printed circuit board. This construction was completed with essentially a perfect success rate, after a few soldering mistakes were identified by teaching assistants. Students subsequently used the Micro-BLIP in weekly experiments involving analog and digital circuits in

9 projects in the weekly laboratory portion of the course. Shown in Figure 2 is a particular project in which the Micro-BLIPâ&#x20AC;&#x2122;s signal generator output and a small microphone are connected to an audio amplifier. Complete descriptions of all the laboratory projects, as well as the rest of the course material, are available through www.pittkit.org.

Figure 2: Schematic and completed circuit for audio lab

In preparation for the upcoming Spring 2019 course, we are updating the Micro-Blip based on our experience with it. We have introduced interrupt-driven routines into the Arduino program, such that the User buttons generate interrupts when pressed and no longer require polling. This has resulted in ~2x speed increase and greater accuracy in many of the operation modes. We ran an experiment to test if we could use inline assembly code to increase the speed further but ultimately found that the compiler was extremely efficient and there was little benefit to using assembly code. We

have changed the waveform generator to allow for much greater precision in frequency determination, by replacing our existing algorithm of stepping sequentially through a waveform table at varying rates, to stepping at a constant rate but with varying size steps. The step-size, which amounts to a deltaphase per constant delta time, is stored as a 32-bit number, whose most significant 8 bits are used to address the waveform table. Thus, a precision of 2^31 (approximately 1 part in 2 billion) is now achieved up to the Nyquist frequency, at which each step is half-way through the table. We are currently looking at making this routine interrupt driven by one of the internal timers on the microprocessor, which would allow for other routines to run in the background and would reduce jitter in the waveform generator. Our current sampling frequency is approximately 100 KHz, limited by our choice of D/A converter, which depends on a serial peripheral interface (SPI). Switching to a D/A converter with a parallel interface could increase this maximum frequency by a factor of 10 but would also increase the size of the PCB. DISCUSSION We believe that students enjoy the hands-on nature of building and using the Micro-BLIP, and that it contributes a valuable experience to their education. By teaching students about microcontrollers and providing them with equipment that they own, we hope will help to increase their potential for lifelong-learning. REFERENCES 1.D. Weiser, et al., BMES, 2005. 2. G. Stetten, et al., ASEE, 2008. 3. J. Donovan, et al., BMES, 2017. ACKNOWLEDGEMENTS Funding was provided by the Swanson School of Engineering and the Office of the Provost

INVESTIGATION OF MULTIPLE STRATEGIES IN MODELING AN EXPANDABLE STENT BY FINITE ELEMENT METHOD Amy Hill Biofluid Mechanics Laboratory, Department of Biomedical Engineering National University of Singapore, Singapore Email: abh26@pitt.edu INTRODUCTION Coronary heart disease (CHD) is one of the leading causes of death worldwide and is characterized by luminal narrowing of the artery [1]. This results in atherosclerosis, or narrowing of the artery causing abnormal blood flow. Narrowing of the coronary artery can lead to angina, arrhythmia, and even heart failure if left untreated [2]. Coronary stents are used to recover the lumen size of arterial wall segments. Stent implantation is minimally invasive, making it one of the most popular treatments of CHD. However, when a stent is deployed into a diseased artery, irregular stresses and deformations can result in non-uniform expansion called the dog-boning phenomenon. This is thought to lead to restenosis, or re-narrowing of the artery [3]. The stent design, as well as inflation technique (self- or balloon-expandable) are common factors that affect the degree of restenosis [4]. Computational analyses are useful tools for simulating stent expansion. Different modeling strategies can be used to evaluate the mechanical stresses present during expansion and reduce the dog-boning effect. One strategy is to apply uniform pressure on the inner surface of stent. Using this inflation strategy, various stent designs were compared in terms of mechanical stress to optimize a stent design and reduce dog-boning. MATERIALS AND METHODS Stent geometries consisting of a linear-link stent and S-link stent, both with circumferential struts, were created using the

commercial 3D CAD software, SolidWorks 2017 (Dassault Systemes Simulia Corp., USA). The parameters for the linear-link stent are as follows: Lx, Link is 0.49 mm, Ly, Link is 0.065 mm, the thickness T is 0.1 mm, W Strut is 0.12 mm, W Link is 0.065 mm, Lx, Strut is 0.795 mm, and Ly, Strut is 0.295 mm. The inner diameter, Di is 1.995 mm. The stent length, Laxial is 9.91 mm. The S-link stent has the same parameters except Ly, Link is 0.443 mm.

Figure 1. Linear-link stent unit design.

Figure 2. S-link stent unit design.

The models were imported into the commercially available simulation software, Abaqus/ CAE 2017 (Dassault Systemes Simulia Corp., Johnston, RI, USA). The 3D mesh for the linear-link was created with 31,724 8-node tetrahedral elements and the S-link was created with 38,315 8-node tetrahedral elements. The stent material was assumed to be 316L stainless steel. It was modeled as a homogeneous, isotropic, elastic-plastic material with a Young’s modulus of 193 GPa, a Poisson coefficient of 0.3, and a yield stress of 205 MPa.

A uniform radial pressure of 1.2 MPa was applied directly onto the inner stent surface. Boundary conditions, restricting axial movement, were established at the center of the stents. The mechanical response of each stent to the applied pressure was evaluated in terms of maximum principal stress, average von mises stress, strain energy, and displacement. The displacement was used to calculate dog-boning as, DB = ((df − dc )/df ) * 100 , where df is the maximum deformation of the stent at the free end and dc is the deformation at the center of the stent. RESULTS Contour maps of the maximum principal stresses of the elements were evaluated for each of the stent designs. The maximum principal stresses occurred at the internal edges of the curved surfaces of the struts at the free end with a value of 0.197 for the linear-link design and 0.257 for the S-link design. The von mises stresses were lower at the curved surfaces of the strut and were evenly distributed across the center and free edges. Figures 3 and 4 show that the S-link stent had a smaller degree of dog-boning (35.1%) at full expansion than a linear-link stent (55.9%).

Figure 3. Displacement contour map of the linear-link stent at the final time step of expansion.

Figure 4. Displacement contour map of the S-link stent at the final time step of expansion.

The linear-link stent expands at the edges first, while the S-link stent expands centrally. The strain energy, which represents creep, was plotted over time with a linear pressure increase. The S-link stent failed at a lower strain energy than the linear-link stent. DISCUSSION AND CONCLUSIONS The mesh, material, and geometry of the stents are good for the purpose of simulating real stent deployment. The model is simple and should have a balloon and artery added to the system for a more accurate depiction. The S-link stent is better at limiting the dog-boning effect than the linear-link stent, which may be due to the way it expands centrally, rather than from the ends. However, the S-link had higher plastic strain over time leading to failure sooner. ACKNOWLEDGEMENTS Computing resources were provided by Prof. Leo and the National University of Singapore through the SERIUS program. Funding was provided by the SSOE Summer Research Program. REFERENCES 1. Go et al. (2014). Heart Disease and Stroke Statistics—2014 Update: A Report From the American Heart Association. Circulation, 129(3), e28-e292. 2. National Heart Lung and Blood Institute (NHLBI). Health Topic: Coronary heart disease. Available from https://www.nhlbi.nih.gov/health-topics/ coronary-heart-disease 2018. 3. Li et al. (2013). Design Optimization of Coronary Stent Based on Finite Element Models. ScientificWorldJournal, 2013.\ 4. Morton et al. (2004). The influence of physical stent parameters upon restenosis. Pathol Biol (Paris), 52(4), 196-205.

ACCURACY OF TWO-PHOTON POLYMERIZATION WITH VARYING OBJECTIVES AND POWER Oreoluwa Odeniyi, Hirut Kollech, Kenny Furdella, and Jonathan Vande Geest Soft Tissue Engineering Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: omo11@pitt.edu, jpv20@pitt.edu

INTRODUCTION Two-photon polymerization (TPP) is a powerful, upcoming technology that yields a lot of potential for fabricating 3-D structures on a micro/nano scale [1]. TPP is a photochemical process initiated by a laser beam focused tightly into the volume of the photosensitive resins called photoresists by a highnumerical-aperture objective. The goal of this work was to determine how accurate TPP was concerning objective choice, power aperture opening percentage, and curing. It was hypothesized that the percent error in length and height would decrease when the power percentage increased and decrease as the objective choice field of view decreased. METHODS A commercial photoresist was placed on a Premium Superfrost Microscope Slide with a depth over 500m. A scratch was then added to the top layer of the slide to detect where the top of the slide and the bottom of the photoresist were under the microscope. After being placed under the microscope, an house software program was used to control the laser. First the objective had to be identified; in this case the 2X, 4X, and10X were used. Then, using the scratch on the slide the range had to be established. The scratch would be the start of the scan and then the objective was moved up 500m, establishing the end of the scan. This technique works the same way extrusion printers work by building from bottom to top, layering each level on top of each other. Then, a script was created to write the letters ‘PITT’ on the x and y plane; then a second script was created that repeat the movement of the objective in the z direction. Once the script was run, the duration of the TPP was varied depending on the objective choice, as step size was kept constant at 10m, pixel time at

1 microseconds, and wavelength at 780nm. Table 1 below displays the loop frequency and duration it took to create the PITT sign for each respective objective. After the TPP had finished, the photoresist was developed. It was placed in a SU-8 photoresist Table 1: Duration of PITT sign Creation Objective Loop Duration(min) Frequency (Hz) 2X

0.03

0.06

10X

0.15

developer for 20 minutes. It was then rinsed with isopropanol acid for one minute. It was then left out to dry leaving only the PITT sign, as shown in Figure 1 below (not to scale).

Figure 1: Image of PITT sign under microscope after developed. Height measurement was vertical and length measurement was horizontal.

RESULTS The results of these experiments supported the hypothesis that when power is increased, there was a decrease in the percent error between the projected length of the “PITT” sign and the actual length that was printed, as well as the projected and actual height. Meaning, the fabricated “PITT” scripts throughout this whole work were always narrower and shorter than the targeted shapes. However, the hypothesis stating that there would be a decrease in the percent error concerning the length of the PITT sign when the objective decreased in field view was not supported. It was observed that the 10X objective at 60% power had the greatest percent error at 48%, and the 4X objective at 90% power had the least percent error at 11%. The hypothesis stating there would be a decrease in percent error concerning the height of the PITT sign when the objective decreased in field view was partly supported. The 2X objective had the highest percent error. However, at 60% power the 10X not developed and developed were higher in percent error than the 4X, but as the power increases the 10X both developed and not developed are lower in percent error than the 4X (see Figure 3).

Figure 2: Length percent error of PITT sign as a result of varying objective and power

Height Percent Error (%)

DATA PROCESSING The length and height measurements for the projected length and height of the entire Pitt script logo and the actual printed length and height were measured using Fiji [2]. These images were produced using the two-photon microscope. The percent error was calculated by finding the difference between the projected value and the actual value and dividing that difference by the projected value and multiplying the total by 100.

30 25 20 15 10 5 0 60

100

Power 2X Objective 10X Developed

4X Objective 10X Not Developed

Figure 3: Height percent error of PITT as a result of varying objective and power

DISCUSSION One explanation for this observation concerning objective choice is that the bigger the objective, the more memory must be stored on the points that will be printed, causing the machine to run out of storage and not print everything. However, if the objective choice is smaller, then the frequency is higher, not allowing enough time for the material to be exposed to the laser light throughout. The results for laser power are consistent with idea that longer laser exposure times lead to more accurately printed structures. Lastly, as a combination of all these factors, when the material goes through the curing process, the percent error increases due to the material not being fully developed under the laser light. REFRENCES [1] X. Zhou. “A Review on the Processing Accuracy of Two-Photon Polymerization.” 2015. https://aip.scitation.org/doi/full/10.1063/1.4916886. [2] J. Schindelin. Arganda-Carreras, I. & Frise, E. et al. (2012), "Fiji: an open-source platform for biological-image analysis", Nature methods 9(7): 676-682, PMID 22743772, doi:10.1038/nmeth.2019 (on Google Scholar). ACKNOWLEDGEMENTS This research was supported by the Swanson School of Engineering, University of Pittsburgh as well as the University of Pittsburgh Center for Medical Innovation. We thank our colleagues from the University of Pittsburgh who provided insight and expertise that greatly assisted the research, although they may not agree with all the conclusions of this paper.

ADVENTITIAL DELIVERY OF THERAPEUTIC CELLS TO LARGE ANIMAL AORTAS Trevor M. Kickliter1, Timothy K. Chung2, Aneesh K. Ramaswamy2, Justin S. Weinbaum2,3,7, and David A. Vorp2,4,5,6,7,8 Departments of Mechanical Engineering and Materials Science1, Bioengineering2, Pathology3, Surgery4, Cardiothoracic Surgery5, and Chemical and Petroleum Engineering6, McGowan Institute for Regenerative Medicine7, Center for Vascular Remodeling and Regeneration8, University of Pittsburgh, Pittsburgh PA

INTRODUCTION Rupture of an abdominal aortic aneurysm (AAA) is a devastating medical phenomenon, ranking as the 13th leading cause of death in the United States [1] and possessing a mortality rate of 90% [2]. To avoid this outcome, patients typically undergo endovascular repair once their AAA grows beyond 5.0-5.5 cm in diameter [3]. However, many AAAs rupture below this diameter [4]. Further, surgical repair of AAAs below this size does not improve survival rates [5]. As such, surgical intervention often fails to treat many patients in need of care and subjects many others to unnecessary risks. This necessitates new therapies for AAA that overcome these shortcomings. Our group has previously demonstrated the effectiveness of adipose-derived mesenchymal stem cells (ADMSCs) as a treatment for AAA. In that study, ADMSC delivery halted aneurysm growth and elastin degradation in an elastin perfusion model of AAA [6]. However, a method to effectively target ADMSCs or other therapeutic cells to the aorta has yet to be developed or tested in large animals. Therefore, the goal of this work was to perform an in-vitro proof-of-concept study for a novel method for localizing therapeutic cells to the adventitia of large-animal aortas. The results suggest that the method could be used to guide cellbased treatments for AAA and other diseases. METHODS Aortas were harvested from six adult pigs and subsequently frozen in Hank’s Balanced Salt Solution. The average aortic diameter was 1.8 ± 0.1 cm. The aortas were prepared by removing connective tissue and ligating peripheral branch arteries. Aortas were mounted in a custom-built perfusion system, lined with latex to prevent leakage, and subjected to pulsatile loading at 0.5 Hz and systolic/diastolic pressures of approximately 130/90

mmHg. No appreciable pre-stretch was applied beyond making sure the vessel was axially taut. The experimental system is shown in Fig. 1.

Fig. 1: A custom perfusion system to induce pulsatile loading.

ADMSCs were isolated from human adipose tissue and provided to us by Dr. J. Peter Rubin, Department of Plastic Surgery, UPMC, using an established protocol [8]. Cells were then cultured in ADMSC growth media (10% fetal bovine serum, 1% penicillin-streptomycin, 1% fungizone, and 88% Dulbecco’s Modified Eagle Medium and Nutrient Mixture F-12, ThermoFisher Scientific) at 37ºC and 5% CO2. ADMSCs were passed once and cultured to 80-90% confluence before experimental use. These cells were also modified in a proprietary manner to allow for directional delivery, that is, attraction of cells towards the aortic surface. 1 hour before usage, ADMSCs were incubated with CellTrackerTM Red CMTPX Dye (ThermoFisher Scientific) to allow for detection after delivery. For delivery, ADMSCs were suspended in culture media and mixed with fibrin gel precursors to produce a gel with 3.3 mg/mL fibrin and 1.1 ± 0.5 x 106 cells/mL. Three gels were prepared, and all gel parameters were kept constant between the experimental (directional) and control (nondirectional) groups. To ensure retention of the fibrin gel, a custom fixture was used to expose a 1.25 cm square section of the adventitia, as shown in Fig. 2.

Moreover, ADMSCs appeared to populate the adventitial surface in the directional delivery group, suggesting improved recruitment of ADMSCs to the tissue itself.

Fig. 2: A fixture to isolate a region of interest on the adventitia.

3mL of the fibrin gel was injected onto the region of interest under pulsatile loading and left for 15-20 minutes. Aortas with cell-loaded fibrin gel were incubated at 37ยบC for 30-45 minutes for gel curing and fixed overnight in 0.37 wt% formaldehyde. A cross-section of the region of interest was harvested, cryosectioned, and mounted on slides for fluorescence microscopy. Images were acquired using a Nikon Eclipse 90i fluorescent microscope with NIS Elements software and processed using ImageJ software. RESULTS The fibrin gel surrounding the aorta appeared to be much more densely populated with ADMSCs in the induced directional delivery group (Fig. 3).

DISCUSSION Here we have demonstrated an effective method for localizing ADMSCs to large animal aortas under physiologic conditions (i.e. aortic pulsation). This proof-of-concept suggests that this method can be used to improve localization of cell-based vascular therapies in other large animals, including humans. Improved localized cell delivery is beneficial for numerous reasons. Firstly, our group has shown that periadventitial ADMSC delivery can slow degradation of elastin and halt expansion of AAAs, effectively halting the progression of this disease [6]. Hence, improved ADMSC density in the fibrin gel surrounding the aorta could greatly improve the effectiveness of ADMSC treatment. Secondly, ADMSCs have the ability to differentiate into smooth muscle cells (SMCs), potentially allowing them to replace damaged SMCs in the diseased aorta, although this effect requires further investigation. Therefore, our method has significant potential for improving both the specificity and effectiveness of therapeutic, cell-based treatments for AAAs. Future work will involve in vivo studies in large animals and investigate strategies for deployment of this method in vivo. REFERENCES [1] Patel et al. J Am Coll Surg 181 371-82, 1995 [2] Pearce et al. Circulation 118 2860-63, 2008 [3] Ashton et al. Lancet 360 1531-39, 2002 [4] Chosky et al. Ann R Coll Surg Engl 81 27-31, 1999 [5] Greenhalgh et al. Eur J Vasc Endovasc 16.6 462, 1998 [6] Blose et al. Regen Med 9.6 733-41, 2014 [7] Krawiec et al. Tissue Eng Part A 21.3-4 426-37, 2015

Fig. 3: ADMSCs (red) appear to be more localized around the aorta (green) in the presence of a directional delivery than in its absence. The aortic adventitia is denoted by the white arrows.

ACKNOWLEDGEMENTS Funding for this project was provided by the Swanson School of Engineering and the Office of the Provost (to TMK) and the National Institutes of Health (R21 HL129066 to DAV). We thank Dr. Rubin and his team for the ADMSCs used in this study.

COMPARISON OF CELL SEEDING QUALITY OF POROUS, BIOMIMETIC, TUBULAR SCAFFOLDS FOR VASCAULAR TISSUE ENGINEERING FABRICATED BY TWO DIFFERENT METHODS Meara Sedlak1, Katherine Lorentz1, Samuel Luketich1,7, Sang-Ho Ye5,7, Antonio D’Amore1,5,7, Justin Weinbaum1,4,7, William Wagner1,5,6,7, David Vorp1,2,3,5,6,7 Departments of Bioengineering1, Chemical and Petroleum Engineering2, Cardiothoracic Surgery3, Pathology4, and Surgery5, Center for Vascular Remodeling and Regeneration6 and the McGowan Institute for Regenerative Medicine7, University of Pittsburgh, PA, USA Email: mms201@pitt.edu, Web: https://www.engineering.pitt.edu/vorplab/

INTRODUCTION In 2013, cardiovascular disease was the cause of 31.5% of global deaths and is expected to be diagnosed in 43.9% of the United States population by 2030 [1]. Most diagnoses require surgical intervention in the form of revascularization, most commonly using an autologous vessel to serve as an arterial conduit bypassing the blockage. The frequently used saphenous vein, which represents the current gold standard, is not a long-term solution due to differences in vein and arterial structure that can lead to graft stenosis and hyperplasia over time [2]. Tissue engineered vascular grafts (TEVGs) are a promising graft alternative offering the potential for growth and repair in vivo while eliminating the need for a secondary surgery. The Vorp Lab has been working towards a functional TEVG by combining the Wagner Lab’s biocompatible, polymeric poly(ester urethane) urea (PEUU) scaffolds with adipose derived stem cells (ADSCs) [2,3]. Previously, there was success with a scaffold fabricated using thermal induced phase separation (TIPS) for the inner layer and electrospinning (ES) for the outer layer; however, porosity and homogeneity of the scaffold has varied from batch to batch. New techniques for the inner layer combine TIPS with salt leaching (SL) which has been shown to increase homogeneity in seeding and increase in pore size. The addition of SL produces scaffolds in a more consistent and reproducible manner [4]. The aim of this project is to seed cells into both TIPS and SL-TIPS scaffold types and compare the two scaffolds’ ability to support repeatably consistent seeding. METHODS Scaffold Fabrication: The study consisted of two types of scaffolds. The first was a bilayered PEUU scaffold fabricated using TIPS to form a porous inner

layer and ES to create a stable outer layer. To fabricate the scaffold, PEUU was injected into a cylindrical mold and rapidly cooled to -80°C for three hours. The mold was then soaked in ethanol for 7 days at 4°C after which it underwent freeze drying. This process created the porous TIPS layer. A coating of circumferentially aligned PEUU fibers was then applied to the inner TIPS layer by electrospinning the polymer while rotating the scaffold. The second scaffold combined TIPS and SL to create a porous inner layer with an ES outer layer. The SL+TIPS scaffold followed a similar production but includes a salt solution combined with the PEUU prior to injection into the molds. The salt solution precipitates when soaked in ethanol. The inclusion of salt crystals into the polymer creates larger, more consistent pores dictated by the size of the salt particles. Seeding: Both graft configurations were mounted on our custom rotational vacuum seeding device for cell seeding. Media consisting of 106 cells/mL, as previously optimized by the Vorp Lab [3], was injected at a steady rate into the device and through the lumen of the graft. The TIPS configuration used patient-derived ADSCs while the SL+TIPS configuration used commercial ADSCs (RoosterBio, Frederick, MD). During seeding, luminal pressure was tracked using two pressure transducers placed equidistant on the inlet and outlet of the seeding chamber. An average of the two pressures was used to represent the luminal pressure. Constructs were then fixed using a 4% paraformaldehyde solution for 1 hour, then soaked in sucrose for 1 hour and frozen into Optimal Cutting Temperature compound (ThermoFisher, Waltham, MA). Ten-micron sections were prepared on glass slides and stained using Hoechst 33342 Solution (ThermoFisher, Waltham, MA) to analyze seeding density and

homogeneity. The sections were imaged using NIS Elements (Nikon, Minato, Japan). DAPI

DAPI

Fig. 1. A representative quadrant of the (A) whole scaffold image was selected (B) and analyzed in ImageJ where (C) DAPI staining was quantified using thresholding.

Image Analysis: Each section image was divided into four quadrants for quantification. Due to variation in size and seeding of scaffolds, one representative quadrant was selected as a region of interest and the cells within that region were quantified using a custom macro in ImageJ (Fig. 1). The same quadrant was thresholded and the area was found using a measure function. The configurations were then compared on the basis of luminal pressure and seeding density. RESULTS 1300

SL+TIPS TIPS

800 300 -200 0

Fig. 2 Representative pressure seeding data of TIPS (orange) and SL+TIPS (blue).

The TIPS scaffold showed 101.3 ± 112.6 cells (n=9) in the region of interest. The SL+TIPS scaffold showed 127.4 ± 32.5 (n=5) cells in the region of interest. The SL+TIPS scaffold maintained 18.3x103 ± 9.8x103 cells/cm2 while the TIPS scaffolds had a density averaging 13.6x104 ± 13.4x104 cells/cm2, but these values do not reach significance (p=0.08). As shown in Fig. 2, the SL+TIPS scaffold maintained a much more consistent and low luminal pressure during seeding, averaging -27 mmHg. Meanwhile, seeding pressures for the TIPS scaffold averaged 414 mmHg.

DISCUSSION Analysis of the representative region of interest presented substantial variance in seeding densities for each TIPS scaffold resulting in a high standard deviation. Qualitative analysis of whole scaffold images revealed inconsistencies in cellular dispersion between regions of interest of the same scaffold. In comparison, the SL+TIPS scaffold showed a more homogeneous and dense cellular dispersion. Qualitatively, the TIPS scaffold showed far less consistency between sections when compared to the SL+TIPS scaffold. Therefore, with a higher cell count and more consistent density, the SL+TIPS configuration should be more translatable. Pressures exceeding 150 mmHg have been shown to induce changes in cell morphology, thus indicating the pressures in the TIPS scaffold (414 mmHg) were potentially harmful to the cell and its properties while the SL+TIPS configuration remained within a safe luminal pressure (-27 mmHg). In terms of pressure, the SL+TIPS configuration similarly displayed consistency and translatability. This study was effective in determining the best configuration for a translatable scaffold to be used in the study of TEVGs. Investigation into scaffold configurations is essential to the scale up and overall consistency of TEVGs. REFERENCES [1] Benjamin, E. J., et al. “Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association.” Circulation. Web. 2017. [2] Krawiec, J. T., et al. “Adult stem cell-based tissue engineered blood vessels: A review.” Biomaterials 33(12): 3388-3400. 2012. [3] Soletti, Lorenzo et al. “A Seeding Device for Tissue Engineered Tubular Structures.” Biomaterials. 27(28): 4863–4870. 2006. [4] Cho, Y. S., et al. “A novel technique for scaffold fabrication: SLUP (salt leaching using powder).” Current Applied Physics. 14(3): 371-377. 2014. ACKNOWLEDGEMENTS We would like to acknowledge funding from the National Institutes of Health (R01HL130077 to DAV), Dr. David Vorp, the Swanson School of Engineering, the Department of Bioengineering and the Office of the Provost.

EFFECT OF PIVOT-BEARING SURFACE ROUGHNESS ON THROMBUS FORMATION Katherine Stevensona,c, Alexandra Maya,d, Ryan Orizondoa,c, Sang-Ho Yeb,c, Brian Frankowskia, William R. Wagnerb,c,d, and William J. Federspiela,c,d a b Medical Devices Lab, Cardiovascular Engineering Lab, cDepartment of Bioengineering, dDepartment of Chemical Engineering McGowan Institute for Regenerative Medicine, University of Pittsburgh, PA, USA Email: kls226@pitt.edu, Web: http://www.mcgowan.pitt.edu/medicaldevices INTRODUCTION Patients with end stage chronic lung disease or acute respiratory distress syndrome (ARDS) are commonly treated with extracorporeal membrane oxygenation (ECMO) [1]. However, only 56% of the 98,000 ECMO patients since 1990 survived to discharge or transfer [2]. A primary shortcoming of ECMO is that patients are typically bedridden in the ICU. This leads to muscle degeneration and poorer patient outcomes [3]. We are developing the Paracorporeal Ambulatory Assist Lung (PAAL) as a compact, wearable pump-lung intended to provide ambulatory respiratory support to lung failure patients as a bridge to transplant or recovery [1]. The PAAL is currently being evaluated in 30-day animal studies. One device related challenge is the development of thrombus at the bottom pivot of the impeller (Figure 1). This causes increased blood damage as well as detrimental increases in motor torque.

Figure 1: Pivot (red) in device impeller. Thrombus often forms underneath impeller around pivot.

Multiple pathways are being investigated to eliminate the thrombus formation. This in vitro work evaluates the effect of pivot material surface roughness on thrombus formation at the bottom pivot of the PAAL. The effect of surface roughness on platelet adhesion has been well established in literature to show that increased surface roughness correlates with an increase in platelet adhesion and protein adsorption [4]. The developed testing procedure replicates the thrombus formation observed during long-term in vivo studies in an accelerated period. This in vitro protocol will provide a quick and affordable means of evaluating various design modifications intended to reduce intra-device thrombus formation.

METHODS The PAAL device consists of an integrated centrifugal pump which pumps blood radially through a blood channel and then through the gas exchanging fiber bundle. For these studies, the resistance of the fiber bundle was replicated by a 1/8” flow path in place of the bundle. Sixteen feet of 3/16’’ tubing was used to replicate the resistance of the intended use 15.5 Fr dual lumen cannula. Two impellers were tested with a repetition of four, one with a ceramic pivot (CPI) and another with a zirconium pivot (ZPI). An Alpha-Step IQ Surface Profiler was used to measure pivot surface roughness. Two loops (one with a ZPI impeller, and one with a CPI impeller) were run simultaneously. Each loop contained 750 ml of ACD sheep blood (Lampire Biological Lab). Heparin (3.5 U/mL), CaCl2 (3 g/L), L-glutamine (0.292 g/L) and gentamicin (2.5 mL/L) were added to the blood. Blood flow rate was set to 0.5 L/min and driven by the integrated pump (1070±22.7 RPM). Flow rate was recorded every 30 min while motor torque was monitored at 2 Hz by a DAQ Device (NI USB - 6002) connected to a computer. Blood samples were taken every 1.5 hr. for Hb (Siemens RAPIDPoint 405 Blood Gas System), plasma free hemoglobin (pfHb), and ACT (Medtronic ACT II) measurements. Digital photographs were taken of thrombi to document size and location on the impeller. Thrombi were removed from the impeller and dried for 24 hours before weighing. Platelet activation was measured in 3 experiments. Blood samples (3 ml) were taken from each loop and immediately placed in a citrate tube (S-Monovette 3 ml 9NC) every 30 min for the first 3 hours of the experiment. The samples were analyzed by the Cardiovascular Engineering Lab using flow cytometry to measure the percentage of activated platelets by the amount of CD62P present, using PAF as an agonist.

DATA PROCESSING Torque data collected using the DAQ Device was processed using MATLAB to find the average torque/hour for each loop. Digital photographs of thrombi were processed using ImageJ and MATLAB to find surface area, radial location on impeller, and to create heat maps. Statistical comparisons for size, weight, platelet activation, and pfHb increase were made using the paired two sample t-test. RESULTS The surface roughness of the ceramic pivot was 68.8 times rougher than the zirconium pivot (n = 9). Torque did not increase by more than 0.1 mNm, which is within typical variation under normal operation. The thrombi surface area on the bottom CPI (1.32±0.7 cm2) was significantly greater than that of the ZPI (0.35±0.4 cm2) (p<0.05, n=4). The surface area as well as the radial location of the thrombus on the impeller is shown in heat maps (Figure 2 & 3). The CPI thrombus weight (8.29±6.9 mg) was also significantly greater than that of the ZPI (2.78±2.1 mg) (p<0.06, n=4). The average pfHb increase in the first 6 hours for the CPIs (70.0±15.3 mg/dl) vs. the ZPIs (19.4±8.0 mg/dl) showed significantly greater hemolysis in the CPI device (p < 0.05, n = 4). There was no significant difference in platelet activation between the ZPI and CPI (p > 0.06, n = 3).

DISCUSSION Thrombus formation within blood pumps has a detrimental effect on device performance. An invitro testbed was developed to predict in-vivo thrombus formation. Specifically, this study evaluated the effect of pivot material surface roughness. ZPIs had less hemolysis and smaller thrombus formation compared to CPIs. This demonstrates that the smoother surface of the zirconium pivot reduces thrombosis. No significant difference in platelet activation was expected since the shear rates within the two devices were the same. In addition, the literature reports no correlation between surface roughness and platelet activation [4]. One limitation is that increased motor torque seen in in-vivo studies was not replicated in these in-vitro studies due to thrombus size. This is likely due to the accelerated time of these studies in comparison to 30-day studies. Reduced anticoagulation is necessary for thrombus to form at the impeller in a 6-10 hour period, but as a result thrombus also forms in the blood reservoir, reducing the blood flow and forcing an end to the study before a significant increase in motor torque occurs. However, the demonstrated reduction in thrombus formation in the ZPI device is sufficient to suggest that a change to the pivot material will help alleviate thrombus-related problems. Based on these results, future PAAL in-vivo studies will utilize zirconium pivots. Future work will evaluate the effect of other design modifications, such as impeller washout holes, on thrombus formation. These designs could have an impact on shear rate in the device, therefore platelet activation will also be investigated for these future studies.

Figure 2: Representative composition and thrombus size on zirconium and ceramic pivot impellers.

Figure 3: Heat maps of the combined data from all 4 experiments, showing the average radial size and location of thrombi on the bottom of the circular impeller.

REFERENCES 1. Madhani et al. J Heart Lung Transplant 36.7, 806-811, 2017. 2. “ECLS Registry Report” 2018. ELSO 3. Maury et al. AM J Transplant, 8, 1275-1281, 2008. 4. Linneweber et al. Artif Organs 31, 345-351, 2017. ACKNOWLEDGEMENTS Supported by NIH grant RO1HL117637 and R01HL135482, the University of Pittsburgh Office of the Provost, and the Swanson School of Engineering

Transitioning to a Democratic Organizational Structure: An Ethnography of a Cooperative Co-working Space Jacquelyn N. Barbush and Trevor Young-Hyman University of Pittsburgh, PA, USA Email: jnb42@pitt.edu INTRODUCTION Democratic organizations, defined as organizations where decision-making authority is equally distributed among internal stakeholders, remain relatively rare but are receiving interest from researchers, the private sector, and the public sector (Lee and Edmondson 2017) This interest in democratic organizations stems from two sources: those concerned with generating innovation and creativity, and those concerned about inequality of power and wealth in society. While there has been more research on the operations of democratic organizations, scholars know relatively little about the process of establishing democratic organizations. This is a particularly salient question because democratic organizations remain relatively uncommon, such that those establishing them have few frames of reference or prior experiences on which to draw as they develop these organizations. In particular, we know relatively little about the processes with which organizational members establish decision-making rules, teach new members how to operate in a democratic setting, learn to manage conflict, and communicate their organizational structure to outside stakeholders. Therefore, in this project we ask, what factors shape the particular decision-making processes that emerge in an attempt to adopt democratic organization. METHODS A qualitative longitudinal case study was fitting for this research agenda because we are interested in comprehending a relatively understudied phenomenon, developing theory, and observing how people develop an understanding of a situation over time (Yin 1984). With this method, we are able to develop a theory that can be tested in later deductive quantitative research. As a setting, we selected a co-working space and business incubator for entrepreneurs and free-lance workers that has recently decided to adopt a

cooperative organizational structure. This organization, which we identify by the pseudonym Coop Space to protect the organizationâ&#x20AC;&#x2122;s confidentiality, offered us the opportunity to collect qualitative data during its transition into workplace democracy. Coop Space presents a useful case because we were able to begin data collection during the early stage of organizational change and track the change over time. It is also valuable because it is a technology-oriented organization that also integrates inequality concerns into its mission, thus serving as relevant to both categories of entities interested in democratic organization. Adopting ethnographic data collection methods (Emerson et al. 2011), I immersed myself in the environment, acting as an intern for this organization. This perspective gave me exceedingly unique and valuable access to the inner workings of Coop Space. Over three months, I spent 3-5 days a week participating in the daily operations, attending regular committee meetings, interacting with other members, completing tasks for the organization, and observing how the co-op operates from the inside out. In addition to active participation, I regularly wrote up detailed field notes, recorded and transcribed interviews with individual members throughout the organization, and attended and recorded recurring committee meetings. I created surveys that members completed and generated data from them. I downloaded archival documents such as their Standard Operating Agreement, business plan, and other miscellaneous documents available to the members. Finally, I extracted conversations from the companyâ&#x20AC;&#x2122;s online chat platform where the majority of cross communication occurs. Access to this online platform allowed us to go back in time and collect data from the earliest stages of this company, even before the beginning of the transition to a democratic organization. DATA PROCESSING I used a grounded theory approach (Charmaz 2000) to analyze our data and develop theory. With a

grounded theory approach, the researcher begins with open coding of the data to look for categories that answer the research question, then proceeds to aggregate those codes into higher level theoretical categories. This coding begins while data collection is ongoing and the researcher seeks out alternative perspectives and sources of data that may invalidate the emerging theory. I also wrote memos around the emerging themes to develop narratives about their relationships. While data collection and analysis is still ongoing, the following are preliminary results from our analysis. RESULTS Our data suggest a model in which organizations seeking to adopt a democratic structure begin from a position of limited democracy, but may be pushed towards greater democratic decision-making by several sets of factors. They begin from a position of weakness because, first, workers are inclined to avoid voluntary participation, especially that which offers little direct incentive. Not all members see a clear benefit of participating or assume they have little to offer. Second, the existence of a founder or founding team limits democratic decision-making. Because, at least initially, the founders are the main source of information on rules, policies, and operational practices, this undermines efforts to distributed authority more broadly. From this point of departure, we focus on two dimensions of organizational democracy that are shaped as the structure emerges: the depth of democratic participation and the scope of participation. Our research suggests several key elements that shape the depth of democratic engagement. First, the lack of familiarity with organizational democracy leads to the reliance on experts and expert sources of information. Thus, the particular vision of democratic engagement held by the expert holds disproportionate influence. A second driver of the depth of participation is the degree to which formalized rules, policies, and routines are established to reduce founder reliance and provide the members with a cognitive framework for involvement. A separate set of factors shapes the scope of participation; i.e. who participates. Our data suggests

that members who gain more from participating more easily rationalize participation. Second, the informal social network shapes the scope of participation. Members with strong social ties to the founder are more likely to participate in democratic governance. Finally, an element of path dependency exists as another corollary element. Workers who have encouraging first experiences in democratic decision-making find themselves coming back for more, while the contrary are deterred and participation of those decreases. DISCUSSION An important caveat is that a single case cannot generate causal claims, but can highlight a set of potential causal relationships to be tested in future research. To date, we have identified a process model of democratic emergence and a set of factors that shape this process. In different settings, these factors will take on different degrees of importance. This is an ongoing study, as we will continue to collect data and revise our model over the coming four months. Having developed a preliminary model, our task is now to further decompose factors we have identified, seek out disconfirming evidence, and build additional support around this model. REFERENCES 1. Charmaz, Kathy. 2014. Constructing Grounded Theory. Thousand Oaks, Calif: SAGE Publications Ltd. 2. Emerson, Robert M., Rachel I. Fretz, and Linda L. Shaw. 2011. Writing Ethnographic Fieldnotes, Chicago: U. Chicago Press. 3. Lee, Michael Y., and Amy C. Edmondson. 2017. â&#x20AC;&#x153;Self-Managing Organizations: Exploring the Limits of Less-Hierarchical Organizing.â&#x20AC;? Research in Organizational Behavior 4. Yin, Robert. 2013. Case Study Research: Design and Methods. Fifth Edition edition. Los Angeles: SAGE Publications, Inc. ACKNOWLEDGEMENTS Funding provided by Swanson School of Engineering and the Office of the Provost. The author thanks the workers at Coop Space, who generously permitted access to the field site.

LOCALLY INDUCED SEMICONDUCTOR-TO-METAL TRANSITION IN TWODIMENSIONAL CRYSTALS USING AN IONOMER Aaron Woeppel, Susan Fullerton Nanoelectronics and Ionics Laboratory, Department of Chemical and Petroleum Engineering University of Pittsburgh, PA, USA Email: abw35@pitt.edu, Web: http://fullertonlab.pitt.edu/ INTRODUCTION Two-dimensional (2D) crystals are a single atom or molecule thick and are more flexible then their bulk counterparts [1]. Some can exist in distinct phases with unique structures and properties. For example, the 2H phase of MoTe2 is semiconducting; while, the 1T phase is metallic. MoTe2 can be switched between the 2H and 1T phases by introducing and relieving strain (~3%) [2]. Current methods to strain the crystal (1) apply strain globally over the entire sample and (2) do not provide sufficient strain to induce the transition [3]. Our group is taking a new approach to this problem using an ionomer gate to electrostatically strain suspended MoTe2 Field Effect Transistors (FETs). An ionomer is a polymer with cations that are free, and anions that are covalently bound to the polymer. Therefore, when a voltage is applied, the cations respond to the electric field creating an Electric Double Layer (EDL) at the ionomer/crystal interface, while the anions are locked in place. An electrostatic imbalance is created that bends the ionomer and strains the underlying 2D crystal. This ion-induced EDL is shown in a simple parallel-plate capacitor geometry in Fig. 1a. Most research on ionomers focuses on ionic polymer metal composites (IPMCs) for artificial muscle [4]. IPMCs consist of an ionomer sandwiched between two flexible metal electrodes similar to Fig. 1(a), but where the structure bends in response to an applied field. MODEL DEVELOPMENT I used Finite Element Modeling via COMSOL Multiphysics© to model ion transport through an ion conductor. For simplicity, the parallel-plate geometry in Fig. 1a was considered. The ionomer was modeled as a solid dielectric separating two electrodes, defined as infinitely thin boundaries. One

electrode was grounded (assigned electric potential equal to zero), while the other had a manually applied voltage. In one case, both ions were allowed to freely respond to the electric field, while the anions were fixed in the ionomer case. COMSOL’s Transport of Diluted Species and Electrostatics modules were used to model the dynamic ion drift and electric field, respectively. The modules were coupled using the Nernst-PlankPoisson relationship. (a)

(b)

Fig 1 (a): Schematic of capacitor geometry modeled in COMSOL. Cations respond to the electric field creating an EDL while anions are held in place. (b) Schematic of structure to check for ion mobility. Ti/Au electrodes are evaporated on SiO2/Si. A custom-synthesized ionomer is dropcast and annealed. A potential is applied between the electrodes, driving drift ion diffusion. INCOPROATING A STERN LAYER At the ion concentration used for this study (0.617 M), cation accumulation and depletion will occur within a few nanometers of the electrode surface. On this length scale, the finite volume of ions must be considered; however, COMSOL’s default is to assume the ions as point charges. This assumption gives unrealistically high sheet densities,

(a)

(b)

(c)

Fig 2: (a) Cation sheet density and voltage profiles across ionomer where ion volume is accounted for by addition of a Stern Layer that is ionic radius wide (0.75 Å). (b) Zoomed view of data in (a) The Stern Layer is shown by the grey box. (c) Dynamics of EDL formation in response to a 3 V bias as monitored by sheet carrier density in simulation (red) and by current in experiment (blue). and, as a result, the finite volume of each ion must be manually accounted for. The ionomer was divided into three regions: the bulk region consisting of the majority of the ionomer, and two Stern Layers – one at each electrode surface – that are one ionic radius thick. There is no ion flux inside the Stern Layers.

used to capture the experimental data at short and long times, respectively. The model cannot fit the data because it cannot capture the ions individual variation of response time. This inhomogeneity accounts for the gradual response in the experimental data compared to the steep response in the model.

While the Stern Layers prevent ions from accumulating at unphysically close distances to the electrode, the unphysically large sheet densities were addressed by redesigning the meshing. Specifically, ion concentrations and voltages are calculated at each node in the mesh, and we set this node-to-node distance to be equivalent to one ionic diameter (1.5 Å) (Fig 2b).

IMPLEMENTING DEFORMATION The next step is to account for the deformation expected due to the electrostatic imbalance. I developed a straightforward Coulombic description and implemented it in COMSOL. Elongation results from the pairwise repulsion between adjacent ions. I used the Solid Mechanics module where the calculated repulsive force was applied as a 2D boundary directly adjacent to each Stern Layer. We observed nonzero deformation suggesting that while our model may not be qualitatively accurate yet, we are able to properly couple the added Solid mechanics model to the ion transport.

With these two modifications, the shape of the profile in Fig. 1(a) remains similar to the initial model; however, the sheet densities are now physically reasonable and in agreement with previously published literature (~4 x 1014) [5]. COMPARISON TO EXPERIMENT A custom ionomer was synthesized by the Beckman group, and the mobility was experimentally tested using the structure shown in Fig. 1b. The ionomer was drop-cast from solvent and annealed Ti/Au electrodes on SiO2/Si (Fig 1b). The ionomer is electrically insulating but ionically conductive, as verified by applying a 3 V bias and monitoring current between the electrodes. At steady state (i.e., full EDL formation), the ions remain stationary and near zero current indicates that the ionomer is electrically insulating. The current response is plotted alongside the modeled sheet density (Fig 2c). A cationic diffusion coefficient of 10-12 cm2/s was

REFERENCES 1. Bertolazzi, S., et al. ACS Nano 2011, 5, 9703 2. Duerloo, K. A., et al. Nat. Comm. 2014, 5, 1 3.Wang, Y. L., et al. Nano Research 2015, 8, 2562 4. Oguro, K., et. al. Micromachine Soc., 1992, 36, 421 5. Efetov, D., Kim, P. Phys. Rev. Lett., 2010, 105, 256805 ACKNOWLEDGEMENTS The authors thank the Swanson School of Engineering, the Office of the Provost, and Covestro for funding; Dr. Ke Xu, and Dr. Hangjun Ding for help with the electrical measurements and ionomer synthesis, respectively.

EVALUATION OF THE ACCURACY OF DUH-HAYMET-HENDERSON THEORY Zheng Guo Johnsonâ&#x20AC;&#x2122;s Research Group, Department of Chemical engineering University of Pittsburgh, PA, USA Email: zhg18@pitt.edu, Web: http://jkj.che.pitt.edu/ INTRODUCTION Most integral equation theories used to calculate the properties of Lennard-Jones fluid have the disadvantage of either computation inefficiency or a less of accuracy. They are based upon the relationship of the correlation functions, including the pair correlation function, g(r), cavity correlation function, y(r), and the direct correlation function, c(r). This paper investigates the accuracy of DuhHaymet-Henderson (DHH) integral equation theory and explores a way of improving. The results are compared with molecular dynamics (MD) simulation, first order Percus-Yevick theory and Hyper-Netted Chain (HNC) theory. Like other theories, DHH theory connects the pair correlation function and direct correlation function by the Ornstein-Zernike (OZ) equation and an additional relation called closure, which usually defined as the bridge function, B(r). Different from other theories, DHH suggests a semi-empirical closure instead of a simple functional between c(r) and y(r) [1], which is described as following , (1) , ,

for

(2)

(5) ,

(7) There are two parts of the experiments reported in this paper. First is the comparison of the simulation results among DHH, HNC and PY theories with MD simulations at T*=2, rho*=0.1 to 0.8 for pure liquid. The second part sketches the analysis of y(r) for Ar/Kr mixtures between MD simulation and integral equation theories. The third part represents the methods of attempting to improve the original DHH theory by modifying equation 6 to the equation 8: , (8) where .

(9)

DATA PROCESSING The raw MD simulation data contains four columns results of g(r) including mixture 11, 12, 21 and 22, while the integral equation theories only have three columns which are 11, 12, 22. Therefore, MD simulation data is modified by an additional script to take the average of mixture 12 and 21.

(3) , (4)

values between two species. The outputs provide us the data of pair correlation function, cavity correlation function, direct correlation function, bridge function, z value and u value, which are defined as:

(6)

METHODS The study focuses on the computer simulation of the pure fluid or fluid mixtures above critical temperature and densities. The DHH theoryâ&#x20AC;&#x2122;s code requires inputs of reduced temperature, reduced density, mole fractions, relative sigma and epsilon

Furthermore, the raw MD simulation data only has a y(r) in a short range. To solve that, the g(r) data achieved from the above process is applied to calculate the y(r) after the given range with the following equation: . (10)

Instead of just comparing the results visually from the plots, another code is developed to calculate the absolute relative mean error between MD simulation and integral equation theories using the following equation: . (11) RESULTS According to the results, DHH works much better than the other two theories for almost all of the densities. Table 1 records the absolute relative mean error between MD simulation and integral equation theories at T*=2. Table 1: Absolute relative mean error of y(r) for DHH, PY and HNC compared to MD simulation rho* HNC PY DHH 0.1 0.00942 0.01869 0.01557 0.2 0.04659 0.057131 0.04229 0.3 0.1386 0.10412 0.06771 0.4 0.354 0.15905 0.09009 0.5 0.90635 0.22415 0.1127 0.6 2.6883 0.30044 0.1359 0.7 11.65 0.3787 0.1601 0.8 90.44 0.4584 0.1716 Figure 1 represents the difference between the results of MD simulation and integral equation theories visually.

Figure 2: asinh(y11(r)) vs r of Ar/Kr mixtures for MD, DHH, and PY at T*=1.2279, rho*=0.6237, xAr=0.485

The improvement of DHH is not as effective as expectation. By setting a=1.3 and b=1, it is possible to reduce the error of y(r). However, the side effect of such modification will cause a loss of accuracy in the calculation of z and u values defined in equation 7. CONCLUSION AND DISCUSSION DHH provides a more accurate result for cavity correlation functions than PY and HNC theories do compared to MD simulations. However, the way of improving DHH theory reported in this paper needs to be adjusted in order not to reduce the accuracy of any original result. REFERENCES 1. Johnson et al. Mol. Phys 115, 1335-1342, 2017. 2. Moosavi et al. Fluid Phase Equilibria, 274, 5158, 2008.

Figure 1: asinh(y11(r)) vs r of pure fluid for MD, DHH, PY and HNC at T*=2, rho*=0.8

Similar patterns also apply to Ar/Kr mixtures. The parameters are set as sigmaAr=3.405, sigmaKr=3.67, epsilonAr=119.8, epsilonKr=167, T*=1.2279, * rho =0.6237, xAr=0.485 according to the reference

ACKNOWLEDGEMENTS The simulations were done on h2p.crc.pitt.edu. The research work was conducted in the University of Pittsburgh Chemical Engineering Department. Thanks for the funding provided by University of Pittsburghâ&#x20AC;&#x2122;s SSOE Summer Research Program. Thanks for the help provided by Professor Karl Johnson. [2]. The comparison of the results among PY, DHH and MD is shown in Figure 2.

Delamination of soft thin films from dynamic wrinkling substrates Joe Hamm Department of Chemical Engineering University of Pittsburgh, PA, USA Email: jrh188@pitt.edu Introduction Fouling is a costly, ubiquitous industrial problem. Medical devices, marine vessels, filtration membranes and heat exchangers are several items which are severely impacted by the development of fouling; bacterial biofilms, particulates and insoluble salts are typically responsible, but vary depending on which industry. Topographical deformation has previously been exploited within the Velankar lab to inhibit fouling and create surface renewing materials. Dynamic wrinkling surfaces have been constructed by developing an elastic moduli gradient and imposing mechanical strain. Dynamic surface topography places adhered material under boundary conditions as the surface begins to undulate. The bonded foulant will attempt to follow changing surface curvature. As the foulant contorts to the changing surface, elastic stress builds within the foulant layer which ultimately delaminates after reaching a threshold level, where delamination becomes energetically favorable opposed to remaining bonded to the wrinkled surface1. The goal of the project was to validate a simulated relationship prediction between critical strain (εc), the strain required to initiate foulant delamination, and the length scale ratio between the foulant thickness and the wrinkle wavelength (h/λ). While wavelength is fixed, the critical strain develops an independence from the foulant thickness when it becomes sufficiently larger than the wavelength (h/λ>1), known as the thick patch limit. If the wavelength is larger than the thickness (h/λ<1) however, the thin patch limit, the foulant is much more capable of contorting to deformations of the surface, which incites a steep inverse relationship between the critical strain and the thickness of the foulant1. Data has currently only been gathered for a single experiment opposed to a variable range needed for comparison to the simulated results.

Methods Dynamic wrinkling structures were produced by a material modulus mismatch. Nitrile rubber served as a soft elastic base whereas cyanoacrylate, commonly referred as superglue, coated the surface as a stiff skin. Prior to the application of the thin film, the rubber was uniaxially prestretched to approximately 40% strain. The bilayer system was then created by manually spreading the glue and

allowing it to cure. Relief of the mechanical tension allowed the rubber substrate to relax, while placing the thin film under compression. Since the coating of glue is incapable of elastic compression relative to the rubber due to the moduli mismatch, the bilayer responds by undulating at the surface. The entire construction process can be visually exemplified by Figure 1, while Figure 2 depicts the wrinkling sample constructed for the quantified experiment.

Figure 1. a) Mechanical pre-stretch was applied to a rubber strip and held. b) While stretched, a thin film of super glue was applied to the rubber super. c) After the glue cured, the initial pre-stretch was relieved, placing the thin layer of glue under compression as the rubber contracts.

Figure 2. View of a wrinkled rubber-superglue bilayer used for delamination experiments with a ruler to the left with millimeter increments.

Thin GI-245 silicone rubber patches (polydimethylsiloxane, PDMS) were selected as mock foulants during experiments. When patches were placed on the bilayer, the relatively smooth super glue surface and mechanically soft silicone allowed for an adhesive bond between the materials with minimal defects and blisters. Camcorders captured videos of the delamination process, which were used to evaluate substrate strain and identify initiation of the mock foulant debonding.

Data Processing Experimental recordings were analyzed by motion tracking software, Blender, by tracing the movement of the contrasting gel-marker blotches. Tracking data was cataloged by storing the X-Y pixel coordinates of every frame number for each marker. Displacement and strains were calculated using the pixel-based data. The temporal local strain was found by finding horizontal pixel distance between given markers at each frame number and essentially using the following equation.

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|(đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ) â&#x2C6;&#x2019; (đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ)| (đ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??źđ??ź đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ)

The moment of delamination, interfacial separation between the wrinkling substrate and the bonded silicone patch, was clearly realizable due the transition from transparency to translucency as air filled the interfacial void.

Surface wrinkling was directly proportional to the thickness of the stiff film, therefore, the non-uniform layer of the superglue produced regions of wrinkes with variable wavelengths. These variations changed the delamination regime which occurred, meaning some areas of the silicone patch may have exhibited the thick patch limit, whereas other sections may have been within the thin patch regime. Figure 4 shows a time lapse of the delamination of the silicone patch from the wrinkling surface. As seen, the patch experiences edge delamination induced by the build up of bending stress in the mock foulant as it attempted to conformation to the surface curvature. The patch continues to delaminate as the strain was increased. In conclusion, no validation can necessarily be made, aside that the mock foulant was effectively debonded due to the wrinkling surface. 1

Results and Discussion The compressive strain of the wrinkling bilayer over time was constructed and fit to a linear relation as seen in Figure 3. Initiation of delamination was qualitatively determined to occur between 88 and 88.33 seconds which corresponded to a compressive local strain of 4.4%.

The aspect ratio between patch thickness and wavelength (h/Îť) was found to vary between 0.84 â&#x20AC;&#x201C; 1.22. The parameter lengths of interest are recorded in Table 1 and are listed in ranges due to using two different methods for measuring patch thickness and the non-uniform thickness of the superglue thin film. 3

Figure 4. The propagation of interfacial separation of the mock silicone foulant from the substrate starting at the initial prestretch. Figure 3. Local compressive strain of the wrinkling substrate as the pre-stretch was manually released over time at an approximately constant rate. Table 1. Relevant measurements recorded from the wrinkling bilayer.

Parameter Measurement Patch Thickness 760 â&#x20AC;&#x201C; 800 Îźm Wrinkle Wavelength 660 â&#x20AC;&#x201C; 910 Îźm Aspect Ratio 0.84 â&#x20AC;&#x201C; 1.22

Acknowledgements Conducting research over the summer would not have been possible for myself had it not been a much appreciate grant offered from Convestro.

References 1. Luka Pocivavsek, S.-H. Y., Joseph Pugar, Robert Oâ&#x20AC;&#x2122;Dea, Edith Tzeng, Enrique Cerda ,Sachin Velankar ,William Wagner. Geometric Tools for Controlling Soft Surface Adhesion 2017. lipid.uchicago.edu/~lukap/PRL/PRL_draft1.pdf.

EXPERIMENTAL STUDY OF THERMO-HYDRO-MECHANO-CHEMICAL (THMC) BEHAVIOR OF GEOLOGICALLY ACTIVATED CEMENTING MATERIALS Nina S. Chang, Yunxing Lu, and Dr. Andrew Bunger Department of Civil Engineering, University of Pittsburgh, Pittsburgh, PA 15213, USA Email: NSC22@pitt.edu, YUL84@pitt.edu, Bunger@pitt.edu Introduction As deep-water oil wells in the Gulf of Mexico (GoM) continue to provide an important energy resource, the prevention and remediation of wellbore leakage has become a crucial matter. The current standard of Portland Cement-based (PC) materials may not always withstand the harsh conditions (high temperature, high pressure, presence of CO2, etc.) of deep-water wells. By developing an entirely new cementing material, called Geologically Activated Cements (GAC), we are turning the challenging deep water conditions of the GoM into an advantage by providing the necessary acceleration of the hydration and carbonation reactions that turn granular ultramafic raw materials into cemented rock (Kelemen et al. 2008). To explore feasibility of laboratory generation of GACs based on carbonation of highly mafic olivine sand, a high temperature and high pressure batch reactor (Fig. 1) was constructed to test the conditions required for effective hydration and carbonation of ultramafic material. To obtain the desired alteration products at the relevant engineering timescales, different combinations of temperature, pressure, fluid chemistry, and grain size were explored. The extent of reaction were determined by subjecting reaction products to X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM). Our preliminary experimental results showed there is a great possibility that we can place ultramafic minerals, such as olivine, into wellbores and allow them to hydrate and carbonate in-situ to plug the well, which also achieves the goal of cap rock restoration in the deep water P&A. Experimental Methods The batch reactor was designed to withstand up to 4000 psi in pressure and 300 ℃ in temperature, mimicking the conditions in deep-water wells. To generate CO2, sodium bicarbonate and anhydrous citric acid were filled into dissolvable

capsules and placed in the reaction chamber. After placing the olivine sand into the reaction chamber and filling it with water, pressure (2000 psi) was applied to the system for five hours at ambient temperature, ensuring the dissolution and hydration of CO2 in the water (Eq. 1&2) (Giammar et al. 2005). The reaction rate of the aqueous carbonation process of olivine is dependent on the slowest, rate-limiting step – magnesium silicate dissolution (Eq. 3). However, the hydrogen ions in aqueous solution will accelerate the process of olivine dissolution, which facilitate in obtaining the desired products in a shorter time.

After five hours, the oven was turned on to 180 ℃ and set to run for 20 hours. The oven was turned off and the pressure was gradually decreased through the regulator to return to atmospheric pressure after the 20 hours of HTHP curing. Products were then carefully removed from the reaction chamber.

Figure 1 – Left: System setup; Right: Reaction chamber Experimental Results As shown in Figure 2, the dispersed sand has been turned into solid, carbonated material after the hydration and carbonation reaction. Based on the

results of XRD (Fig, 3), the product is a mixture of magnesium carbonate and olivine sand itself. Among the solids in the reaction chamber, there was a brown viscous liquid which mainly contains silica gel. With higher temperature and pressure, the carbonated material became strong and adhered to the inner surface of the tubing.

the carbon dioxide reacts with magnesium silicate to form a magnesite and silicate based product. Those carbonated products surrounded the unreacted magnesium silicate and bonded to each other to form a hardened substance.

Figure 5 – Schematic of hardening process Figure 2 – Left to right: Olivine sand before reaction; Carbonated solid after reaction; Brown liquid in reaction chamber; Hard, cementitious carbonated solid stuck to inner surface of reaction chamber.

Figure 3 – XRD result

Conclusions A laboratory test of generating the GAC by accelerating carbonation of highly mafic olivine sand was carried out, and reaction products were identified. The key findings include: • With a suitable range of temperature and pressure, the olivine sand can be turned into desirable alteration products, which are mainly composed of magnesium carbonate. This alteration can occur within one day and hence, it is relevant for engineering purposes. • The experiment is reproducible, and additional parameters to modify the reaction, such as water to solid ratio, olivine carbonation ratio, are being considered. Future work will aim at testing strength and permeability of GAC materials under high temperature, high pressure, low pH environments that simulate Gulf of Mexico wellbore conditions. References

Figure 4 – Analysis via EDS under SEM As shown in Figure 4, several points were selected to perform Energy Dispersive X-ray analysis under SEM. Spectrum 7, 11, 15 and 17 were indicated as magnesium silicate, whereas spectrum 8, 9, 18, 19, 20 and 21 were indicated as magnesium carbonate. The schematic of this hardening process can be illustrated by Figure 5,

1. Kelemen, P. B., & Matter, J. (2008). In situ carbonation of peridotite for CO2 storage. Proceedings of the National Academy of Sciences, 105(45), 17295-17300. 2 Giammar, D. E., Bruant Jr, R. G., & Peters, C. A. (2005). Forsterite dissolution and magnesite precipitation at conditions relevant for deep saline aquifer storage and sequestration of carbon dioxide. Chemical Geology, 217(3-4), 257-276. Acknowledgements Funding provided by the Swanson School of Engineering and the National Academy of Science Gulf Research Program under Grant No. 20008863 Subaward No. PO-0000053988. Lab assistance was provided by Charles Hager.

CHEMICAL WEAKENING OF GRANITE AND SANDSTONE Jiangnan Zheng and Andrew P. Bunger Department of Civil & Environmental Engineering University of Pittsburgh, PA, USA Email: jiz128@pitt.edu INTRODUCTION

DATA ANALYSIS

Granite and sandstones are some of the most abundant rocks in the Earthâ&#x20AC;&#x2122;s crust, and, as such, are encountered in a wide range of engineering applications. For example, granite and sandstones comprise reservoir rocks for oil, gas, geothermal, and water resources. Granite and sandstones are also frequently encountered in mining applications. In these contexts, understanding how pore fluids affect the mechanical properties of rocks is an essential to solving problems in numerous fields of fundamental and applied research (e.g. Van Eeckhout 1976, Nara et al. 2014).

Load versus time data is collected for each experiment. Tensile stress (ď łt) is deduced from the load (P) as (see e.g. Fernau et al. 2016)

where L is separation between lower load pins, d is specimen thickness, and h is specimen height. Then we plotted the graph of load rate (MPa/min) versus stress at failure (MPa) for both granite samples and sandstone samples.

The interaction between pore fluids and sandstones is of particular interest, especially when such interaction can enhance fracture growth in the subsurface. Thus motivated, it is common for the petroleum industry to use Hydrochloric Acid to promote fracturing of certain types of formations (e.g. Economides and Nolte 2000). However, the nature of the impact of this practice, especially in non-carbonate rocks, is unknown. The goal of the research is to determine the types of fluid that are most effective at weakening granite and sandstone. Figure 1:a. Specimen dimension. b. Load frame for three-point bending

METHODS The granite used in the experiment is Pegasus Beige Granite that is saw-cut into 0.75-inch x 0.75-inch x 4-inch beams as shown in Figure1a. The sandstone used in the experiment is Agra Red Sandstone that is saw-cut into 0.85-inch x 0.85-inch x 5-inch beams as shown in Figure1a. The method used to test granite and sandstone is the same. After cutting, the granite and sandstone beams need to absorb the target fluids which are water, hydrochloric acid, sodium hydroxide and kerosene. The method for saturation entails: 1) immersing the rock beam in the fluid container, 2) placing the fluid container into a vacuum chamber, 3) sustaining a vacuum until bubbles cease to emit from the rock, 4) releasing the vacuum and allowing specimen to remain submerged for 12-24 hours. After saturating, the rocks are loaded in three-point bending in a load-frame (Figure 1b) and the pump used to apply pressure to the actuator is a 260D ISCO syringe pump. The granite sample was put on the two support pins which have 3.5-inch separation, and the support pins distance of sandstone is 4-inch. Using a flat loading platen with locating groves to ensure the rods were in the same location each time. The load is then applied through a third rod on the top of the specimen.

RESULTS Ten tests were performed for each combination of rock and saturating fluid. The load rate was varied from 1 to 125 MPa/min. For higher load rate, the stress at failure becomes higher, there is a positive exponential relationship between these two variables. The research did three comparison experiment for granite and one comparison experiment for sandstone.

Figure 2 Granite dry group compare with water and kerosene group

The first comparison group for granite is polar fluid (water) and nonpolar fluid (kerosene). The strength of

granite specimens saturated by both fluids is lower than the dry granite specimens. The lines for water and kerosene have similar slope and compared to dry granite, the strength of granite saturated by water decreased by 27% and strength of granite saturated by kerosene decreased 23%. This difference is slight compared to experimental noise. Hence, polar and nonpolar fluid does not have significant impact on weakening the granite.

Figure 5 Sandstone dry group compare with water and kerosene group

Figure 3 Granite dry group compare with different pH group

The second comparison group for granite is pH of the fluids, namely, comparing 10% concentration of HCL and 10% concentration of NaOH. Both are found to decrease the strength of granite (Figure 3). The lines between hydrogen chloride and sodium hydroxide are very close, so that pH does not have significant effect on the strength of granite.

The one comparison group for sandstone is polar versus nonpolar fluid. According to Figure 5, strength of sandstone saturated by kerosene decreased 19%, and strength of sandstone saturated by water decreased 27%. While there is some observable effect, note that data points are scattered, so we cannot conclude with certainty that the water is more effective than kerosene at weakening sandstone. Also note that, similar to granite, 12-hour and 24-hour saturation gives the same results.

DISCUSSION We conclude that fluid saturation weakens both types of rocks compares to dry conditions. Fluid type matters very little; similar weakening occurs regardless of pH, viscosity, or polar versus nonpolar nature of the fluids. Water provides slightly more weakening than kerosene in both types of rocks, however difference between water and kerosene very little for granite but slightly more difference between sandstone and, in both cases, the difference is not large enough compared to noise to be certain that there is a difference between kerosene and water.

REFERENCES

Figure 4 Granite polar & nonpolar group compare with Hydraulic Oil group

The third comparison group for granite is low and high viscosity fluids. Both water and kerosene have similar viscosity (around 1 mPa·s), so for contrast we use Enerpac 102 hydraulic oil with viscosity of around 54 mPa·s. The result (Figure 4) shows that viscosity does not have significant impact on strength of granite. Finally, we check the possible impact of saturation time and find that the 12-hour saturation and 24-hour saturation are approximately the same for all fluids. 12-hour saturation fluids and 24-hour saturation fluids, and so we conclude that 12 to 24-hour saturation time does not have significant influence on weakening the strength of granite.

[1] Nara, Y., Nakabayashi, R., Maruyama, M., Hiroyoshi, N., Yoneda, T., & Kaneko, K. (2014). Influences of electrolyte concentration on subcritical crack growth in sandstone in water. Engineering Geology, 179, 41-49. [2] Economides, M. J., & Nolte, K. G. (2000). Reservoir stimulation (3rd ed). Wiley. [3] Fernau, H.C., Lu, G., Bunger, A.P.2016. Load-Rate Dpendence of Rock Tensile Strength Testing: Experimental Evidence and Implications of Kinetic Fracture Theory. 50th US Rock Mechanics, Houston, Texas, USA, 26-29 June 2016, ARMA 16-398.

ACKNOWLEDGEMENTS This summer research fellowship award was funded by the University of Pittsburgh’s Swanson School of Engineering and the Office of the Provost. Additional funding was provided by the University of Pittsburgh Department of Civil and Environmental Engineering.

MICROPLASTIC ADSORPTION AND ANALYZATION Elaine Yates, Eric Ben David, and Isam Sabbah Ort Braude College of Engineering, Karmiel, Israel Email: emy16@pitt.edu INTRODUCTION Plastic production continues to increase along with concerns of plastic waste and contamination. A focal concern of plastic waste is microplastic, plastics less than 5 mm in size [1]. Microplastics are known to be sources and sinks of persistent organic pollutants in marine environment [1] and have been found in wastewater treatment plant (WWTP) effluent in the United States [2]. The GoJelly Project aims to create microplastic filters made of jellyfish mucus. The beginning stages of the project focus on the adsorption of chemicals onto plastic and the recording and analyzation of microplastic in WWTP effluents. Studies such as Goedecke Caroline, et al. have observed adsorption of pollutants onto microplastic materials [3], which raises concerns of transportation of pollutants, especially with the supply of microplastics being introduced to the environment by WWTP effluents. METHODS The absorbance of pollutants by microplastics was observed through spectrometer readings. The pollutant used was chlorophenol with a stock solution of 800 µl per 100ml of synthetic wastewater. The synthetic wastewater was created by dissolving CaCl2 into water with a concentration of 83 mg/l. Additionally, the chlorophenol was reduced to a pH of 6 by adding NaOH to the solution. The calibration curve was calculated by measuring the absorbance of the chlorophenol at concentrations 0, 10, 20, 30, 40, and 50 mg/l and graphing the average absorbance for each of these concentrations against the concentrations themselves. The plastic used was two types of resins: labeled RS1 and RS2. 20 ml of concentrations of 0-50 mg/l of chlorophenol were placed into different beakers, each one containing 0.2g of either RS1 or RS2. The RS1 set also included a beaker of just 30 mg/l of chlorophenol without RS1 as a control. Both the RS1 and RS2 set were shaken for 2.5 hours. At every half hour, the resin and chlorophenol mixture would be measured by taking 1 ml of the solution to the spectrometer to read the absorbance level.

The microplastic analyzation was carried out through sieving wastewater collected from WWTP in Karmiel. There were 19 samples from tertiary treatment, 12 from secondary treatment, and one from primary treatment with an average of 24.7 liters per sample. The wastewater was filtered through two sieves collecting everything above 250 and above 80 µm. The filters were washed into separate beakers to collect the captured plastics and particles. Some of the samples were subjected to a digestion mixture of 10 ml of H2O2 and 10 ml of FeSO4 solution over heat of 60°C; most of the secondary treatment samples underwent this process to eliminate biological substances. If the sample went through the digestion process, density separation with either NaCl or NaI would occur if needed, then the solution would be filtered through a glass paper filter of 1.4 µm. If the sample did not go through digestion process, it would immediately be filtered through a glass paper filter. After the filter dried, using an optical microscope, the microplastic fragments and fibers were picked by forceps and placed into a glass tube with ethanol. Tap water was also collected and processed in the same manner for a comparison. DATA PROCESSING The absorbance levels were processed by finding the unknown concentrations of chlorophenol by dividing the measured absorbance for the beaker by the slope of the calibration curve. This calculated concentration represents the concentration of chlorophenol in the beaker, which changed over time when placed with RS1 and RS2. Graphs of the absorbance and calculated concentration against time were formed for each beaker. The calculated adsorption, q (mg/g), was found by subtracting the calculated chlorophenol concentration by the initial chlorophenol concentration and then dividing by 10: the RS1 or RS2 weight, 0.2 g, divided by the volume in the beaker, 0.02 L. The adsorption isotherm plot could then be formed by plotting q values against the equilibrium solution concentration of chlorophenol. The best fit for RS1 was linear and the best fit for RS2 was a polynomial, not fitting an isotherm model.

The microplastic analyzation of the fragments continued by drying with Ag gas and staining with nylon red, about 1-2 ml. After 30 minutes, the fragments could be viewed with fluorescence microscope in both green and red. Through the fluorescence microscope, sizes and general structures were determined. Some fragments were not stained and viewed with light to determine size and structure. Additionally, other fragments and the fibers were viewed with a scanning electron microscope (SEM) to determine detailed surface characteristics. RESULTS The isotherm adsorption graph for RS1, Figure 1, shows a linear model. The equilibrium distribution coefficient, K, is 0.24: the slope of the line. The chlorophenol absorbance did decrease over time, indicating chlorophenol was adsorbed onto the plastic for both RS1 and RS2. The equilibrium for both sets was reached about half an hour after the mixture of chlorophenol and plastic. Figure 1: Isotherm for RS1

The microplastic collection is summarized in Table 1. In comparison, the tap water samples had 0.027 fragments/l. The characterization of fragments and fibers could be seen in SEM pictures, such as Figure 2. There were possible biological substances on some of the microplastics as seen in Figure 2, but more analyzation would be needed to confirm their existence.

DISCUSSION The decreased absorbance levels show that chlorophenol was absorbed onto the plastic. The RS1 values fit a linear isotherm adsorption model, while the RS2 set of values did not, most likely due to errors of measurement. Different additional types of pollutants and plastics should be measured to increase the understanding of the adsorption. The microplastic count showed that many microplastics past through WWTP. The Karmiel WWTP has a design flow of 36,500 m3/day [4]; using 0.30 fragments/l, the Karmiel WWTP releases over 10 million fragments a day. There is a wide standard deviation with the data collected, so the accuracy is not ideal but the large volume of WWTP means that any amount of microplastic can accumulate quickly. Increased sample sizes will result in more accurate data. Adsorption of pollutants to plastic is a concern for transportation and bioaccumulation, and microplastics in effluent may enter rivers or be reclaimed for unrestricted agricultural use, spreading the possibly contaminated microplastics through the environment. REFERENCES 1. Frias, J.p.g.l., et al. Marine Pollution Bulletin, vol. 60, no. 11, 2010, pp. 1988–1992. 2. Mason, Sherri A., et al. Environmental Pollution, vol. 218, 2016, pp. 1045–1054. 3. Goedecke, Caroline, et al. Journal of Environmental Analytical Chemistry, vol. 04, no. 01, 2017 4. “Karmiel WWTP - Israel.” Balasha-Jalon Infrastructure Systems Ltd., www.bj-is.com/wwtp7.html. ACKNOWLEDGEMENTS Summer research fellowship funded jointly by the Swanson School of Engineering and the Office of the Provost of the University of Pittsburgh >80 µm

Average No./L

>250 µm 0.277

Standard Deviation Fibers Size (µm)

Table 1

Fragments

Figure 2: Microplastic Fragment

0.303

Tertiary Treatment 0.286

Secondary Treatment 0.300

0.309

0.241

0.274

0.28

Average No./L

1.554

1.668

1.815

1.244

Standard Deviation Average Width

1.137 369.51

0.851 289.95

1.021 332.88

0.851 366.74

Standard Deviation(W) Average Height

128.4 427.63

61.59 316.65

107.83 371.34

140.03 361.76

Standard Deviation(H)

141.61

62.67

112.15

101.98

VISION FIELD TESTING WITH VIRTUAL REALITY Ava Chong and Dr. Tank Kok Zuea Department of Electrical & Computer Engineering in conjunction with the Innovation and Design-Centric Programme National University of Singapore, Singapore, Singapore Email: ava.chong@pitt.edu INTRODUCTION Early detection, through regular and complete eye exams, is key to protecting vision and treating damage as soon as possible. Vision test and eye examination are traditionally practiced in hospitals and other health facilities to diagnose certain diseases such as color blindness, glaucoma, optic neuritis and brain damage. Such eye examinations are overseen by an optometrist or nurse and required 1 on 1 patient to supervisor attention. The implementation of various test such as the ones discussed in this report (Wilkins, Dry Eye and Glaucoma) are often performed on specialized instruments that cannot be reused easily for other tests. Many of these tests also take a substantial amount of space causing for a need for large testing areas. This makes tests not as easily accessible to patients and the 1 on 1 supervision causes for slow patient turnover rate. Glaucoma is a group of diseases that cause nerve damage within the eye’s optic nerve and results in vision loss and blindness. Regular glaucoma eye tests are performed in multiple distinct ways. Tonometry measures the pressure of within the eye. Eye drops are used to numb the eye followed by a warm puff of air. Normal eye pressure ranges from 12-22mm Hg with causes of glaucoma having an eye pressure exceeding 20 mm Hg. Ophthalmoscopy looks for nerve damage of the eye using specialized eye drops to dilate the pupil to examine the shape and color of the optic nerve. The perimetry test uses vision field testing the map out the patient’s complete field on vision. During this test, the patient’s response to various flashes of light in different sections of their field of vision are recorded and used to create a representation of the patient’s field of vision. Gonioscopy is the diagnostic exam that measures the angle where the iris meets the cornea [1].

METHODS Traditional methods of testing for glaucoma require high precision from the optometrist and therefore take an enormous amount of 1 on 1 time with the patient. Diagnosing glaucoma is not easy and requires careful examination. In this report we will investigate the algorithms and methods used to implement a glaucoma test in virtual reality. In this report, we will also investigate saving time during eye tests by optimizing the nurse. Instead of having 1 on 1 attention per patient, we will investigate a solution to how multiple patients can be seen at once. In this study we will attempt to research and develop the following using previous methods and studies. Some framework has already been laid out from previous projects so we will aim to debug and further develop the current project. The eye tests will be able to be taken by the patient without 1 on 1 supervision from a nurse or doctor. The eye tests will be located within the Oculus rift. There will be visual cues within the eye tests that will take the patient through the entire test. For some tests, voice recognition will be used. The voice recognition software will compare the patient’s findings to the correct answers and then output a score for the test for the nurse. A nurse station will be implemented in order to toggle between multiple patients and keep track of each individual’s personal data. The nurse will be able to monitor up to four patients both visually and numerically. First, an Asus ROG gaming desktop (i7-770 processor, 32 GB DDR4, GTX 1080 graphics) was used to port the Wilkins test. The Oculus Rift was setup with this computer to test the Wilkins test. Three Dell desktops in the lab were upgraded with Nvidia GeForce GTX 1080 Windforce graphics cards and an external fan. In total, 4 computers were able to run the eye tests at optimal resolution. The

Oculus rift was configured to work on all computers. DATA PROCESSING During this study, two tests were further developed. The Wilkin’s test consists of a paragraph of words that did not make sense strung together in black font on a white background. The patient is instructed to read the paragraph out loud and speak into a microphone which will record the speech. The second test was the vision field test. The patient is instructed to look at the center of the screen which consists of a red dot. A white dot will flash across the screen in various areas and the patient is instructed to notify the program whenever a white dot is seen. Both tests are full implemented in the Oculus rift and completely immerse the patient within the eye exam. The benefits include a controlled setting for the eye exam to take place along within minimizing the amount of space and equipment needed to take the test. Multiple eye exams can be developed for the Oculus and can be taken simultaneously. RESULTS The nurse station developed to oversee multiple patient stations was able to capture the patients’ visuals and data from patient tests. However, the original iVESA nurse station only has one crossfire input and therefore can only handle one patient station at once. The quality of the Wilkins reading test was also degraded through the Oculus. Users have noted that the paragraph is blurry and hard to read even with good vision. The voice recognition software was only able to pick up pronounced and loud feedback. This was troublesome for some trials in which the test patient spoke quietly. The software failed to understand line breaks in the paragraph and multiple words as one single word at some points. If the user read one word wrong, it would subsequently mark all other words after it as wrong. DISCUSSION After investigating the current setup, a few conclusions have been reached. The nurse station was not able to handle all computers. An introduction of a router to hook up all the computer will be needed. The router will have four cross fire cables and connect all the computers a central point.

The router will convey the computers data to the iVESA nurse station. Conclusive results as to whether or not the glaucoma test could accurately detect glaucoma could not be reached and there are still some quality and technical issues that need to be resolved within the eye exams. However, the tests made during this study show promising results. With further development and clinical trials, I believe an eye examination system through virtual reality is a feasible reality. REFERENCES [1] National Eye Institute (2015), "Facts About Glaucoma," National Eye Institute [Online]. Available: https://nei.nih.gov/health/glaucoma/glaucoma_facts. [Accessed 2 July 2018]. [2] Lim Keng, Zhi (2017), “Development and Feasibility Study of Digitized Wilkins Rate of Ready Test”. National University of Singapore, Department of Electrical and Computer Engineering & Innovation and Design-Centre Programme. [3] Cheng, Shan (2017), “Intelligent Medical Assistant for Comprehensive Eye Examination”. National University of Singapore, Department of Electrical and Computer Engineering & Innovation and Design-Centre Programme. [4] Goh Chung, Sern (2018), “Development of a Cloud-Based and Scalable Virtual Reality Platform with Data Analytics”. National University of Singapore, Department of Electrical and Computer Engineering & Innovation and Design-Centre Programme. ACKNOWLEDGEMENTS This project was an extension of the tremendous work already done by many. I would like to extend the warmest thanks to Dr. Tang Kok Zuea for his support and input as the principle supervisor. His valuable and constructive suggestions directed this project. Our project collaborator, i3 Precision and Mr. Lim Teck Sin helped with providing feedback and aiding the project. Special thanks to the doctors and clinicians at the Singapore National Eye Center (SNEC) who helped by conduction the projects’ experimental study with patients. Lastly, I would like to thank the SERIUS program, my home university and my family for making this opportunity possible for me

DESIGNING A DIELECTROPHORESIS SIMULATOR Ronen Orland and Dr. Samuel Dickerson Swanson School of Engineering, Department of Computer Engineering University of Pittsburgh, PA, USA Email: roo18@pitt.edu INTRODUCTION Dielectrophoresis (DEP) is the phenomenon of a non-uniform electric field applying a force to dielectric particles within a fluidic medium. This can be used for many applications, including categorization and separation of cells and other nanoparticles [1]. This can be very useful because it allows for cells, viruses, etc. to be identified by their dielectric properties and behaviors, as well as separate different cells from one another â&#x20AC;&#x201C; cancer cells from healthy cells and blood cells, dead cells from live ones. â&#x152;Šđ??šđ??šâ&#x192;&#x2014;đ??ˇđ??ˇđ??ˇđ??ˇđ??ˇđ??ˇ â&#x152;Ş = 2đ?&#x153;&#x2039;đ?&#x153;&#x2039;đ?&#x2018;&#x;đ?&#x2018;&#x; 3 đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;0 đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;đ?&#x2018;&#x161;đ?&#x2018;&#x161; Re[đ??žđ??ž(đ?&#x153;&#x201D;đ?&#x153;&#x201D;)]â&#x2C6;&#x2021;đ??¸đ??¸ 2 đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;

(1)

Currently, to see how cells and particles would react to different combinations of fields and frequencies, one would have to make a system that has the necessary elements and then run their own experiments. Otherwise, one would have to just read about othersâ&#x20AC;&#x2122; results. With a simulator, one would ideally only need to know the physical and electric properties of the particle and medium and could simulate any setup desired. METHODS The code for this simulator was written in Java, as it is object-oriented (which fits the nature of the simulation manipulating objects) and already has a built-in visual library, JavaFX, which could be used to make the user interface. Since the environment is three-dimensional, a Vector class was made to more easily and concisely represent the vector components of the calculations. This class allows for storage of three component values and operations on them. To represent particles, abstract class was written, which would hold all the information and could perform needed sub-operations. To create a new particle, all that is needed is to make a new class that extends the Particle class and replaces the

placeholder values in the it with the specific ones for the desired particle. These values include mass, radius, permittivity, conductivity, position, and the real and imaginary parts of the Clausius-Mossotti factor (a relationship between the complex permittivities of the particle and its surrounding medium). â&#x2C6;&#x2014; â&#x2C6;&#x2019;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; â&#x2C6;&#x2014; đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;đ?&#x2018;?đ?&#x2018;? đ?&#x2018;&#x161;đ?&#x2018;&#x161;

đ?&#x153;&#x17D;đ?&#x153;&#x17D;

đ??žđ??ž(đ?&#x153;&#x201D;đ?&#x153;&#x201D;) = đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x2C6;&#x2014; +2đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x2C6;&#x2014; where đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; â&#x2C6;&#x2014; = đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; â&#x2C6;&#x2019; đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; đ?&#x153;&#x201D;đ?&#x153;&#x201D; đ?&#x2018;?đ?&#x2018;?

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(2)

To represent mediums, another interface, Medium, allows one to call on the medium to get all the needed information: its permittivity, conductivity, density, and water level. To make a new medium in the system, all thatâ&#x20AC;&#x2122;s needed is that the new class implement the Medium interface and have the values specific to the medium. Since DEP requires an electric field to work, there must be a means of representing it. Another interface, Electrode, would be used for different electrode configurations, such as a dipole and quadrupole. These classesâ&#x20AC;&#x2122; primary function is to calculate the gradient of the root-mean-squared value of the electric field squared, as seen in Equation (1). To do so the electrode must have a set voltage and a coordinate position at which to calculate the gradient. To run all the calculations, the main class, Simulation, holds all the components of the system: the electrode configuration, the medium, the frequency, all the particles currently being simulated, and the bounds of the container in which the simulation occurs. This class has a loop that, when played, will iterate over every particle and calculate the DEP force on it at its current position, then move it according to that force. DATA PROCESSING As this is a simulator, it must take input from the user. The user can input the desired medium,

particles, electrode configuration, frequency, and voltage.

be said to be accurate yet, and requires several developments to reach that point.

Whenever the medium or frequency are changed, the Clausius-Mossotti factor for each particle must be recalculated, as it will change if the frequency or medium do. The calculation for the ClausiusMossotti factor can be seen in Equation (2). This calculation is simple, as the calculation for the real and imaginary parts can be found by multiplying out the whole equation and solving for each component.

DISCUSSION As it is now, the simulator doesnâ&#x20AC;&#x2122;t have all the features to be quite realistic in its performance. Planned additions include more user input and control over factors (container size, ability to input custom particles and mediums), more electrode configurations, and more forces acting on the particles to more realistically simulate movement (drag force, buoyancy, Brownian motion, calculations for multi-shelled particles, collisions between particles). Another addition that would help make the simulator more interactive and useful to potential users would be the ability to manually place particles at different positions in the container, instead of leaving the simulator to automatically place particles at assigned positions.

The electrode configurations are currently modeled as having point charges for their poles. So, the dipole has two point charges that generate the field, and the quadrupole has four. Since the calculation for voltage of the electrode is dependent on distance from each point charge and the charge magnitude, the electrode configuration stores the charge instead of the voltage. Using the desired voltage, a new charge is calculated for the electrode. These are different for each configuration, though, and must be coded individually. The user can add desired particles one at a time and reset the set of particles in the system to be empty. Adding a particle simply adds another iteration to the simulation loop. The user can play, pause, and stop the simulation loop. Stopping the simulation sets the particles back to their starting positions. The user can also increase or decrease the speed of the simulation, from 0.25x speed to 8x speed. This does not add any more calculation to the system, as it just changes the time interval that the force on each particle is applied. This way, the performance of the simulator is not affected. RESULTS As it is now, the simulator can take user input for most of the configurable aspects of the system, and different configurations and inputs translate to different movements for the particles. It still lacks a visual basis for tracking movement, however, and currently the only display is a console output that prints each particle and its position on every loop. Once a visual is introduced, it will be easier to tell if the movement of particles is what is expected, and as a result, more straightforward to troubleshoot. The only force that currently acts on the particles is the DEP force, which is unrealistic, as many other factors influence their movement. As such, it cannot

Once enough of the simulator has been developed to behave in a consistent and accurate manner, it will be made open source. This way, if any interested parties want to develop it further, they can do so as needed. This would also help build up the library of configuration choices in the system, as those who experimentally determine the dielectric properties of particles and mediums can add their findings. There are currently no openly available DEP simulators, so this has the potential to be a useful new resource to those interested in working with DEP. REFERENCES 1. Gascoyne, Peter R. C., and Jody Vykoukal, â&#x20AC;&#x153;Particle Separation by Dielectrophoresis.â&#x20AC;? Electrophoresis 23.13 (2002): 1973-1983. PMC. Web. 15 May 2018. ACKNOWLEDGEMENTS Funding jointly provided by the Swanson School of Engineering and the Office of the Provost.

MODEL AND ANALYSIS OF MASK-RCNN Zheng S.E.1 and Ge S.S.2 1 Department of Electrical and Computer Engineering, University of Pittsburgh , Email: shz93@pitt.edu Web: https://www.engineering.pitt.edu/computer/ 2 Department of Electrical and Computer Engineering, National University of Singapore , Email: samge@nus.edu.sg Web: http://www.ceg.nus.edu.sg INTRODUCTION yxiw ]T ]T ) and 2k output denoting object/background probabilities respectively, where k Convolutional Neural Network is originally inspired by acis the number of boxes lined out around that position tivation and connection mechanism of biological light revia permuting different scales and hw-ratios [1]. ceptors. Despite such a successful bionic model, its mathematical backstone is relatively deficient and many issues • Fast-RCNN Object Detection Network: (e.g. model uniqueness) remain unresolved. Mask-RCNN – A CNN with a RoI Projection: has been shown to be the state-of-the-art machine learning solution on COCO 2017 Object Detection Task for instance-aware semantic segmentation (pixel-level object RoIj = RoI(yxi ) = rjL , for {x|cx > δ} detection) of an image. In this paper we will give a formal rjl = [rjl−1 /plss ], rj1 = yxi model of Mask-RCNN from formalizing some of its milestone preceedors (i.e. CNN, and Faster-RCNN ) (Readers that maps the position of region proposal to are recommended to acknowledge the general data structhat of the correspondent Region of Interest ture and algorithm of the network prior to this paper). We (RoI) (with objectness higher than a certain will also analyze the robustness performance of a Tensorthreshold δ) on the feature map output by the Flow implementation of this model on street view images, CNN. Here rjl is the temporary result of recurwhich we have a particular interest on. sion at layer l and L is the total number of layers. – A RoI-Pooling (Sub-sampling) layer in the CNN between the convolutional part and the fully connected part:

FORMAL MODEL GAKKI OPERATOR Define the Gakki Operator (·) for the algebra · and between two arbitrary vectorial forms u, v as w = u(·)v, where:

z l = z l−1 (∗)W l dim(u)

wi :=

where z l is the feature vector of RoIj , z l−1 is the pixels of region RoIj on the feature map, plh = dRoIj2 /plbn e, stride pls = plh , and plbn is the bin number of the RoI-Pooling layer. – A bounding box rectification linear regression layer and a object classification softmax layer:

uj · vi , i = 1, ..., dim(v)

j=1

for product operation it is equivalent to [u]T v, where [u]ij := ui , for j = 1, ..., dim(v). We introduce this notation for more convenience in multiple convolutions on multiple input in a convolutional layer.

ybbx = Wbbx z l−1 , ycls = Wcls z l−1 Where z l−1 is the output of the RoI-Pooling layer (for a particular RoIj ). The loss function is: X 1 ∗ ||22 −λ log(eycls∗ / eyclsk ) L(y, y ∗ ) = ||ybbx −ybbx 4

FASTER-RCNN Faster-RCNN is a composition of: • Region Proposal Network (RPN): a network with input: image and output: rectified positions of the pre-generated boxes that maximize their objectness (the probability of containing an object):

where k is the classification ordinal and ycls∗ is the inferred score for the ground-truth classification.

f = F CNp (x)

MASK-RCNN In addition to a Fast-RCNN, Mask-RCNN introduces a mechanism to ensure the fidelity of RoI Projection, which is the RoIAlign: Y RoIj = RoIAlign(yxi ) = yxi / plbn , for {x|cx > δ}

T T yx = Wreg fx , cx = Wcls fx , ∀x in f

L(yx , yx∗ ) =

k 1X ∗ ∗ Lcls (yxi , yxi ) + λyxi Lreg (cxi , c∗xi ) k i=1

The second equation represents two paralell fully connected layers with 4k-dimensional output encoding boxes positions (in the format [[yxix , yxiy ]T , [yxih ,

The input of the RoI-Pooling layer is bilinearly interpolated according to real-numbered coordinates RoI: 1

zil−1

R21 − RoIj11 RoIj11 − R11 I(R11 , R12 ) I(R11 , R22 ) R22 − RoIj12 = (∗)avg I(R21 , R12 ) I(R12 , R22 ) RoIj12 − R21 (R21 − R11 )(R22 − R12 ) R1 = brc, R2 = dre r = RoIj1 + RoIj2

Where I(·) is the value on Image for a given position and i is a position ordinal on the RoI (max i = size(yxi )). Here the interpolation result is also sub-sampled by a average window, with plss = size(yxi )/RoIj2 . ANALYSIS: DATA, METHODS, AND RESULTS We take a simple analysis on a Mask-RCNN implementation on Keras. [2] It’s based on Feature Pyramid Network (FPN) and a ResNet101 for the co-trained FCN. We utilize the model pre-trained with Microsoft COCO dataset [3] to test the model robustness w.r.t. brightness, contrast, and noise transforms, which are common parameters that affect the quality of an image in a natural scene. Particularly, we take images of street view as our test dataset, and compute the MSE between masks of the transformed image output and the original image output. We found strong robustness of the model w.r.t. the tested transforms (average covariance is significantly low).

(1) (2) (3) (4) (5)

Data at position (pixel) denoted by vector x

yxi

i-th element of the data (e.g. i-th anchor box) at position x

[3] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision. Springer, 2014, pp. 740– 755.

ACKNOWLEDGEMENTS This research was conducted under the supervision of S.S. Ge.

p ∈ [0, q) ⇔ pxi ∈ [0, qi ) for each position x and dimension i

Noise Noise Noise Noise Noise AVG

A distinguishment from similar data y

[2] “Mask rcnn: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow,” Jul. 2018, original-date: 2017-10-19T20:28:34Z. [Online]. Available: https://github.com/matterport/ Mask RCNN

Parameter h at layer l of the network

Image

ỹ

REFERENCES [1] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” arXiv:1506.01497 [cs], Jun. 2015. [Online]. Available: http://arxiv.org/abs/1506.01497

NOMENCLATURE ph A vectorial parameter will only be boldfaced when it’s essentially a dictionary of structurally associated parameters (e.g. belong to the same layer) , except for the position x. ph represents the value of key h in p, rather than the h-th element of p. plh

i size(yxi )

Table 1: MSE w.r.t. Noise Transform 30 40 50 60 70

Covariance

1.69539 1.52667 0.41963 0.42665 0.31494 0.87665

1.64609 3.14646 1.52444 0.55302 0.59310 1.49262

1.57965 3.48750 1.17963 0.49367 0.44138 1.43637

3.22860 2.71750 1.95444 3.01989 15.57625 5.29933

0.08713 0.09792 0.09608 0.16042 0.79070 0.24645

0.87449 4.02646 1.44519 1.28614 0.57011 1.64048

0.83733 3.98729 1.83593 1.34578 1.61149 1.92357

1.83256 3.82625 1.95000 1.30887 1.80613 2.14476

2.14335 4.41729 1.91000 1.55482 3.50268 2.70563

DEEP LEARNING FOR HYPERSPECTRAL IMAGE CLASSIFICATION ON EMBEDDED PLATFORMS Siddharth Balakrishnan, David Langerman, Evan Gretok, Alan George NSF Center for Space, High-performance, and Resilient Computing (SHREC) University of Pittsburgh, PA, USA Email: sid.bala@pitt.edu, Web: http://www.shrec.us

INTRODUCTION Numerous images of Earth are taken from the International Space Station. These images are usually very high in resolution and pixel depth. As a result, downloading every single image for analysis on earth is prohibitive in communication time and processing power in an already computationally constrained environment [1]. The primary goal of this project is to benchmark and evaluate deep-learning apps for hyperspectral image (HSI) classification on embedded platforms. From this study, the optimal app for platforms with limited computing capabilities can be determined. This study also serves as a proof-ofconcept for conducting such analysis on-board a spacecraft, so a subset of images of interest can be downloaded â&#x20AC;&#x201C; saving time, memory, and other mission-critical resources. HSI data is usually gathered using special cameras that collect light-intensity information for various wavelengths along the electromagnetic spectrum for each pixel in an image [2]. Classification codes are used to detect and differentiate between characteristic features in light intensity values to identify the object of interest. Three classification methods were used for HSI analysis in this study: Support Vector Machine (SVM); Multi-Layer Perceptron (MLP); and Convolutional Neural Network (CNN) [3] [4]. These were executed on a desktop CPU (Intel Core i7-6700 quad core @ 3.4GHz, 16GB RAM), and two embedded platforms, the ODROID-C2 (ARM CortexA53 quad core @ 1.5GHz, 2GB RAM) and the Raspberry Pi 3B (ARM Cortex-A53 quad core @ 1.2GHz, 1GB RAM). METHODS In this study, the Indian Pines HSI dataset, an image of farmland in northwestern Indiana, is used [5]. The classification techniques were used to identify different types of land cover, ranging from buildings to a variety of vegetation in the farmland. Once the data set was downloaded, each of the three different codes was used to conduct HSI analysis. The Scikit-learn python library was used to develop the SVM in this study [6]. The values of the parameters for the SVM model are summarized in Table 1a. A radial basis function kernel was used to

develop the SVM. The value of C in Table 1a indicates the relative weight coefficient. It should be noted that the tolerance value may differ on other platforms. The values for each parameter were chosen using a fivefold cross validation technique. The TensorFlow framework was used to construct and train the MLP and CNN deep-learning models in this study based on land-cover classification models developed by SAC-ISRO [4] [7]. The architecture of the MLP model was set so that each patch of the image used to train and test the model was used as Figure 1: MLP Architecture input into the MLP model with the architecture shown in Figure 1 [4] [7]. The parameters used to Figure 2: CNN Architecture construct the model are summarized in Table 1b. Batch size is the number of samples in the training dataset for each class. In order to maximize accuracy, the model was trained for 50,000 epochs. Similarly, each patch of the image used to train and test the model was input into the CNN model with the architecture shown in Figure 2 [4]. The parameters used to construct the model are summarized in Table 1c. In order to maximize accuracy, the model was trained for 4,000 epochs. Table 1. Parameters Used for SVM, MLP and CNN Models (a) SVM Gamma: 2-8 C: 27 Tolerance: 1e-14

(b) MLP Patch Size: 1 Batch Size: 200 Learning Rate: 0.01

The raw dataset was pre-processed to include a border to avoid any loss of data (which could occur depending on the size of the patch used when training deep-learning models). The pixels in the image were divided into 80% training and 20% testing sets. The training and testing sets were initially used to develop and validate the SVM, MLP, and CNN models on the desktop PC before porting the codes to other platforms.

(a) Accuracy Benchmarks

PC SVM

OD MLP

PI CNN

3,032

1,543 205 42

72 PC

546

SVM

390

OD MLP

673

CNN

529

Megabytes

Seconds

(b) Run-Time Benchmarks

85 97

178

168

235

PC SVM

168

247

169

OD MLP

153

CNN

201 128

Figure 4: Benchmark Results Collected on the PC, ODROID and Pi

For the final prediction, each pixel from the raw data was input into the trained models and the predicted outputs (numeric labels associated with each of the land-cover types of interest) were used to reconstruct an image (like those shown in Figure 3). The labels predicted for each pixel were compared with the predefined labels in the ground-truth image to determine the accuracy of the prediction. The predictions were executed on the PC, ODROID, and Pi platforms, during which run-time performance and memory utilization were measured. Ground Truth

SVM

MLP

CNN

Figure 3: Image Outputs

RESULTS A comparison of accuracy results for the models from this study with similar models in other works is shown in Table 2. The accuracy benchmarks for the SVM, MLP, and CNN were relatively consistent across all of the platforms (Figure 4a). Output images for each model are shown in Figure 3. Run-time benchmarks were recorded for each classification model. The CNN was consistently the slowest on all platforms. SVM was fastest on the PC, while the MLP was fastest on the ODROID and Pi (Figure 4b). Memory use was relatively consistent across all platforms, shown in Figure 4c.

SVM MLP CNN

Table 2. Accuracy of Models Ours Others 62% 77% [3] 85% 82% [4] 97% 94% [3], 96% [4]

DISCUSSION Comparing accuracy of models with other studies reinforced that the three types of predictive models used in this study are valid. The run-time performance for all three models can be seen in Figure 3b. The runtime increases from SVM to MLP and CNN models

can be explained by the increased complexity of the models and the abundance of processing power, memory capacity (RAM), and memory bandwidth in the PC compared to the embedded platforms. Between the embedded platforms, MLP and CNN were observed to be slower on the Pi than on the ODROID despite their common architecture. The Pi has less RAM (1GB vs. 2GB) and a lower clock speed (1.5GHz vs. 1.2GHz) than the ODROID, which could explain this run-time discrepancy. CONCLUSIONS The recommended code for each platform is as follows. CNN is recommended for the PC, due to the high accuracy of 97% that was achieved, 7.5 times faster than the fastest embedded platform (ODROID). MLP is recommended for the ODROID and Pi, as it showcased the shortest run-times (390 seconds and 529 seconds, respectively) and second-highest accuracy benchmark of 85%. A minimal increase of 12% in accuracy, in our opinion, is not justified for the CNN on the embedded platforms, due to the massive run-time increase by a factor of 4 for the ODROID and a factor of 5.7 for the Pi. REFERENCES [1] A. J. Pellish, Radiation 101: Effects on Hardware and Robotic Systems, MD: NASA Goddard, 2015. [2] HySpex, "Hyperspectral Imaging," Norsk Elektro Optikk, Oslo, Norway, 2016. [3] X. Cao, "Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network," ArXiv, vol. 1705, pp. 727-741, 2017. [4] S. A., Deep Learning for Land-cover Classification in Hyperspectral Images, Satellite App Center - ISRO, 2016. [5] Baumgardner, 220 Band AVIRIS Hyperspectral Image Data Set, Purdue University Research Repo, 2015. [6] Pedregosa, "Scikit Learn: Machine Learning in Python," JMLR, vol. 12, pp. 2825-2830, 2013. [7] Abadi., TensorFlow: A System for Large-Scale Machine Learning, 2016.

ACKNOWLEDGEMENTS This research was funded by SSoE and SHREC

Load Detection Algorithm For Smart Grid Applications Dekwuan Stokes, Ryan Brody, Adam Emes and Alex Williams Department of Electrical and Computer Engineering University of Pittsburgh, PA, USA Email: des154@pitt.edu INTRODUCTION The efficiency of the power grid is sought out to be improved. One way to improve the grid, is the use of load detection. Load detection uses an algorithm to detect various loads such as resistive, inductive, and capacitive [1]. The algorithm implemented in this research is called Non-Intrusive Load Monitoring (NILM). NILM is an algorithm that helps identify whether a load has been turned on or off. Each event is classified by a machine-learning code that was created specifically for this research. The machinelearning code was written in MATLAB and takes advantage of the classify function in order to determine the type of load that was turned on or off. The results from this research will provide assistance in courses on smart grids and power for students at the University of Pittsburgh. The students will be able to use microcontrollers in order to control the interaction between loads. Testing for the interface of household loads through sensors will be done in the University of Pittsburgh’s Electric Power System Laboratory. METHODS In the University of Pittsburgh’s Electric Power System Laboratory, there’s six lab benches that contain single and 3 phase resistive, inductive and capacitive loads. In Figure 1, one of the benches is pictured. Using these benches and Dranetz meters, the voltage and current waveforms are taken.

The voltage waveforms were captured with DranView 7 software and all the data was exported into MATLAB. In Figure 2, 3, and 4, the load waveforms are shown. These waveforms were fed into a machine learning algorithm where the waveforms are cut from a sampling rate of 256 samples/cycle. The pre and post transient were cut to include the first two cycles and final two cycles respectively. All of the samples’ starting and end points were chosen from the zero crossings of their phase voltages. The pre-transient data is calculated at the first crossing and 512 is added to the time index since each cycle contains 256 data points in a cycle. The post-transient data is calculated the same except it starts at the maximum time and the time index is subtracted by 512. In order to correctly label the load, a training and class array was formed with one of the benches. Then they are inputted into the built in MATLAB ‘classify’ function. After the waveform is classified, the data is exported to a file named ‘test.mat’. Using the phase voltages and currents, the real and reactive power were calculated by just finding the rms voltages and currents and just basic power equations. The values were then stored into a file named ‘power.mat’.

Figure 2: Current waveform of a purely resistive load.

Figure 1: Power Lab benches at University of Pittsburgh.

Figure 3: Current waveform of a purely capacitive load. Figure 6: Output result for a capacitor turn on event.

Figure 4: Current waveform of a purely inductive load.

DATA PROCESSING The raw voltage waveforms were taken and exported from the Pitt power benches to a computer using Dranetz meters. Every electrical element in the six Pitt benches was recorded and each waveform was inserted into the code. The code then outputs the load type using 0 for no event, 1 for a resistor load, 2 for a capacitive load, and 3 for an inductive load. The code also outputs real and reactive power consumption. RESULTS Overall, the results were excellent. Using the training data from one of the benches helped provide accurate responses to different loads that were tested. In Figures 5, 6 and 7, the outputs are for one turn on event for a resistor, capacitor, and inductor respectively.

Figure 7: Output result for an inductor turn on event.

DISCUSSION In the results, each event starts off with 0 and then proceeds with the number that corresponds with the correct load. This means that originally there was no event and then a given event occurred (turn on). Each phase is listed because the Pitt benches have a 3phase output. There are some components that will only use one phase and have no event occur in the other two phases. The code is able to take that into consideration and give no event to that particular phase. For turn off events the output is the inverse of the turn on events pictured in the results section. For example, instead of starting with 0 and then have the number for the load, the first number is the load and the next is a 0. The real and reactive power consumption is another tool to determine if the load was correctly identified and to measure the amount of energy consumption of a given load. REFERENCES

Figure 5: Output result for a resistor turn on event.

1. S. Werner and J. LundĂŠn, "Smart Load Tracking and Reporting for Real-Time Metering in Electric Power Grids," in IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1723-1731, May 2016. 2. M. A. Mengistu, A. A. Girmay, C. Camarda, A. Acquaviva and E. Patti, "A Cloud-based On-line Disaggregation Algorithm for Home Appliance Loads," in IEEE Transactions on Smart Grid.

ACKNOWLEDGEMENTS Thank you to SSOE for research internship award. Ryan Brody, Adam Emes, Alex Williams, and Dr. Kerestes for help on the project and great guidance.

EVALUATING OCCLUSION SUCCESS OF ESOPHOCCLUDE PROTOTYPES IN COMPARISON TO DIAMETER AND RADIAL FORCE Gordon Bryson1, Dr. Youngjae Chun1, and Dr. Philip Carullo2 1 Medical Device Manufacturing Laboratory, Department of Industrial Engineering 2 University of Pittsburgh Medical Center, Department of Anesthesiology University of Pittsburgh, PA, USA Email: gkb6@pitt.edu, Web: http://www.pitt.edu/~yjchun/home.html INTRODUCTION Pulmonary aspiration is the passive flow of gastric contents from the stomach up the esophagus and into the lungs. Food particles obstructing the lung tissue can result in increased heart and breathing rates, low blood oxygen levels, and eventually pneumonia [1]. During rapid sequence intubation before emergency surgeries, the risk of aspiration is higher than planned surgeries because patients have not fasted in preparation the procedure. Current solutions for aspiration such as cricoid pressure and the nasogastric tube are ineffective and time-consuming [2]. The Esophocclude is a device concept that can be swallowed by the conscious patient just before intubation. The pill-sized capsule contains a balloon that expands in the lower portion of the esophagus to block the flow of gastric contents up the esophagus until the breathing tube is placed. The balloon needs to be designed such that it is small enough to fit in a pill-sized capsule, yet large enough to occlude the esophagus from gastric contents. Furthermore, the radial force of the balloon must be strong enough to not migrate because of pressure from gastric contents, yet weak enough that it does not cause tissue damage. Thus, the purpose of this study is to find a relationship between balloon diameter, radial force, migration, and occlusion efficiency. Result from this study are intended to be used create more optimal device prototypes for in vitro testing in animal tissue.

Figure 1. Image of device number 1 fully inflated.

METHODS Five Esophocclude devices were tested with varying diameters and radial forces. These models are constructed out of a thin film nitinol wire, which is super elastic and self-expanding. This skeleton is covered in a polytetrafluoroethylene (PTFE) membrane to create a waterproof barrier. The devices were numbered arbitrarily, but their increasing diameter (4, 2, 1, 3, 5) and increasing radial force (4, 3, 5, 1, 2) were recorded. One of the devices tested is shown in Figure 1. In place of esophagus tissue, a model tube was used to simulate the mechanical characteristics of esophagus tissue. Latex surgical gloves showed similar stretch, radial elasticity, and impermeability to water as living esophagus tissue. Three esophagus models were created of circumferences 5cm, 6cm, and 7cm to show how each device might perform differently in different size esophagi. To simulate the pressure of the stomach in a passive manner rather, a commercial latex balloon was used. The balloon could produce passive pressures ranging from 0.56 â&#x20AC;&#x201C; 0.68 psi depending on the volume of water in the balloon. Once valves at the end of the balloon were opened, the balloon pushed water through a series of silicon tubes toward the model esophagus tube. Between these points, a pressure sensor continuously measured the pressure the device was withstanding. The model esophagus contained the Esophocclude prototype of interest already fully expanded. Liquid flow rate was determined by measuring the rate of water leaving the model tube. DATA PROCESSING A control test was completed first with no Esophocclude device inserted to establish a flowrate and pressure for the devices to reduce. Then all five devices were tested in numerical order one at a time in each of the three esophagus model sizes. The flowrate reported is the average flow rate over the first 100ml volume leaving the esophagus model past

the device. The pressure reported is the maximum and minimum pressure readings during the first 10 seconds the device withstands the passive pressure from the balloon. RESULTS Without an occluding device, the flow rate measured 456 mL/min with a pressure of 0.18 psi inside the tube system. Each test with the devices showed a reduction in flow rate, as shown in Figure 2, but only devices #1 and #5 showed meaningful occlusion success, reducing flow to 32 mL/min at best.

Flowrate (mL/min)

Flow rate in varying esophagus circumferences 500 400 300

5cm

200

6cm

100

7cm

0 1

2 3 4 Device number

Figure 2. Flow rate measurements from each of 5 Esophocclude prototypes. The three tests for each device correspond to the circumferences of the three esophagus models

Unsurprisingly, these two devices also withstood the highest passive pressure from the stomach. A notable observation with devices #1 and #5 was how water penetrated the PTFE membrane to fill the balloon with water, improving occlusion success. This phenomenon was not seen in the other devices. No relationship was found between device diameter and occlusion ability. For example, devices 1 and 5 had the lowest flowrates, but device 3, which had comparable diameter, did not have a low flowrate. Pressure on each device during flow rate measurement

Pressure (psi)

0.6 0.4 0.2 0 1 2 3 4 5 Figure 3. Maximum and minimum pressure measured Device number upon device max across all three sizes esophagus model. pressure minof pressure

The study also did not show a correlation between the esophagus model size and its interaction with the device to reduce flow rate. Figure 3 shows measured maximum and minimum pressures from each device during the first 10 seconds of flow. As is to be expected, the higher maximum pressures are seen in devices 1 and 5, corresponding to their low observed flow rate. DISCUSSION The study was not successful in showing a strict relationship between radial force, diameter, migration, and occlusion success, but it did bring to attention other factors in the device that affect flow rate reduction. Noticing how the more successful devices filled with water, future devices will be designed and constructed to encourage filling. Since the devices tested did not offer simple increases in diameter and radial force independent of each other, future devices will be designed with these two variables isolated. These engineered factors should help future studies find the optimal ratios maximizing flow rate reduction. This study also brought to light the possibility of the device migrating through the esophagus model due to low friction between PTFE and surgical glove material. Future esophagus models may incorporate a layer of material to better simulate esophagus endothelial texture to understand if the migration could occur in live tissue. In conclusion, the study did not show a correlation between device diameter and occlusion success, nor between radial force and occlusion success. It did, however, suggest useful techniques for improving the device prototype in future iterations. REFERENCES 1. Nason, K.S. Thorac Surg Clin 25, 301-307, 2015. 2. Beckford et al. AORN J 107, 716-725, 2018. ACKNOWLEDGEMENTS I would like to acknowledge Dr. Youngjae Chun, the Swanson School of Engineering, and the Office of the Provost for jointly funding this project. I would also like to acknowledge Yanfei Chen for his guidance in the experimental setup and materials selection.

IN VITRO VIABILITY TESTING OF PH SENSOR INCORPORATION IN TONGUE PROSTHETIC ASSIST DEVICE FOR TREATING DYSPHAGIA Jack Hastings1, Neil Gildener-Leapman, MD2, and Youngjae Chun, PhD1,3 1 Department of Bioengineering, University of Pittsburgh 2 Department of Surgery, Albany Medical College 3 Department of Industrial Engineering University of Pittsburgh, PA, USA Email: jth66@pitt.edu, Web: http://www.pitt.edu/~yjchun/home.html INTRODUCTION Dysphagia is a medical condition in which the afflicted patient has difficulty swallowing. Difficulty starting a swallow is referred to as oropharyngeal dysphagia. Oropharyngeal dysphagia typically results from atypical functioning of nerves in the tongue, mouth, pharynx, and upper esophageal sphincter [1]. The Medical Device Manufacturing Laboratory has developed a tongue prosthetic assist device (TPAD) to help dysphagia patients produce greater force in the swallowing motion. A specific case of dysphagia exists in patients who are missing parts or all of their tongue, which is commonplace in those suffering from tongue cancer. In this case, the patient would have limited to no ability to taste. Thus, including a pH sensor is the first step in creating add-on features to the TPAD to aid patients in better analyzing and understanding what they are eating without a sense of taste. In this study, the concept of incorporating a pH sensor into the TPAD for treating dysphagia was explored utilizing an in vitro silicone mouth and throat model. The aim of this study was to evaluate the effectiveness of a pH sensor’s ability to accurately measure the pH of a liquid flowing past it in the swallowing process and hold this measurement for enough time to observe an accurate value with the TPAD. METHODS Two different sets of tests were conducted using two different experimental setups. However, both set-ups used the same principle of utilizing three test fluids (green tea, orange juice, and Coca-Cola) for three trials of each. In the first experimental setup a Hanna Instruments HI10832 HALO® Wireless pH Meter was inserted into the silicone throat so that only the measuring “Microbulb” of the pH meter was exposed. For each trial, the test liquid’s initial pH

was measured prior to testing as a baseline accurate reading. The test liquid was then pumped rapidly through a piece of silicone tubing and across the pH meter using a syringe to simulate the flow of liquid through the mouth and down the throat in swallowing. The pH meter would record the observed pH every second. The focus of the second set of testing was to more accurately represent how this concept would be applied to the TPAD, as well as factor the randomness of chewing into experimentation. The pH Meter was inserted into the model through the roof of the mouth with only the measuring Microbulb on the end of the meter exposed. A plastic bag with a small hole to thread the Microbulb through was used to seal the mouth model and prevent leakages. A mouthguard taken from early stages of TPAD testing was modified with a flexible support and plastic wrap with a poked hole for the Microbulb. The test liquid would be poured into the plastic baglined mouth model, and the plastic bag would be sealed with tape. Three rapid chewing motions were then performed using the mechanical crank incorporated into the silicone model, while again recording a pH reading each second for a minute. DATA PROCESSING The observed pH range being displayed throughout a trial was compared to the baseline measured pH value of the test liquid using a line graph. RESULTS The results of the first experiment showed that the pH meter was relatively accurate in measuring the pH of the test liquid as it flowed past. All trials using this model recorded an actual pH by the end of the trial that was within 0.4 of the actual pH, except for just Trial 2 of the green tea. All other trials were very accurate and practically overlapped the target pH line exactly. The second set of testing had exceptional

results as well. While the pH range of the observed pH throughout the trials were slightly less accurate when compared to the target pH values, there were no outlier trials in this set. The green tea had the most significant pH fluctuations during chewing, with the other two test liquids having little to none. Although, the fluctuations seem to have little effect on the final pH readings. The least accurate trial was Trial 1 of the green tea, and yet the ending pH was just 0.26 off from the target measured pH (MpH). The graphs of both experiments utilizing green tea are shown below for reference. Each trial has an observed pH line (of darker color), and a target measured pH line (of lighter color). The closer the observed pH line is to the target MpH line, the more accurate the reading throughout the trial.

Figure 2. Combined plot of three trials using green tea as the test liquid in experimental setup #2.

DISCUSSION These two sets of experiments have demonstrated that not only can pH be accurately measured when a liquid is flowing across the pH meter, but the meter can maintain the pH measurement for a longer period of time. Additionally, the second set of tests demonstrated that a chewing motion was essentially a non-factor in affecting the pH measurement, which is an important discovery for TPAD implementation.

Figure 1. Combined plot of three trials using green tea as the test liquid in experimental setup #1.

Current limitations that need to be explored for this study to be implemented into the TPAD include the need for a much smaller pH meter and an algorithm for determining when an accurate pH reading has been obtained and can be displayed to a user. Further experimentation in this area could include more variation in test liquids and using test foods instead of liquids. REFERENCES 1. Gyawali, C., MD, MRCP. (2010, November). Dysphagia. Retrieved February 26, 2018, from http://patients.gi.org/topics/dysphagia/ ACKNOWLEDGEMENTS Partial funding was provided jointly by the University of Pittsburgh Swanson School of Engineering and the Office of the Provost.

MODAL ANALYSIS OF HUMAN BRAIN DYNAMICS AFTER HEAD IMPACT Ryan Black and Hessam Babaee Department of Mechanical Engineering and Materials Science University of Pittsburgh, PA, USA Email: rtb18@pitt.edu INTRODUCTION Each year in the U.S., approximately 1.4 million people suffer mild traumatic brain injury (MTBI) [1,2]. However, diagnosing MTBI is challenging due to the acute nature of symptoms that resolve quickly, lack of injury evidence in brain imaging, and the absence of a universally accepted definition of MTBI [3]. As a result, this leads to undiagnosed cases of MTBI, which is especially problematic for at-risk groups, such as contact-sport athletes, because chronic concussions can lead to increased risk of neurocognitive impairments, poor mental health, and neurodegenerative diseases [4]. With the prevalence of MTBI and the long-term effects of multiple concussions, there is a need to better understand the mechanisms behind MTBI to prevent brain injury and improve diagnostic accuracy. Currently, the widely accepted hypothesis for the injury mechanism of MTBI is excessive axonal stretching in certain regions of the brain caused by rapid accelerations of the tissue following impact [5]. This hypothesis has been supported by several studies that have investigated the relationship between indicators of brain injury, such as large tissue deformations and brain acceleration [5,6]. Overall, spatial characteristics of brain impact dynamics have been examined through a variety studies, however the temporal characteristics of the transient event are largely unknown [7]. In this study, we investigate the spatiotemporal characteristics of human brain impact dynamics through modal analysis, using a new data-driven low-rank approximation technique to extract modal behavior [8]. METHODS For this study, we used relative (to the skull) brain tissue deformation data from 183 FE simulations from a previously published study by Laksari et al. [7]. We extracted the spatiotemporal characteristics of the nodal relative brain displacement data using a novel data-driven low-rank approximation that can

capture the transient behavior of time developed stochastic data, Dynamic Basis (DB). DB is a linear reduction of time developed stochastic data of the form: (1) đ?&#x2018;ťđ?&#x2018;ť(đ?&#x2018;Ąđ?&#x2018;Ą) = đ?&#x2018;źđ?&#x2018;ź(đ?&#x2018;Ąđ?&#x2018;Ą)đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;(đ?&#x2018;Ąđ?&#x2018;Ą)đ?&#x2018;&#x2021;đ?&#x2018;&#x2021; + â&#x201E;&#x2021; where đ?&#x2018;ťđ?&#x2018;ť(đ?&#x2018;Ąđ?&#x2018;Ą) (n x s) is a snapshot data matrix (đ?&#x201D;źđ?&#x201D;ź[đ?&#x2018;ťđ?&#x2018;ť(đ?&#x2018;Ąđ?&#x2018;Ą)] = 0), whose columns are observations of the stochastic system at time đ?&#x2018;Ąđ?&#x2018;Ą, đ?&#x2018;źđ?&#x2018;ź(đ?&#x2018;Ąđ?&#x2018;Ą) (n x r) is a low rank subspace, whose columns are modes, đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;(đ?&#x2018;Ąđ?&#x2018;Ą) (s x r) are stochastic coefficients, and â&#x201E;&#x2021; is the reduction error. The evolution equations for DB are derived from the following variational principle with an orthonormality condition for the modes introduced using Lagrange multipliers: đ?&#x2018;&#x;đ?&#x2018;&#x;

đ?&#x2019;šđ?&#x2019;š(đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; đ?&#x2019;&#x161;đ?&#x2019;&#x161;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; ) đ?&#x2019;˘đ?&#x2019;˘ďż˝đ?&#x2018;źđ?&#x2018;źĚ&#x2021;, đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;Ě&#x2021;ďż˝ = ďż˝đ??¸đ??¸ ďż˝ďż˝ â&#x2C6;&#x2019; đ?&#x2018;ťđ?&#x2018;ťĚ&#x2021;ďż˝ ďż˝ đ?&#x2019;šđ?&#x2019;šđ?&#x2019;šđ?&#x2019;š đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;=1 đ?&#x2018;&#x;đ?&#x2018;&#x;

(2)

+ ďż˝ đ?&#x153;&#x2020;đ?&#x153;&#x2020;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; (đ?&#x2018;Ąđ?&#x2018;Ą)(â&#x;¨đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; , đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;Ě&#x2021; đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; â&#x;Š â&#x2C6;&#x2019; đ??&#x201C;đ??&#x201C;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; ) đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;,đ?&#x2018;&#x2014;đ?&#x2018;&#x2014;=1

where đ??&#x201C;đ??&#x201C;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; (đ?&#x2018;Ąđ?&#x2018;Ą) â&#x2030;&#x201D; â&#x;¨đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; , đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;Ě&#x2021; đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; â&#x;Š and đ?&#x2018;&#x;đ?&#x2018;&#x; represents the reduction order (RO). This equation leads to the following evolution equations: đ?&#x2018;źđ?&#x2018;źĚ&#x2021; = ďż˝ đ?&#x201D;źđ?&#x201D;źďż˝đ?&#x2018;ťđ?&#x2018;ťĚ&#x2021;đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;ďż˝đ?&#x2018;Şđ?&#x2018;Şâ&#x2C6;&#x2019;1 + đ?&#x2018;źđ?&#x2018;źđ?&#x2018;źđ?&#x2018;ź (3) â&#x160;Ľđ?&#x2018;&#x2C6;đ?&#x2018;&#x2C6;

đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;Ě&#x2021; = ďż˝đ?&#x2018;ťđ?&#x2018;ťĚ&#x2021;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021; , đ?&#x2018;źđ?&#x2018;źâ&#x;Š + đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC; where đ?&#x2018;Şđ?&#x2018;Ş = đ?&#x201D;źđ?&#x201D;ź[đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;đ?&#x2018;&#x2021;đ?&#x2018;&#x2021; đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;] is the covariance matrix.

(4)

DATA PROCESSING To perform modal analysis using the DB algorithm, we first assembled the zero mean snapshot data matrix đ?&#x2018;ťđ?&#x2018;ť(đ?&#x2018;Ąđ?&#x2018;Ą): (5) đ?&#x2018;ťđ?&#x2018;ť(đ?&#x2018;Ąđ?&#x2018;Ą) = [đ?&#x2019;&#x2022;đ?&#x2019;&#x2022;1 (đ?&#x2018;Ąđ?&#x2018;Ą) â&#x20AC;Ś đ?&#x2019;&#x2022;đ?&#x2019;&#x2022;đ?&#x2018; đ?&#x2018; (đ?&#x2018;Ąđ?&#x2018;Ą)]đ?&#x2018;&#x203A;đ?&#x2018;&#x203A; đ?&#x2018;Ľđ?&#x2018;Ľ đ?&#x2018; đ?&#x2018; (6) ďż˝ â&#x2C6;&#x2019; đ?&#x201D;źđ?&#x201D;ź[đ?&#x2018;ťđ?&#x2018;ť ďż˝]đ?&#x;?đ?&#x;?1 đ?&#x2018;Ľđ?&#x2018;Ľ đ?&#x2018; đ?&#x2018; đ?&#x2018;ťđ?&#x2018;ť = đ?&#x2018;ťđ?&#x2018;ť

ďż˝ is the snapshot data matrix with đ?&#x201D;źđ?&#x201D;źďż˝đ?&#x2018;ťđ?&#x2018;ť ďż˝ďż˝ â&#x2030; 0. where đ?&#x2018;ťđ?&#x2018;ť Each column in đ?&#x2018;ťđ?&#x2018;ť(đ?&#x2018;Ąđ?&#x2018;Ą) contains the nodal relative brain displacements for all three principal directions for all 183 cases. Therefore, n = number of nodes x 3 degrees of freedom and s = number of snapshots in time. Next, we computed đ?&#x2018;ťđ?&#x2018;ťĚ&#x2021;(đ?&#x2018;Ąđ?&#x2018;Ą) using a second order finite difference approximation. To determine the initial conditions of the evolution equations, we performed a Karhunen-LoĂŠve decomposition (KL) of đ?&#x2018;ťđ?&#x2018;ť(0). Finally, we evolved the evolution equations to determine đ?&#x2018;źđ?&#x2018;ź(đ?&#x2018;Ąđ?&#x2018;Ą) and đ?&#x2019;&#x20AC;đ?&#x2019;&#x20AC;(đ?&#x2018;Ąđ?&#x2018;Ą) using a fourth-order Runge-Kutta scheme and use the r dominant KL modes for the reduction. For analysis of the DB reduction, we plot the eigenvalues of the covariance matrix (modal energy) versus time, relative modal energy versus time, and the two highest energy modes, đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;1 (đ?&#x2018;Ąđ?&#x2018;Ą) and đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;2 (đ?&#x2018;Ąđ?&#x2018;Ą).

RESULTS The eigenvalues (đ?&#x153;&#x2020;đ?&#x153;&#x2020;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; (đ?&#x2018;Ąđ?&#x2018;Ą)) of the covariance matrix đ?&#x2018;Şđ?&#x2018;Ş versus time are plotted for the first 6 ROs in Figure 1a. The common eigenvalues between ROs appear to be very similar for the duration of the simulation regardless of the RO. In addition, there seems to be two distinct groupings of eigenvalues, with the higher energy grouping around 10-10 and the lower energy grouping around 10-12. Furthermore, in Figure 1b, over 90% of the modal energy relative to RO 6 can be captured using a RO of 3. (a) Modal Energy

(b) Relative Modal Energy

Figure 1: (a) Eigenvalues versus time for the first six ROs, each color represents the eigenvalues of a RO. (b) Percent of energy captured by the sum of the eigenvalues for a given RO relative to the sum of the eigenvalues for RO 6.

The two highest energy modes for RO 3 are plotted versus time along with the relative brain displacement data for Case 1 in Figure 2. As the brain displaces following impact in Case 1 (Figure 2a), the highest energy mode (Figure 2b) indicates the highest displacements in a similar region, while the second highest energy mode (Figure 2c) captures high displacements in nearby regions as well.

Figure 2: (a) Relative brain displacement for Case 1 FE simulation (b) Mode 1 with energy added for RO 3 (c) Mode 2 with energy added for RO 3. The modes are plotted with their corresponding energies (ďż˝đ?&#x153;&#x2020;đ?&#x153;&#x2020;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; (đ?&#x2018;Ąđ?&#x2018;Ą) â&#x2C6;&#x2014; đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;đ?&#x2018;&#x2013;đ?&#x2018;&#x2013; (đ?&#x2018;Ąđ?&#x2018;Ą)) since the modes themselves (đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;1 (đ?&#x2018;Ąđ?&#x2018;Ą) and đ?&#x2019;&#x2013;đ?&#x2019;&#x2013;2 (đ?&#x2018;Ąđ?&#x2018;Ą)) are orthonormal.

DISCUSSION Overall, the results suggest that human brain impact dynamics have low dimensional structures in the motion, which could be utilized to build reduced order models of brain tissue deformations in the future. Access to rapid simulations of brain dynamics following head trauma using a reduced order model would provide clinicians with critical information for brain injury assessment [7]. REFERENCES 1. Faul et al. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control (2010). 2. Cassidy et al. J Rehabil Med Suppl 43. 28-60, 2004. 3. Ruff et al. Arch Clinical Neurophysiol 24. 3-10, 2009. 4. Institute of Medicine and National Research Council. Sports-Related Concussions in Youth: Improving the Science, Changing the Culture. Washington, DC: The National Academies Press; 2014. 5. Meaney et al. Clin Sports Med 30. 19-vii, 2011. 6. Bazarian et al. PloS One 9. e94734, 2014. 7. Laksari et al. Phys Rev Lett 120. 138101-138107, 2018. 8. Babaee et al. J Comput Phys 344. 303-319, 2017. ACKNOWLEDGEMENTS Support from the University of Pittsburgh Department of Mechanical Engineering and Materials Science is gratefully acknowledged.

MODELING FLOW OVER AN AIRFOIL USING PROPER ORTHOGONAL DECOMPOSITION Ian Piper and Dr. Hessam Babaee Department of Mechanical Engineering and Material Science University of Pittsburgh, PA, USA Email: iep2@pitt.edu INTRODUCTION For the many scenarios in which it is too difficult or expensive to perform a physical experiment, it is advantageous to compute fluid dynamics simulations as an alternative. However, the cost of running these simulations can be high, as the required time and computational power rises with the complexity and resolution of the problem. To combat this cost issue, reduced order modeling (ROM) is employed, which strives to decrease the complexity of the problem while attempting to retain the quality of the solution [1]. This desirable end is achieved by reducing the dimension of the problem, and therefore the quantity of variables for which computation is required [2]. The nuance of this effort lies in the minimization of error for the effective reduction in computation. An extremely popular method, and the one utilized in this project, is that of Proper Orthogonal Decomposition (POD) [1]. The objective of this project is to investigate the application of POD-based ROM to obtain a lowcost model for solving the flow over an airfoil. Trials were performed with differing numbers of modes, and their effectiveness was compared. METHODS In order to build the reduced-order model, it is first necessary to perform a direct numerical simulation (DNS) of the problem from which to extract the POD modes [3]. The problem in question is the solution of the Navier-Stokes momentum equations (NS) in two dimensions for the flow over an airfoil. The specifics of this simulation are as follows: Re = 500, dt = 5x10-5, Nsteps = 105, Îą = 20o. The result of this simulation is 105 time steps of data, 500 of which are saved as snapshots for the POD code. The POD code takes these snapshots as input, then from them derives the mean and the temporal

covariance matrix, which is deconstructed using singular value decomposition (SVD) to obtain the eigenvalues and eigenvectors [3]. These eigenvectors build the spatial basis functions of the POD, often referred to as the modes. A critical choice at this point is to determine the number of modes that will be used in the model. An infinite number of modes would fully represent the solution, but the choice must be a compromise of accuracy and computation. The modes are ordered from most to least energetic, so with each mode there is less energy captured then the one before [4]. The experiments conducted for this project ran the codes with 2, 3, 4, 5, 6, 8, 10, 12, 14, and 15 modes. The velocity is represented as the mean and the sum of the M modes, as shown below, where um is the mean, ui represents the spatial basis functions, and ai represents the temporal coefficients [4]. (1)

Following the development of the spatial modes, another code uses the equation above as the velocity in a solution of the NS equations, leading to a Galerkin projection onto the spatial basis functions, resulting in an ordinary differential equation in which the goal is to solve for the temporal coefficients ak [2]. This code outputs three matrices which are referred to as the POD coefficient matrices, which serve as coefficients Qkij, Lki, and bk in the ODE shown below [4]. (2)

A ROM code, which was developed for this project, took these matrices as input and applied them to solve the ODE that was previously discussed. The transient portion was ignored, and statistically steady results were collected for analysis.

To analyze the results, the DNS data, which was spatial in nature, was projected to each time step, effectively providing a theoretical temporal coefficient by which to compare the ROM temporal coefficients. Each time the codes were run for a certain number of modes, the temporal coefficient was plotted with the DNS values, and the amplitude and period were assessed. A more mathematically rigorous test was performed by calculating the energy contained in each mode by squaring all the values, integrating the coefficient for each mode, then dividing by the relevant timespan to find the time average (both for the DNS and ROM). For each successive mode, the energy is expected to decrease, and if this is not the case, error is certain. Another characteristic of modes is that they operate in similar pairs, or â&#x20AC;&#x153;sister modesâ&#x20AC;?, in that modes 1 and 2 will have similar energies, as will 3 and 4, and so on. RESULTS The varying of modes used resulted in a surprising difference of result. For 2 modes alone, the energies are 5 orders of magnitude different, but for the 4 mode experiment, there was only a 1% and 2% difference for modes 1 and 2, respectively. For instances where an odd number of modes were examined (3, 5, 15), the last, and odd, mode was always significantly errant. The most successful energy comparison was for the 4 mode scenario, which is represented in Figures 1 and 2. While the discrepancy between ROM and DNS values is extremely small for the first two modes, modes 3 and 4 are still twice the expected value.

Figure 1: ROM vs DNS comparison of Mode 1 temporal coefficient for 4 modes

As the number of modes increased, the factor of difference between ROM and DNS varied, but not necessarily as a function of the number of modes.

Despite this factor difference, the POD energy maintained the same overall shape as the DNS energy as far as 8 modes. Beginning with the 9th mode, the energy begins to increase in both the ROM and DNS results.

Figure 2: ROM vs DNS energy of each mode for 4 modes

DISCUSSION The error in the later modes was investigated through inspection of the original spatial modes. Within these graphs, there were flow structures near the boundary that were atypically significant, more so than the previous modes, which led to the conclusion that these modes were inherently flawed, and anything beyond mode 8 was not considered further. For the experiments with fewer modes, it was expected that the lower modes would be fairly accurate, and then as more modes were added, convergence would be seen. This was not the case, as the error became more significant, and the closest result was in a lower-mode trial. A potential reason for this discrepancy is the corruption of the higher modes due to their low energy. The modes are interdependent, as can be seen in Eq. 2, so any error in their computation would affect the matrices that are used to compute the solution. REFERENCES 1. Noack et al. J Fluid Mechanics 802, 1-4, 2016. 2. Cordier et al. Int J for Numerical Methods in Fluids 63, 269-296, 2009. 3. Taira et al. AIAA Journal 55, 12, 2017 4. Kalb et al. Physics of Fluids 19, 054106 ACKNOWLEDGEMENTS Several codes were computed on Pitt CRC Funding available by the Swanson School and the Office of the Provost

X-projects: hands-on student-run projects Audrey Chester Pitt Makerspaces, I&E Program University of Pittsburgh, PA, USA Email: aec106@pitt.edu INTRODUCTION Real world design team experience is crucial for undergraduates hoping to pursue a career in design post-college. In order to provide students with this precise type of experience, as well as to continue to engender the culture of design and innovation at the University within the existing Innovation & Engineering Program, Dr. William Clark and Brandon Barber spearheaded a new organization of XProjects that match real world clients to student design teams. These XProjects offer students the experience of working in a team of other designers to communicate with a real client on a project that is both relevant and important. This summer, an intern team comprised of Audrey Chester, Daniel Yates, and Emelyn Jaros researched the design process both in the real world and within the XProjects to better contextualize this soon-to-be course and eventually form a curriculum to carry the XProjects forward. METHODS To study the design process within XProjects, the intern team conducted nine projects this summer. The projects consisted of IHITS, Visual Learning Aid, Dear You, Swing Coach, Major League Baseball Arm Tester, Farmbot, Automated Water Tester XCarve, and Lockout. Several of these teams ran under the supervision of one of the summer research interns, while IHITS, Visual Learning Aid, and Dear You were completed by a team made up of only the three interns. Audrey Chesterâ&#x20AC;&#x2122;s primary leadership position this summer was on the Farmbot team. The Farmbot team worked together to assemble phase one of an open source CNC gardening robot for disabled greenhouse gardeners. The first phase consisted of assembling and actuating the robot as instructed on

the open source forum. The second phase, which the team began discussing with the client, is to design modifications to accommodate a hydroponic based greenhouse using NFT rails instead of traditional dirt rows. This team was comprised of four students spanning the undergraduate years and completed the project quickly and effectively. Audrey was also the design lead of Dear You, an interactive exhibit designed to welcome freshmen students to Pitt. The exhibit was mounted in the Forbes Skywalk during Orientation Week and was made up of a case of forty eight birds mechanically engineered to fly that responded to a series of buttons. The heart of the exhibit lied in the pieces of advice the exhibit printed out when participants activated the third button in the line up. These sixty two pieces of advice were curated from people of all ages and majors from across the country and programmed into a library of letters that the exhibit would pull from when the button was pressed (herein lies the origin of the title, Dear You). Audrey was in charge of curating and programming the advice. She was also responsible for the original idea and overall aesthetic coordination of the exhibit. The project occurred over a period of six weeks and gave the intern team an intense learning experience with the design process. Additionally, Audrey was documentation lead for the IHITS and Visual Learning Aid projects, and provided portfolio content for herself and her teammates during and after the completion of these projects. She also participated in the design and assembly of both projects. DISCUSSION Many of the most important discoveries of the summer in relation to the XProjects are specifics:

have the kickoff client meeting only a day before the official proposal contract meeting, focus on both interest and availability when choosing what students to put on a team, be explicit about what the design process is as well as when and how to use it, etc. The culminating “curriculum” devised is the following timeline: 1. Initial team introduction meeting, in which Dr. Clark and Brandon give a brief description of the content of the project and the team members begin to get to know each other. 2. Prior team handoff meeting (not always necessary), in which the previous team passes on documentation, helpful contacts, and existing designs (both in CAD and as sketches) to the new team. 3. Client kickoff meeting, in which the team hosts the client at Pitt to hold an official debriefing meeting to learn the goals and requirements of the project. 4. Proposal contract meeting, in which the team writes an official statement of work to send to the client detailing the goals and timeline for the specific phase of the project they are in. 5. Four to six weekly checkins, either as meetings or emails in which the team provides an overview of their progress as well as proof of documentation to Dr. Clark and Brandon in order to keep the team focused and documenting their efforts. 6. Closing client meeting, in which the team gives their final product to the client and officially finishes that phase of the XProject. The best checkins are those that occur in person with either Brandon or Dr. Clark, but in order to accommodate for varying availabilities Audrey created a worksheet-style email checkin. The most effective teams turned out not to be the teams with the most technical expertise, but the teams most passionate about their project and most willing to ask for assistance when they needed it. The support from Dr. Clark and Brandon is always

at the ready, but only some teams will choose to accept that they need help and seek it out. It is these teams that create the most compelling outcomes, and it is also on these teams that students learn the most. In order to foster this sort of mindset, the intern team worked to create an atmosphere of “learning” rather than “already knowing” with their community correspondences. The XProjects are now phrased as more of a learning experience and less something you become a part of once you already know everything there is to know about design. The intern team increased the XProject community to include eighteen new active members placed on the aforementioned projects. These new members have been trained and given access to the makerspace in B02 and are ready to move up to the Swanson Center for Product Innovation makerspace and shop to continue fabricating and learning design at Pitt. Additionally, the team inspired a new wave of XProject participants with the Dear You interactive exhibit that attracted attention during its week on the Forbes Skywalk. They hope to continue expanding the XProject community of designers at Pitt as the organization continues to grow in order to give more students the experience of real world design groups before they graduate. ACKNOWLEDGEMENTS Audrey would like to thank Dr. Clark and Brandon Barber for this amazing learning experience, as well as the Swanson School of Engineering for the opportunity to participate in the summer research internship. She would like to thank Monica Bell, Canard Brigsby, and Tim Havics for their assistance in making the interactive exhibit a reality. She would also like to thank Jeff and Andy from the Swanson Center for Product Innovation for their help on designing and housing the interactive exhibit. Thank you, lastly, to all of the amazing team members and community members in the XProject community, for being amazing, passionate people willing to help pioneer a new age of design at Pitt.

X-projects: hands-on student-run projects Emelyn Jaros Pitt Makerspaces, I and E University of Pittsburgh, PA, USA Email: emj42@pitt.edu INTRODUCTION In order to introduce more hands-on learning opportunities to the Swanson ecosystem, multiple projects were run and managed this summer. These were used to test and build out the protocols for the X-projects program. X-projects was started and is run by Dr. William Clark and Brandon Barber. Three interns worked closely with them this summer to build out the program. The intern team consisted of Daniel Yates, Audrey Chester and Emelyn Jaros. The summer projects included ones conducted by the intern team, ones directly led by the intern team and ones that ran independently with only intern mentorship. Each team consisted of four students. The goal was to prepare to run more projects and to train more students to safely and effectively use the numerous and powerful resources the Pitt Makerspaces offer. METHODS The projects conducted by the intern team included iHITS. iHits is an adjustable target designed to be used in research to track the movement of stroke patients. The target included interchangeable fixtures which prompt the subject to point, grab and twist (the motions the team is interested in tracking through computer vision). The project is to be used in upcoming studies here at Pitt. The team also developed an open source learning aid for people with macular degeneration, a condition which causes progressive vision loss. The target system helps people who are learning to cope with their vision loss learn how to compensate using their peripheral vision. The design can be replicated with only a home printer and foam board which can be found at common retailers such as Target and Michaels. The design files and instruction will soon be available on Instructables. The team also created an interactive exhibit to welcome the Freshman class. The interactive includes a kinetic sculpture of flying birds and a pair of laser cut wings. There are three buttons built into the base of the display. The first triggers lights,

the second increases the speed of the bird flapping and the third prints an advice letter. The advice letters were curated from people of all ages and are focused on helping Freshman adapt to college. The exhibit was on display to the public in the Forbes sky bridge during orientation. The build shows off the capabilities of the machines that students have access to here in the Pitt Makerspaces. In this project, Emelyn designed the frame that supports the plexiglass display case. She also designed the paneling and the nest design which surrounds the base. Lights were also added to the base. These were programmed to twinkle as their base state and light up sequentially and glow at the touch of the button. She created a locking access door to allow for maintenance and a printer mount which allows for easy removal of the printer. She also designed and fabricated the laser cut wings (each of which measured around four and a half feet in length). Each intern also led separate teams to conduct other X-projects. Daniel ran two projects. The first was a second iteration of a swing coach device. The second is a device to test shoulder strength. This second project is funded by a Major League Baseball team. Audrey ran a project to build an open source farming robot, called a farmbot. This build was the first phase in a larger project that will center around modification to the machine and software. Emelyn Jaros ran a project to build an open source automated water test. The mechatronic tester measures the pH of water samples that it draws in from a tank. This project involved 3D printing parts and soldering a circuit board. Emelyn handled the circuit board aspect of the project and assisted in the mechanical assembly. The design is meant for fish tanks but might be later modified to be incorporated into the Farmbot project. One of the two independent teams is working on assembling an X-carve for the new makerspace that will soon be open in G17. X-carve is a CNC router machine that is capable of complex 3D cuts.

The other independent team is continuing the work of the Lockout team which began during the past academic year. The previous team had developed a system that would only allow trained individuals to use the equipment in the Makerspaces and restrict access to anyone who is untrained or working alone. This summerâ&#x20AC;&#x2122;s team began to modify the design to work with specific machines such as the drill presses. This project, which is scheduled for another phase, will allow the Makerspaces to be more open without the risk of personal injury and machine damage associated with untrained individuals using machinery. DISCUSSION This summer twenty three students were involved with X-projects. They gained valuable hands on experience and work that will make their personal portfolios competitive when they are seeking jobs or internships in the future. Through running and participating in the projects, the team identified ways the Makerspaces can be improved to fit the needs of student teams. The extremely intensive design and build process associated with the interactive project allowed for the Makerspaces to be tested under the demands of heavy use and tight deadlines. This allowed for the identification of needs such as a larger supply of onhand hardware and efficient project storage. It also emphasized the need for a more efficient ordering system, a need the team identified across the board with the projects run this summer and one that is being addressed. The team also experimented with different forms of team communication and organization to develop a system for future projects. Slack proved to be the most efficient messaging platform. Teams also used Teamgantt (a detailed scheduling tool) to manage the workloads and schedules for individual team members. These tools helped projects to run smoothly this summer and will be employed in the future as well. This summer also emphasized the importance of the X-projects helping students learn team management and conflict resolution. Issues are inevitable when teams work on demanding and complicated projects. Effective team management is often needed to save a project. This summer, issues were

handled on an issue-to-issue basis but in the future a more official system will be in place. Issue-to-issue resolution was effective this summer but not feasible as the project continues to evolve. The team is working with a member of Pittâ&#x20AC;&#x2122;s faculty to create a position of a team work mentor who will be responsible for keeping teams working effectively through conflict. This summer the team also offered training to every person involved in the projects who had not already received the training. Six students were fully trained and will now have swipe access to the B02 makerspace. Six additional students have only one final phase of training before they too will have swipe access. The training procedure developed this summer will allow more students to be efficiently trained in the coming academic year. The team also learned about how to work with external clients. This is new experience for most students and can at times be complicated. The variety of clients the summer projects offered gave the team valuable insights into how to structure the client relationship. X-projects is now in a place to run a larger number of projects efficiently. With systems such as weekly check-in reports, a greater number of teams will be able to run simultaneously. Since the majority of these projects were funded by the clients, they provide a cost-effective way to enhance the educational experience offered here while simultaneously supporting research activities. X-projects offer students opportunities that prepare them for upper level design courses and Senior design project. They are learning how manage client interactions, work effectively in interdisciplinary teams, and design and build complex mechanisms and devices. These skills will make Pitt engineers able to take on a wide variety of challenges with confidence and make them yet more competitive in the job and internship markets. ACKNOWLEDGEMENTS The Interactive Exhibit was assisted by Monica Bell who helped with finding a location for the installation. Machining of parts was provided in part by the Swanson Center for Product Innovation. A special thanks to Brandon Barber and Dr. Clark as well for their support and mentorship.

X PROJECTS: HANDS-ON STUDENT-LED PROJECTS Daniel Yates Pitt Makerspaces, I&E Program University of Pittsburgh, PA, USA Email: day37@pitt.edu INTRODUCTION Real world design work is a key part of a wellrounded engineering education. A push to increase accessibility to these types of design projects for engineering students in the Swanson School of Engineering was the main focus of the X Projects Summer Research Internship. X Projects connect real world clients with design problems to student engineering groups that can work through these problems. Clients come both from internal sources within the university and from external sources ranging from companies to individuals. Students tackle a specific design problem in groups of four for a period of about six weeks, receiving guidance from mentors Brandon Barber and Dr. William Clark.

process as well as increase the usage and accessibility of the makerspaces in Benedum.

The work done this summer was to test and formalize the processes of the X Projects by running several projects, trying out different methods in each, and putting the learnings from each project into action. This research internship was led by Dr. William Clark and supported by Brandon Barber, who provided mentorship, resources, and guidance.

The team spent weeks researching and prototyping to explore different adjustable height mechanisms, keeping in regular contact with the client to get approval of various design directions.

Three interns â&#x20AC;&#x201C; Audrey Chester, Emelyn Jaros, and Daniel Yates â&#x20AC;&#x201C; worked together to lead and oversee nine X Projects over the course of the summer. Three of these projects (Dear You, IHITS, Visual Learning Aid) were completed solely by the intern team. In addition, each intern led projects with three students outside the group. Audrey Chester ran the Farmbot Project, Emelyn Jaros ran the Water Tester Project, and Daniel Yates ran the Swing Coach Project and Major League Baseball Team Arm Tester Project. Two projects were done by four students outside of the internship team, with oversight from the interns (X Carve, Lockout). By running multiple projects, the intern team was able to identify improvement points in the X Project

METHODS The two projects led by Daniel Yates were the Swing Coach and the Major League Baseball Team Arm Tester. The Swing Coach Project came from a client who trains baseball players, from children to college athletes. The client already had a rough prototype of an adjustable baseball tee that forces the user to use proper swing technique. The design problem that the student team had to tackle was to improve the design to make it robust and sturdy and explore possibilities for future commercialization.

After finalizing designs, the team ordered parts to build both a four-bar adjustment mechanism and an automatic telescoping mechanism, both of which will be delivered to the client in September 2018 for testing at his training area. The Major League Baseball Team Arm Tester is a project for an undisclosed Major League Baseball Team that needs a repeatable way to test shoulder and arm strength in pitchers, especially those recovering from injury. The current method of testing is done with one trainer holding a handheld dynamometer in various positions so the pitcher can push against it and get a force readout. In order to stabilize and speed up this process, the student team was asked to create a mechanical device that could hold the dynamometer and be adjusted to assist the administration of four different arm tests.

The team is currently developing sketches and prototypes for three potential solutions. These include a freestanding telescoping structure, a wall mounted structure, and a suction cup structure. Further design and development will be done until the team is ready to fabricate and deliver a final product in mid-September 2018. In additional, Daniel took part in three other projects with the rest of the intern team. Dear You was an interactive installation that was designed to welcome new students to Pitt during Freshman Orientation Week. The project consisted of a kinetic sculpture, light and movement interactions, and a receipt takeaway. The receipt takeaway was letters of advice curated from people across the country and across age groups with words of wisdom for incoming college freshman. Danielâ&#x20AC;&#x2122;s primary contribution to the Dear You project was designing and fabricating all of the mechanical motion. The motion, which was mimicking a flapping motion of a flock of birds, was created using eight motors driving eight offset camshafts. Daniel designed the shafts, cams, mounts for the motor, bearings, and the base of each camshaft so that all would be incorporated into the rest of the design. Daniel also designed shaft couplers, chose the proper motors, and mapped and created the electrical setup to power the eight motors and allow the speed to be controlled by a button on the front of the exhibit. After coordinating with administrators, the exhibit was successfully installed during Orientation Week. Visual learning aid was a handheld device designed to help patients with macular degeneration to learn how to read words at small distances. The designed solution was a target system that helped the user to know where to direct their eyes in order to read a series of central letters and words. IHITS was a project done for a research project within the University of Pittsburgh. In order to test and track the movement of stroke victims doing certain tests, the researches needed an adjustable target system that would allow for stroke victims to go through three motions (turning a doorknob,

picking up a soda can, and pointing at a target). Danielâ&#x20AC;&#x2122;s main contribution was designing laser cut shelves that could piece in and out of a larger target system to create adjustability in the final design. DISCUSSION The running of the nine X Projects this summer helped to shed some light on challenges and future improvements for the X Carve program. Team communication proved to be a very important factor in the success of student projects. The students working on the Major League Baseball Team Arm Tester Project had similar availabilities to meet and consistently used the messaging program Slack to communicate, as well as slowly incorporating the scheduling program TeamGantt. This allowed for quicker communication of ideas, more team meetings, and more reliable progress. The Swing Coach team used GroupMe instead of Slack, and had meetings of two people at a time rather than full team meetings. This resulted in miscommunication within the team and slowed progress. Future iterations of X Projects should benefit from team members that have similar schedules and are set up with Slack, TeamGantt, and any other helpful tools for communication. The other very important factor was mentorship. In general, projects did the best when they were in more frequent contact with Brandon Barber and Dr. Clark, who provided expert design feedback and suggestions. This also provided learning experience for the students, and helped direct groups in the right direction while allowing the students to learn and gain valuable experience. The X Projects system would benefit from more of this mentorship, both from past members of X Projects and from experts in their field like Mr. Barber and Dr. Clark. ACKNOWLEDGEMENTS Consulting for the interactive exhibit was provided by Stephen Spencer. Coordination for the installation of the exhibit was helped by Monica Bell, Canard Grigsby, Tim Havics, and Phillip Hieber. The Swanson Center for Product Innovation provided machine work and project consultation.

PURPLE BACTERIAL PROTEIN-SEMICONDUCTOR HYBRID PHOTOELECTROCHEMICAL CELLS AND QUANTUM DOT SOLAR CELLS Alexandra Beebout, Sai Kishore Ravi, Matthew Duff Lab of Dr. Tan Swee Ching; Lab of Dr. Jung-Kun Lee National University of Singapore, Singapore; University of Pittsburgh, PA, USA INTRODUCTION There is no longer any debate over the importance of developing technology for harvesting renewable energy.1-3 A multitude of renewable energy sources are being feverishly studied by researchers around the world. For many, renewable energy is synonymous with solar power. Solar cell research started growing in popularity from the end of the 19th century as our understanding of lightmatter interactions blossomed.4-7 Looking to the modern day, solar panels are widely available and already in use around the world, largely due to a massive drop in production and operating costs per watt.8 The basic operating principles of photovoltaic cells simply require that an electron is released when a photon of sufficient energy strikes an atom, that this electron be separated from its hole, and that these charge carriers move through an external circuit, creating electricity. The most important material in most PV cells is the semiconductor, and the most widely used semiconductor is silicon. The semiconductor is the locus of energy conversion in PV cells: the semiconductor atoms absorb photons and eject electron-hole pairs into the appropriate energy bands.8 The most efficient and stable semiconductors are also the most expensive and often the most environmentally damaging. In the bio-hybrid cells we focused on the use of reaction centers as the light-harnessing compound. Reaction centers are clusters of proteins with different roles held together in a specific orientation on a specialized scaffold structure.11 In our solar cells, the reaction center serves to absorb photons, generate electron-hole pairs, and efficiently separate the charges. There are a series of specialized reactions that take place to ensure that the charges are effectively separated, something that is difficult to achieve in traditional PV cells, often leading to recombination loss.9 These are the initial and most important steps in harvesting light energy, and biological materials carry out these

steps with an incredible quantum efficiency of around 100%.10 In the quantum dot (QD) solar cells, the quantum dots themselves function as the semiconductors. Colloidal quantum dots (CQDs) are nanometer-scale semiconductor crystals capped with surfactant molecules (ligands) and dispersed in solution.12 The QDs are of interest to researchers because they can be tailored to absorb very specific wavelengths of light according to their size.11,12 They are also relatively easy and cheap to produce on an industrial scale, making them an ideal alternative to traditional semiconductors.12 It has been found that their performance can be improved through a ligand-exchange process, which allows them to spread out and absorb a specific wavelength more efficiently.13 In this experiment, we made and compared the properties of QDs produced with and without the ligand exchange process. METHODS For the biohybrid solar cell experiment, three different cells were constructed with the same products, using different protein thickness as an independent variable. First, 2cm by 2cm squares of aluminum-doped zinc oxide were cut. Layers of adhesive and spacer were added interchangeably on the Al-doped side to create a well on the electrode for the protein to reside. The three thickness values tested were 100, 200, and 300 microns. Once the well had been formed, protein was added to the well via pipette, and the sample was placed in an air drying machine. This process was repeated until the dried protein was able to touch the electrode that was placed on top of the well. The back electrode that was added at this point was cut from an N-type doped silicon wafer. Attention was paid to prevent the two electrode surfaces from coming into direct contact. Samples were labelled to indicate their thickness and protein type. Red photosynthetic proteins called RCLHIX+ from a partner lab in Bristol, UK were used. The proteins were isolated as a whole photosynthetic membrane rather than as

isolated photosystems allowing them to be oriented in a consistent fashion with respect to the light source. After the cells were properly constructed, testing was performed. A Keithley 2450 source meter was set to apply current and measure voltage. The conducting sides of the electrodes were connected to the circuit via alligator clamps. The entire cell was placed in a dark environment. When the voltage reached a plateau, a bright halogen lamp located in the dark chamber was illuminated. When the voltage started to decay, the light was switched off. Current and voltage measurements were continuously collected until the voltage once again achieved the level it had exhibited prior to illumination. The CQDs were made according to a procedure described by Margaret Hines and Gregory Scholes at the University of Toronto.11 This procedure was performed one time, and the CDQs produced from it were used for all of the CQD experiments. Multiple trials of the ligandexchange procedure described by Mengxia Liu et. al were performed on the CQDs.13 The QD colloids were tested in a Perkin Elmer Lambda 35 UV/VIS Spectrometer to determine their absorbance spectra. RESULTS AND DISCUSSION Most samples were tested multiple times on different days. Many of the resulting graphs indicate the cells were not stable during the testing period. An example of a stable, predictable graph is shown in Figure 1. Here, the most significant steps in the testing process can easily be discerned. After placing the sample in darkness, it is imperative to wait for the sample to reach a stable, steady-state voltage. This is represented by a plateau of the voltage values, where they appear to stay at a constant value. Once the plateau is observed, the light was activated. This is indicated by an arrow in Figure 1. The voltage increases dramatically once the sample is exposed to a strong, directed light source. It is clear that the proteins are not very stable, as the voltage starts to decay after just a few minutes of illumination. When the absorbance spectra of the QD colloids were measured, it was found that the ligand exchange process yielded less favorable absorbance than the untreated CQDs. This can be noted in Figure 2. The POST1, POST2, and POST3 data all represent absorbance of ligand-exchange treated cells. They have a wider and less specialized absorbance, which is not ideal in CQDs. These

results are not in accordance with the results published by the group who first described the ligand-exchange procedure.13

Figure 1: The results of electrical testing on a model photoelectrochemical cell made using a 100 µm layer of photosynthetic proteins. Illumination is indicated on the graph.

Figure 2: Absorbance spectra of PbS CQDs before (PRE) and after (POST) ligand exchange treatment. REFERENCES [1] Solar Cell Materials: Developing Technologies, ed. G. J. Conibeer and A. Willoughby, John Wiley & Sons, 2014. [2] Advanced Energy Materials, ed. A. Tiwari and S. Valyukh, John Wiley & Sons, 2014. [3] E. W. McFarland, Energy Environ. Sci., 2014,7, 846–854. [4] Weston, E. (1888) US389124, US389125. Newark, New Jersey. [5] Severy, M. (1894) US527377, US527379. [6] Reagan, H. (1897) US588177 [7] Bowser, W. (1899) US598177 [8] Gul, M., Kotak, Y., Muneer, T. Review on recent trend of solar photovoltaic technology. Energy Exploration and Exploitation, 34, pg 485-526 (2016) [9] Jones, M. R. Prog. Lipid Res., 46, pg 56-87 (2007) [10] N. Lebedev, S. A. Trammell, A. Spano, E. Lukashev, I. Griva and J. Schnur,J. Am. Chem. Soc., 2006,128(37),12044–12045. [11] Hines, M and Scholes, G, Adv. Mater. 2003, 15, 1844-1849. [12] Carey, G., Abdelhady, A., Ning, Z., Thon, S., Bakr, O., Sargent, E. Chem. Rev., 2015, 115, 12732-12763 [13] Liu M. et al. Nat. Mater., 2017, 16, 258-263 ACKNOWLEDGEMENTS This research was made possible by the SSOE undergraduate summer research grant, the EJ Slack Engineering Scholarship, and by the help of Dr. Tan and Dr. Lee and all the researchers in their labs.

Modeling and Energy Calculations of Perovskite Methylammonium Lead Iodide Grain Boundaries Philip A. Williamson and Wissam A. Saidi Department of Mechanical Engineering & Materials Science University of Pittsburgh, PA, USA Email: phw17@pitt.edu INTRODUCTION Photovoltaic perovskite materials, such as methylammonium lead halides (MAPbX3, X = Cl, Br, I), have garnered much attention as over the past decade their power conversion efficiency has increased from approximately 2% to over 20%. However, crystalline interfaces, such as grain boundaries (GBs), are known to have an impact on overall cell performance [1]. Computational methods can provide atomistic insight into how these defects behave and their effect on the performance of perovskite photovoltaics. Towards this goal, our group has begun modeling MAPbI3 crystalline grain boundaries to better understand their structure and behavior. Previous research on several materials has shown that there is a correlation between the energy of a grain boundary and its occurrence in a polycrystalline material [2,3]. For example, in their study of grain boundaries in polycrystalline SrTiO3, Saylor et al. demonstrated that lower energy grain boundaries are more common than higher energy ones [4]. The energy of a grain boundary can be attributed to broken or strained bonds that occur when there is a mismatch of atom positions across the grain boundary interface [5]. We employ models based on the coincident-site lattice (CSL) of the MAPbI3 cubic unit cell. CSL based models ensure a periodic structure across the boundaries perpendicular to the GB plane. These models can be classified based on a sigma number (Σ) that serves as an indicator of the density of lattice points along the GB interface; the higher Σ, the lower the density [6]. The sigma number is occasionally followed by miller indices (h k l) to specify which GB plane the model contains or the family of the GB planes. We hypothesize that CSL-based models with lower Σ will have better matching across their GB interfaces. With better matching, the GB energy of these interfaces should be relatively lower.

METHODS Using software provided by Ogawa [6], CSL-based symmetric tilt GB models were constructed with Σ ranging from 1 to 97. An example model is shown in Figure 1 for Σ3 (2 1 1) grain boundary.

Figure 1: Profile view of MAPbI3 Σ3 (2 1 1) grain boundary. The GB plane is perpendicular to the blue arrow while the axis of rotation, specified by position vector in brackets, is parallel to the green arrow (out of page).

Using interatomic potential parameters provided by Caddeo et al. [7], energy minimization and molecular dynamics (MD) calculations were performed using the LAMMPS software package [8]. These calculations included an initial damped dynamics (FIRE) energy minimization followed by 10 MD runs, each with 500k timesteps using 0.02 fs increments. Each MD run was followed by another damped dynamics energy minimization. DATA PROCESSING The energy of three different models, with lengths varying from approximately 48 Å to 80 Å, was calculated for each GB type. With the energy of each model calculated for a single GB type, we plot the total energy vs. the number of atoms, producing plots like those shown in Figure 2. Using these models and their corresponding energies, the GB energy for each GB type was determined according to Equation 1.

Figure 2: GB model energy vs. number of atoms in the supercell. The intercept is proportional to the GB energy.

đ?&#x2018; đ??¸Bulk + 2đ??´ đ?&#x203A;Ľđ??¸đ??şđ??ľ = đ??¸GB .

(1)

Here, N is the number of atoms, EBulk is the energy per atom in an equivalent bulk crystal model, A is the GB interface area, Î&#x201D;EGB is the grain boundary energy, and EGB is the total energy of the GB model. Thus, by fitting our data to a straight line, we can take the intercept of the data trendline, divide it by 2A, and the result will be Î&#x201D;EGB, the calculated GB energy. This is shown in Figure 2 for three GB models. RESULTS AND DISCUSSION Table 1 below shows the calculated GB energies of our selected models. Observing the data, we find a correlation between ÎŁ and GB energy among certain sequences of ÎŁ, such as ÎŁ3 (111) to ÎŁ13 (431). However, there are several exceptions to this trend throughout the entire data set. Table 1: GB Energy Data, GB energy in units of kcal/(mol*Ă&#x2026;2)

Sigma Number (GB Plane) ÎŁ1 (1 0 1) ÎŁ3 (2 1 1) ÎŁ3 (1 1 1) ÎŁ5 (2 1 0) ÎŁ7 (3 2 1) ÎŁ13 (3 -1 4) ÎŁ13 (4 3 1) ÎŁ19 (3 -2 5) ÎŁ21 (4 -1 5) ÎŁ37 (7 4 3) ÎŁ91 (11 6 5)

GB Angle (Degrees) 120 60.0 70.5 36.9 38.2 27.8 148 167 142 50.6 54.0

MD GB Energy 0.45 0.38 0.10 0.20 0.43 0.78 0.90 0.27 0.39 0.43 0.51

DFT CsPbI3 GB Energy 0.01 0.05 0.19 -

In our groupâ&#x20AC;&#x2122;s previous work [9], density functional theory (DFT) calculations were used to calculate the GB energies of ÎŁ3 and ÎŁ5 GBs in the CsPbI3 perovskite structure. DFT is significantly more accurate than the classical forcefield approach employed in our study, but at the same time is computationally more expensive. While both methods predict that a ÎŁ3 GB will be lower in energy

than ÎŁ5, there is disagreement as to which ÎŁ3 boundary. The MD method predicts that the ÎŁ3 (211) boundary will actually have a higher energy than the ÎŁ5 (210) boundary while the ÎŁ3 (111) is predicted to have a lower energy than both. There is a difference of 0.37 kcal/(molĂ&#x2026;2) between the MD and DFT calculations for the ÎŁ3 (211) GB energy. The GB energies of the ÎŁ3 (111) and ÎŁ5 (210) GBs show more agreement between the MD and DFT calculations with the largest difference being 0.051 kcal/(molĂ&#x2026;2). While several factors could explain these differences, such as the different formula units of each perovskite, DFT can be used to verify the present results produced by MD. The results obtained using the classical force fields are very promising considering the simplicity of the approach. Nevertheless, there is still room for improvements for the force field parameters, which would increase their fidelity especially for modeling GB structures. In our future work, we will work in this direction and also in developing new force fields based on machine learning. REFERENCES [1] Ono et al. J. Phys. Chem. Lett. 7 (2016) 4764â&#x20AC;&#x201C; 4794. doi:10.1021/acs.jpclett.6b01951. [2] Saylor et al. Acta Mater. 52 (2004) 3649â&#x20AC;&#x201C; 3655. doi:10.1016/j.actamat.2004.04.018. [3] Kim et al. Scr. Mater. 52 (2005) 633â&#x20AC;&#x201C;637. doi:10.1016/j.scriptamat.2004.11.025. [4] Saylor et al. J. Am. Ceram. Soc. 87 (2004) 670â&#x20AC;&#x201C;676. [5] Porter et al. Phase Transformations in Metals and Alloys, CRC Press, 2016. [6] Ogawa. Mater. Trans. 47 (2006) 2706â&#x20AC;&#x201C;2710. doi:10.2320/matertrans.47.2706. [7] Caddeo et al. ACS Nano. 11 (2017) 9183â&#x20AC;&#x201C; 9190. doi:10.1021/acsnano.7b04116. [8] Plimpton et al. LAMMPS Molecular Dynamics Simulator, (n.d.). https://lammps.sandia.gov/. [9] Guo et al. J. Phys. Chem. C. 121 (2017) 1715â&#x20AC;&#x201C; 1722. doi:10.1021/acs.jpcc.6b11434. ACKNOWLEDGEMENTS This research was made possible thanks to funding from the University of Pittsburghâ&#x20AC;&#x2122;s Swanson School of Engineering and the Office of the Provost. Calculations were carried out using resources provided by the Center for Research Computing at the University of Pittsburgh.

WEARABLE UPPER LIMB ELBOW EXOSKELETON Zach E. Egolf and Dr. Nitin Sharma Sharma Lab: Neuromuscular Control and Robotics Lab, Department of Mechanical Engineering University of Pittsburgh, PA, USA Email: zee6@pitt.edu, Web: http://www.pittsharmalab.org/ INTRODUCTION Approximately 795,000 people suffer from strokes annually, and about 610,000 are first time victims [1]. Stroke leaves survivors incapable of performing various motor functions that are needed for activities of daily living. For survivors whom have lost functional control of their upper limbs, one common symptom is spasticity. Robotic exoskeletons are recently being investigated for rehabilitation of stroke victims. In a study, by Lo et al., three methods were evaluated for rehabilitation; robotic-assisted therapy, an intensive comparison therapy, and the standard physical therapy [2]. The average total cost was $17,831 (robotic-assisted), $19,746 (intensive), and $19,098 (standard). While various exoskeletons exist, leveraging different actuation methods, they are generally large stationary machines [3]. This limits treatments to clinics where such machines are available. Wearable exoskeletons, along with lowering cost, would allow patients to continue their treatment at home and reduce the number of clinical visits. METHODS Objective: The goal of this research project was to develop a wearable upper limb exoskeleton (as Figure 1: A person wearing the final exoskeleton prototype. The final prototype weighed ~ 13N (3lbs) Motor

Aluminum parts

Human Arm 3D printed spacer

Breg Brace

shown in Figure1) for use in the rehabilitation of elbow flexion and extension of individuals suffering from spasticity. Brace Design: The brace was made of a Breg Telescoping Elbow Brace which is used in current physical therapy methods. The aluminum parts were designed by approximating them as cantilever beams with rectangular crosssections. The parts were then 3D printed for test fitting before being machined out of 6061 and 6061 T6 stock. Motor Integration: The motor used was an X816 module developed by HEBI Robotics that contained a series elastic actuator and gear box. In order to lower the complexity, of aligning the centers of rotation of the motor and elbow, a direct drive concept was used. Further the module contained a motor driver and the necessary sensors to gather limb position, output velocity, and output torque. Programming: For initial testing purposes, a simple MATLAB script was developed to control the motor using the HEBI Robotics MATLAB API and standard MATLAB commands. Additionally, communications with the Simulink Real-Time environment were tested to send a sinusoidal input signal to the motor using an analog to digital converter, Raspberry Pi, router, and python script (for generating the motor commands). Testing and Results: The tests were performed on two able-bodied persons. The exoskeleton proved successful in moving both peopleâ&#x20AC;&#x2122;s arms when given a step input signal. The results can be seen in Figure 2. DATA PROCESSING The data shown in Figure 2 was collected using the MATLAB program previously discussed. The command signal was generated using the standard MATLAB API to simulate a step

response. The signal was then sent to the motor driver inside the module. The response data was then gathered by the module itself and accessed using the HEBI Robotics MATLAB API. The data was then plotted against time (units of seconds).

From the American Heart Association. Circulation, 2016. 133(4): p. e38-368

For tests using the Simulink-Real Time Environment, the Simulink model was ran on a Speedgoat Real-Time Target machine. The signal (a sine wave) was sent to a Raspberry Pi 2 using an ADS1115 analog to digital converter. The Raspberry Pi was running a Python script to send position commands to the motor through a router.

[3] – Gopura, R.A.R.C. and K. Kiguchi. Mechanical Designs of Active Upper-Limb Exoskeleton Robots: State-of-the-Art and Design Difficulties. in 2009 IEEE International Conference on Rehabilitation Robotics. 2009.

RESULTS The exoskeleton prototype proved successful in moving two people’s arms. The torques required to move the arm of an able-bodied people, from 11⁰ (carrying angle) to 100⁰, are shown in Figure 2. The final prototype weighed ~13 N (3 lbs). Additionally, it was possible to control the motor using the Speedgoat which means future controllers can be built using the Simulink RealTime environment. DISCUSSION While our exoskeleton is wearable, it still requires a stationary power supply and a wired connection to the Speedgoat. Future research will focus on eliminating that wired connection to the Speedgoat, the router between the Raspberry Pi and motor, and the development of a portable power supply. Further testing with the Simulink Real-Time Environment will also include gathering feedback through integration of a MCP4725 digital to analog converter (DAC). Once the DAC is implemented, an Impedance controller for force control can be built in Simulink. Additionally, the exoskeleton design will be expanded to implement active degrees of freedom for additional joints and incorporate a Functional Electrical Stimulation array. REFERENCES [1] – Writing Group, M., et al., Heart Disease and Stroke Statistics – 2016 Update: A Report

[2] – Lo, A.C., et al., Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke. New England Journal of Medicine, 2010. 362(19): p. 1772-1783.

ACKNOWLEDGEMENTS This research was funded by the Swanson School of Engineering, the Office of the Provost, and Kennametal. Additionally, I would like to thank Dr. Sharma and his lab for their guidance during this summer.

Figure 2: Data collected from two trials.

DESIGN AND FABRICATION OF A FLEXIBLE ELECTRODE ARRAY FOR FUNCTIONAL ELECTRICAL STIMULATION OF THE CALF Thomas Hinds Neuromuscular Control & Robotics Lab University of Pittsburgh, PA, USA Email: tah85@pitt.edu, Web: http://www.pittsharmalab.org/ INTRODUCTION Annually, approximately 17,000 new cases of spinal cord injury (SCI) occur in the United States [1]. SCI can cause partial to complete paralysis of the limbs, which leads to many negative health effects. Functional Electrical Stimulation (FES) is a therapy that produces muscle contractions by delivering electrical current to nerves or muscles via subcutaneous or transcutaneous electrodes. The Neuromuscular Control & Robotics Lab at the University of Pittsburgh combines FES with a walking robotic exoskeleton to mobilize those living with SCI. Most FES therapies use singular cathode/anode pairs that adhere to a users’ skin to stimulate various muscles. This method is not ideal because the placement of the electrodes is time consuming and must be precise in order to avoid stimulating muscles not needed for the desired motion. Incorrect placement can cause user discomfort and rapid fatigue [2]. Additionally, the amount of movements that can achieved with FES is limited by the number of electrodes. The use of smaller electrodes assembled in an array has emerged as a solution to these problems [3]. The placement of an array can vary between uses, because the location of the activated electrode can be shifted as well. Muscle selectivity is also improved with the shrinking of electrodes, as well as the onset of fatigue because of the various stimulation patterns that can be produced. In this study, I investigated various screen printing designs for manufacturing an array of FES electrodes When realized, the array will coordinate with current

FES and exoskeleton systems developed in the lab to provide gait rehabilitation in SCI patients. METHODS For the wearable garment, electrode patterns are applied via screen printing directly to fabric. 100% nylon (Fabri-Quilt) was used as the substrate for printing. The synthetic fibers are smoother than natural fibers such as cotton, thus providing a more consistent surface to print on. For safe and reliable current transmission from stimulator to electrode, multiple layers of ink are used, with the properties described below in Table 1. A flexible dielectric layer is first applied to the substrate to provide a smooth surface for application of subsequent layers, and to insulate the back surface of the garment. The next layer is composed of a highly conductive silver ink. Current travels on this layer from the stimulator to the electrode pads. Finally, a top layer of dielectric is applied over the conductive paths not meant to contact skin, while leaving the silver electrodes shapes exposed. Table 1: Inks and Properties Ink Material Highly conductive CI-1036 silver ink DI-7540 Dielectric ink

Curing 10 min @ 120°C UV light, 0.650 J/cm2

For the electrodes to apply current evenly across the skin, an interface layer of hydrogel (AG2540, Axelgaard) is placed on each electrode pad. The hydrogel adheres to the skin to provide proper contact, and is conductive (1000Ω-cm).

16 square electrodes 15mm in length are spaced 4mm apart. These parameters combined with the 1000Ωcm resistivity of hydrogel ensures muscle selectivity, provides proper current density to decrease discomfort, while maximizing muscle activation depth [4]. Figure 1 shows the final printed electrode array with a hydrogel sheet covering the pads.

Figure 1: Electrode array. Stimulator channels attach on the silver terminals on the right side, and current travels across the blue leads to the square silver electrode pads.

RESULTS As shown in Figure 1, I was able to screen print the electrode array on a nylon fabric. A test of the electrode conductivity was performed. However, the conductivity results were negative. On my further investigation, I found the following process issues. After the application of the top insulating layer and before curing, the silver leads became nonconductive and FES signals were unable to reach the electrode pads. When examined with a Scanning Electron Microscope (SEM), it was determined that improper curing and separation between layers contributed to poor performance. It is believed that these problems are the result of substrate selection: the nylon fabric is not flat enough to produce consistent paths for reliable current transmission.

Figure 2: top: control specimen, vinyl film substrate with base layer of CI-1036 and top layer of DI-7540. Bottom: CI-1036 encapsulated by DI-7540 on nylon fabric.

Figure 2 above shows a cross section of an insulated lead taken at the red line in Figure 1. It is seen that

the curvature of each layer is significant, as compared to a sample of the same inks printed onto vinyl film. This curvature combined with the constant deformation introduces further degradation of current transmission. DISCUSSION The design of a flexible electrode array for FES to produce plantar flexion and dorsiflexion of the ankle. While this function was not tested, previous experiments have shown that these movements can be achieved using FES electrode arrays [5]. In order to produce functional garments, future iterations will require greater quality control in production. Primary work will focus on substrate selection. Inconsistencies in the placement of layers decreases the viability of printing higher numbers of layers which are necessary for both insulation and current transmission. Additionally, the equipment that was used to produce the prints have no control over layer thickness. This parameter is important for ensuring safe and reliable current transmission. REFERENCES 1. National Spinal Cord Injury Statistical Center, Facts and Figures at a Glance. Birmingham, AL: University of Alabama at Birmingham, 2016. 2. L. Z. Popovic et al. "Muscle fatigue of quadriceps in paraplegics: Comparison between single vs. multipad electrode surface stimulation," 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 3. Koutsou et al. (2016). Advances in selective activation of muscles for non-invasive motor neuroprostheses. Journal of NeuroEngineering and Rehabilitation. 4. Andreas Kuhn et al. “Array electrode design for transcutaneous electrical stimulation: A simulation study,” Medical Engineering & Physics, 2009. 5. Malešević J. et al. “A decision support system for electrode shaping in multi-pad FES foot drop correction.” Journal of NeuroEngineering and Rehabilitation, 2017. ACKNOWLEGDEMENTS This project was funded by the Swanson School of Engineering, and the Office of the Provost. I would like to thank Dr. Sharma and Dr. J. K. Lee, Fen Qin, as well as Andrew Holmes and Jeffrey Speakman for their assistance throughout the project.

HEARING AMPLIFIER WITH BUILT-IN INDEX SELECTABLE FILTERS Zihao Huang and Jeffrey S. Vipperman Sound, Structures and Systems Laboratory, Department of Mechanical Engineering University of Pittsburgh, PA, USA Email: zih4@pitt.edu INTRODUCTION Hearing degradation is a major modern social problem. Damage to hearing is often caused by extended exposure to high-amplitude sound sources, such as industrial equipment and excessive loud speakers. Reduced occurrence of noise-induced hearing loss (NIHL) could be realized by shortening the duration or level of exposure to noise or use of worn hearing protection devices. However, those at risk for occupational NIHL often fail to recognize the presence of damaging noise in daily life [1]. Hearing loss can also occur naturally via aging, which is called presbycusis. Hearing loss complicates communication, making it hard to converse with loved ones or fully able to participate in their healthcare. Hearing aids help to improve communication capabilities. Many “hearing aids” are simple devices which amplify all nearby sound. However, such devices are susceptible to feedback and noise amplification, which further complicates effective communication. Additionally, these low-cost devices do not compensate for the unique hearing loss of an individual, nor do they adapt to softer or louder sounds. This primary goal of this research is to develop a custom hearing amplifier which corrects for a specific individual’s hearing loss in all ambient noise conditions. METHODS Hearing-assist devices should be personalized since the magnitude of hearing loss at specific frequencies important for speech is unique to every user. This study corrects hearing for a particular patient who suffer from both noise and age-induced hearing loss: a senior male veteran, aged 85+ years. The patient’s audiogram (an empirical measurement of the extent of hearing loss as a function of frequency) was obtained. This data revealed that the patient experienced moderate-to-severe hearing loss (-56 ~ -70 dBHL) in the frequency range of 125 to 8000 Hz. The specific audiogram results were used to generate the gain of a corrective filter.

An ADAU-1701 (Analog Devices, Inc.) digital signal processor (DSP) was used as a platform for the hearing aid. After analog-to-digital conversion, the digital signal was processed by a root-mean-square lookup table (RMS LUT), which was used to select an appropriate filter shape based on the amplitude of the input signal. Three separate filters are available for amplification, determined by the noise level (sound pressure level, or SPL) in the environment: quiet environments (<55 dB SPL), moderate environments (55 ~ 75 dB SPL), and loud environments (> 75 dB SPL). The use of different filters enables significant amplifications of normalloudness voice in quiet areas, while also preventing excessive noise delivered to the subject’s ears in loud rooms. An index selectable filter construction (ISF) was used to implement the three filters. Some challenges overcome in the implementation of this system included handling and converting different numerical representations within the DSP. Before generating sound to be played to the user, a compressor was added to ensure the output was under a safe threshold of 110 dB SPL. CALIBRATIONS The hearing aid requires a microphone to measure sound from the environment as well as speakers to deliver amplified sound to the user. A commercial, in-ear headset with in-line microphone was used as a practical solution. Measurement of the microphone and speaker sensitivities were conducted to ensure appropriate sound levels were generated by the device. The microphone measurement setup compared the headset microphone to an adjacent, wellcharacterized measurement microphone in free-field conditions. The microphones were placed 1 meter away from a full-range sound source and a 1-point calibration obtained at 1kHz.

A similar 1-point speaker calibration was accomplished using measurement microphones. PROTOTYPE Figure 1 shows the internals of the prototype device, which include the DSP mainboard, a DC boost converter (which generates a power supply of 5 Volts from the AA battery pack), and signal conditioning circuitry for the microphone. Figure 2 shows the shape of the final implemented corrective filter functions for the patientâ&#x20AC;&#x2122;s right and left ears. Figure 1. Prototype device, internal view.

DISCUSSION Figure 2 shows that a simple case (of approximate size 6x3x1 inches) can contain all the required hardware, making the hearing amplifier portable. Future work might focus on selecting a more precise and smaller headset and microphone combination. A more advanced software program could correct for the true dynamics of the microphone and speaker, rather than using a one-point calibration. Additionally, rather than using a large, expensive, general-purpose evaluation board as the main audio processor, a custom circuit could be built to minimize size and cost of the device.

ACKNOWLEDGEMENTS Design, characterization, and fabrication of the device took place in the Sound, Structures and Systems Laboratory within the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh. The authors gratefully acknowledge the support of Dr. Lori Zitelli, Dr. Catherine Palmer, and Christopher Dumm. Project funding was provided jointly by the Swanson School of Engineering, the Office of the Provost, Kennametal Inc., and Dr. Jeffrey S. Vipperman.

REFERENCES 1. Berger et al. The Noise Manual, Fifth Edition, 2003.

Filter configuration 30 22

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30 16 20 10 0

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Figure 2. Filter configuration.

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INFLUENCE OF NITROGEN DOPING ON ELECTROCATALYTIC ACTIVITY OF FeN4 EMBEDDED GRAPHENE Lydia Kuebler, Boyang Li, and Guofeng Wang Laboratory of Dr. Guofeng Wang, Department of Materials Science, Swanson School of Engineering, University of Pittsburgh, PA, USA Email: ljk49@pitt.edu INTRODUCTION Hydrogen fuel cells have become one of the most promising renewable energy technologies, converting hydrogen fuel to electricity and water via electrochemical reactions. Due to the nature of the reaction, the oxygen reduction reaction (ORR) is known to be five times slower than the hydrogen oxidation reaction [1]. Thus, efforts have mainly focused on manipulating the ORR reaction with electrocatalysts to increase fuel cell efficiencies. FeN4, a low-cost alternative to widely used platinum electrocatalysts, is used in fuel cells to speed up the ORR. It has been hypothesized that introducing nonmetal dopants (such as nitrogen (N)) onto graphene, the support for this catalyst, could enhance the chemical reactivity of the FeN4 embedded graphene through modifying its electronic structure. This is due to the inherent partially charged nature of any carbon and nitrogen bond due to nitrogenâ&#x20AC;&#x2122;s higher electronegativity. As the excess valence electrons from nitrogen become delocalized from bonding with the sp2 neighboring carbon atoms, this introduces n-type doping, characteristic of semiconductor materials [2]. Recent experiments found that 4-5 atomic % N lead to the highest performance [3,4] in contrast, others suggested 7.7 at. % to 9.05 at. % N doping to be optimal [5, 6]. This project aims to explore the influence of doping concentration and special configuration of nitrogen in the lattice on electrocatalytic activity of the FeN4 embedded graphene using computational methods. Specifically, the adsorption energies of ORR intermediates will be calculated and used to predict if the reaction is feasible with free energy diagrams and if the catalyst demonstrates performance levels comparable to Pt in terms of limiting potential, which is determined by the smallest energy difference between two steps in the ORR and defines the point at which all reaction steps are downhill in energy.

METHODS Density functional theory (DFT) calculations were performed using the Vienna Ab initio Simulation Package (VASP) software. The generalized gradient approximation (GGA) method functional developed by Perdew, Burke, and Ernzerhof (PBE) was used to perform the first principle calculations for the surface of the active site complex. A 3 x 3 x 1 MonkhorstPack k point sampling was used for the graphene super cell. MODELS The undoped slab was composed of 90 C atoms, 4 N atoms, and 1 Fe atom in a hexagonal lattice viewed from the c unit cell vector vertical to the surface plane, as shown in Figure 1. This creates a 2-D monolayer of graphene with the FeN4 active site fully embedded into the surface. All intermediates were arranged to have the most stable bond and arrangement with Fe. All nitrogen doping was through graphitic substitution while keeping the effects symmetric with the graphene lattice.

Figure 1: FeN4 Undoped Graphene Slab: C atoms are brown, N atoms are light gray, and Fe atom is yellow.

RESULTS To establish a basis for our electrocatalytic performance, the undoped slab adsorption energies for all ORR intermediates were computed. From the adsorption energies of the undoped slab, the limiting potential of the electrocatalyst was found to be 0.695 eV. The free energy diagrams were based off the standard hydrogen electrode with a baseline potential of 0 V (U= 0 V). The free energy change of O2 was obtained from the overall reaction O2 + 2H2 ď&#x192; 2H2O and is 4.92 eV.

Before creating configurations for each doping concentration, the 2N case (two additional nitrogen atoms) was utilized to determine the influence of the placement of nitrogen atoms onto the graphene lattice. It was found that 4.98 Ă&#x2026; was the ideal Fe-N distance for the most stable configuration. This is illustrated in Figure 2 with the best case decreasing the total energy of the slab by 0.15 eV.

Figure 2: 2N Case: Influence of Doping Distance on Total Energy

Next, at least 12 configurations were created for each doping concentration case (2N, 4N, 6N, 8N, 10N). Overall, the 4N case showed the best performance out of all doping concentrations with 4.2 atomic % N. The adsorption energy, Ead = -0.94 eV for O2 and Ead = -0.09 eV for H2O, showing a decrease in energy of -0.04 eV for O2 and an increase in energy of 0.05 eV compared to the undoped slab. The limiting potential was 0.73 eV which is an increase of 0.03 eV compared to the undoped slab. For all the doping cases involved, the O* and OH* was the limiting step for the ORR reaction. In addition to the 4N case, there was an increase in limiting potential for the 2N and 6N cases, with a potential of 0.715 eV and 0.710 eV, and a decrease in limiting potential for the 8N and 10N case, with a potential of 0.692 eV and 0.680 eV seen in Figure 3.

DISCUSSION Based on our results, it can be said that there are two main factors that influence final ORR performance based on the introduction of nitrogen into the graphene lattice. Firstly, the total nitrogen content, or doping concentration, creates a global effect on the surface. Thus, the more nitrogen atoms introduced onto the surface, the more likely the nitrogen will reject the negatively charged adsorbates such as O or OH. Secondly, the distance between iron, the active dblock metal in the active site, and the nitrogen dopants, creates a local effect on the surface. If the nitrogen is too close, the adsorption of intermediates is too strong, but if too far, it is too weak. The 6N case adsorption energy is also influenced by an increase in doping concentration, which most likely explains why its energy is higher than in between the two 2N cases. These competing phenomena result in the overall performance of our FeN4 electrocatalyst seen in Figure 3. REFERENCES [1] Larminie, James, et al. Fuel Cell Systems Explained. Second Edition. John Wiley & Sons. 2003. [2] Lu, Yu-Fen, et al. ACS Nano, Vol. 7. Pg. 65226532. 2013. [3] Stacy, John, et al. Renewable and Sustainable Energy Reviews, Vol. 69. Pg. 401-414. 2017. [4] Videla, Alessandro, et al. Carbon, Vol. 76. Pg. 386-400 2014. [5] Shao, Minhua, et al. Chemical Reviews, Vol. 116. 2016. [6] Liu, Yisi, et al. Int. Journal of Hydrogen Energy, Vol. 41. Pg. 10354-10365. 2016. ACKNOWLEDGEMENTS Special thanks to Swanson School of Engineering, the Office of the Provost, and Kennametal for providing the funding necessary to carry out this research.

Figure 3: ORR Performance with Increasing N doping

ANALYZING RIGHT VENTRICULAR RESPONSE TO SACUBITRIL/VALSARTAN IN PULMONARY HYPERTENSION Claire Tushak, Danial Sharifi Kia, Evan Benza, Dr. Kang Kim and Dr. Marc Simon Vascular Medicine Institute, UPMC Heart and Vascular Institute University of Pittsburgh, PA, USA Email: cmt98@pitt.edu, Web: http://www.vmi.pitt.edu/faculty/simon.html INTRODUCTION Pulmonary arterial hypertension (PAH) is a condition which results in increased blood pressure due to the constriction or dysfunction in the pulmonary arteries, which carry blood from the right ventricle (RV). RV dysfunction has been found to be closely linked to death in patients with PAH [1]. As a result, an improved understanding of how the RV responds to high blood pressure and eventually fails in PAH is necessary to develop treatments aimed to restore normal RV function. The increased pressure load on the right ventricular free wall (RVFW) resulting from PAH is thought to cause both myofibers and collagen fibers to become reoriented and realigned in the longitudinal direction. Tissue also undergoes hypertrophy, having a significantly stiffer response in the longitudinal direction after developing PAH [1]. Sacubitril/Valsartan (Sac/Val) is a dual-acting drug that is used to treat systolic left ventricular heart failure and has shown potential to be used as clinical treatment for PAH [2]. This drug combines an angiotensin receptor blocker valsartan with sacubitril, a specific inhibitor of neprilysin [3]. Sac/Val works in two ways by preventing overactivity of neurohormonal pathways and by increasing regulatory hormones in the natriuretic peptide system [2]. We hypothesize that treatment with Sac/Val in an experimental PAH model induced by pulmonary arterial (PA) banding will prevent remodeling as well as tissue stiffening which may lead to progressive right ventricular dysfunction in PAH [1]. METHODS Under a protocol approved by the University of Pittsburgh Institutional Animal Care and Use Committee, three experimental groups of rats are used in this study: control, PA banded, and treated with Sac/Val. The animals used are male Sprague-

Dawley rats. The animals rest for a week after delivery before beginning experimental procedures. The rats, excluding those in the control group, undergo a PA banding procedure in order to induce an experimental model of PAH. Two pain medications, ketoprofen and buprenorphine, are administered twice daily for three days after the PA banding procedure. Oral medication via gavage is administered daily, or water for PA banded and control groups. Medication or water is administered beginning the first day after the PA banding procedure, lasting for three weeks. Pressure-volume loops are obtained before harvesting the heart, which provides the end systolic pressure as well as contractility of the RV. To maintain tissue viability at the time of harvest, the RV is placed in cardioplegic solution until the sample is prepared for testing. Tissues are kept viable for biaxial testing to determine its stressstrain relationship to gain insights into RV remodeling. The RVFW is cut into a square sample and initial measurements are obtained. Colored beads are placed on the tissue to maintain orientation of the apex to outflow tract. Visual tracking markers are placed onto the epicardium for deformation measurements.

Figure 1: An example of a tissue sample loaded onto the biaxial testing machine.

After initial measurements, the sample is hooked onto the machine and placed in modified Krebs solution, as previously reported [1]. The RVFW undergoes multi-protocol displacement-controlled biaxial testing. The repeatability of the experimental protocol is examined by performing an additional

final set of 1:1 displacement-controlled tests. The final protocol is compared with the initial 1:1 displacement-controlled protocol. Final measurements of mass and dimensions are obtained from the sample. DATA PROCESSING The data from the biaxial testing device is processed by tracking marker displacements from the recorded images and analyzing the stress-strain response of the tissue. Tissue strains are computed from the marker displacements using finite element interpolation. Stress-strain plots are created using an in-house Mathcad (PTC, Needham, MA) code. Our lab will continue to analyze this data in the future with constitutive modeling. RESULTS The end systolic pressure is an indication of the afterload faced by the RV. As shown in Figure 2, the control group consisting of nine rats had an end systolic pressure of 25 ± 3.3 mmHg. The PA banded group with four rats had a mean RV end systolic pressure of 72.5 ± 21 mmHg. The Sac/Val group with two rats had pressures of 50 mmHg and

Figure 2: The mean end systolic pressure of each group of rats studied.

40 mmHg. Figure 3 shows a stress-strain plot of a representative sample from each group of animals studied, obtained after data analysis. DISCUSSION We are able to successfully create a model of PAH and RV pressure overload, and treat with Sac/Val. Additionally, we are able to obtain pressure-volume data as well as biaxial biomechanics of the RV myocardium. The study is ongoing and these are preliminary results. From the end systolic pressures collected, Sac/Val seems to show effectiveness by lowering the pressure substantially from the untreated rats with PAH. Demonstrated by Figure 3,

PAH leads to an increase in both tissue stiffness and anisotropy. Sac/Val treatment in the PAH model seems to reduce myocardial stiffness and

Figure 3: The stress-strain relationships of one representative data set from each group of rats studied.

deformability to levels comparable to the control samples. This data suggests that Sac/Val may be a treatment for PAH capable of reducing RV afterload and preventing adverse RV remodeling, which is felt to be a precursor to RV failure. Figure 3 also shows that the energy required to obtain a certain level of strain on the tissue is significantly increased by PAH. Treatment with Sac/Val decreases the required energy to near-healthy levels in this preliminary analysis of the first animals tested. Data accrual is continuing. While our study is still ongoing, we have collected promising data for the effectiveness of Sac/Val. Sac/Val appears to lower the end systolic pressure, prevent tissue stiffening, and decrease remodeling when compared to control and PA banded samples. These results may allow for Sac/Val to be the basis for future studies and suggests Sac/Val’s potential to be used clinically for PAH treatment to prevent adverse RV remodeling and failure. REFERENCES 1. Avazmohammadi et al. APL Bioengineering 1, 016105, 2017. 2. Menendez et al. Cardiac Failure Review 2, 4046, 2016. 3. Andersen et al. Basic & Clinical Pharmacology & Toxicology 118, 14-22, 2016. ACKNOWLEDGEMENTS We gratefully acknowledge research funding from Novartis and the support for this Summer Research Internship from the Simon Lab, the Swanson School of Engineering, and the Office of the Provost.

METFORMIN SUPPRESSES CATABOLIC ACTIVITY IN RAT ANNULUS FIBROSUS Rahul Ramanathan, Nam Vo PhD, Gwendolyn Sowa MD, PhD Ferguson Spine Laboratory, Department of Orthopaedic Surgery University of Pittsburgh, PA, USA Email: rar122@pitt.edu, Web: http://www.fergusonlab.pitt.edu/ INTRODUCTION Intervertebral disc degeneration (IDD) is closely related to decreased autophagy and heightened inflammatory response in the annulus fibrosis (AF) and nucleus pulposus (NP) cells in the disc. Catabolic factors that degrade the extracellular matrix, such as MMP-1,3 IL-1β, and COX-2, directly contribute to pro-inflammatory responses in both disc cell types. These factors can be triggered through other inflammatory pathways as well as tensile/compressive stresses. Anabolic factors and matrix-building proteins expressed in the intervertebral disc include: collagen I,II, and Aggrecan. An imbalance in the homeostasis of catabolic and anabolic activity has been shown to contribute to aging and disc degeneration. There have been multiple efforts to curtail this imbalance through both therapeutic and preventative measures. Metformin, a synthetic diabetes medication, has been shown to also express pro-autophagy properties and suppression of apoptosis and cellular senescence through the AMPK pathway (D. Chen et al). Developed in 1922, metformin has been the most widely used oral medication for type II diabetes in the United States since the early 21st century. It is chemically synthesized with organic reactions involving dimethylamine hydrochloride and 2-cyanoguanidine. Traditionally, metformin has been used to decrease hepatic gluconeogenesis and increase insulin sensitivity by inhibiting certain mitochondrial pathways in diabetic patients. It has also been noted to inhibit the expression of catabolic factors such as Matrix metalloproteinase (MMP) and cyclooxygenase-2 (COX-2) in nucleus pulposus (NP) cells in the intervertebral disc in response to oxidative stress (tert-Butyl hydroperoxide). However, the literature fails to explore how metformin responds to mechanical stresses in conjunction with chemical cytokines, which may provide a better platform to observe metformin’s potential anti-aging properties.

The objective of this study is to elucidate the effects of metformin on the inflammatory response of rat AF cells in the intervertebral discs in response to stresses. Specifically, we will explore the differences of adding metformin, inflammatory stimulation, and mechanical tensile stress to rat AF disc cells both separately, and in conjunction. METHODS Four Fischer rats (Mean age: 9 ± 2.2 months) were sacrificed and their spines isolated. AF tissue was extracted from L3-L5 and the entirety of the tail and cells were harvested and cultured in F-12 media with 10% Fetal Bovine Serum (FBS) and 1% Penicillin/Streptomycin (PS). Samples reaching 80%+ confluence were split into four groups and passaged into six well plates at 50k-80k cells per well: Control, IL-1β (inflammatory stimulus), Metformin, and Metformin+IL-1β. 48 hours after passaging, 100µM metformin (sourced from SigmaAldrich; 1,1-Dimethylbiguanide hydrochloride -CAS 1115-70-4.) solution was made in 1% FBS and 1% PS. Cells in the metformin groups were then pre-incubated with the 100µM solution for 4h. Following pre-incubation, a 1ng/mL IL-1β solution was added to the appropriate groups for an additional 4h. At t=8h, cells in all groups were lysed with 100:1 dilution of RLT Plus Lysis Buffer:BME and collected in 1.5mL Eppendorf tubes for RNA isolation. Cell media from each group was also collected for Aggrecan fragmentation assays. The QIAGEN RNeasy Plus Mini Kit (QIAGEN, catalog no. 74134) was used to isolate mRNA from cell lysate for all groups, and RNA concentration and quality was obtained. Gene expression profiles of all four groups were measured with RT-PCR. Ratspecific primers for Matrix Metallopeptidase 13 (MMP-13), cyclooxygenase-2 (COX-2), and Aggrecan (AGC) were used, with MMP-13 and COX-2 representing catabolic gene expression, and AGC representing anabolic.

DATA PROCESSING Raw RT-PCR data for each group was analyzed with the ∆∆Ct method for quantifying relative fold gene expression profiles, with the control group normalized at a ratio of 1. One-way ANOVA was utilized for statistical analysis, with Type I error set at α=0.05. RESULTS

Figure 1: Catabolic and anabolic gene expression profiles for all groups. Asterisk indicates significance at p < .05.

The catabolic gene markers COX-2 and MMP-13 both exhibited significant increases (p=.037 and p=.031 respectively) in the IL-1β group compared to Control, indicating inflammatory stimulation. After adding metformin to the inflammatory stimulus, MMP-13 was significantly suppressed (p=.042), while COX-2 trended towards suppression (p=.067). AGC gene expression showed no significance in any of the groups with respect to control. DISCUSSION Metformin exhibits tremendous potential as an antiinflammatory therapeutic agent through suppression of catabolic activity within the intervertebral disc in response to inflammatory cytokines. Many additional experiments are needed to confirm metformin’s response to tensile stress and its mechanism of action. Thus, the next phase of this study will involve applying mechanical cell strain in addition to inflammatory stress to better simulate typical in-vivo stresses of the intervertebral disc. Aggrecan fragmentation assays will also be conducted to measure protein content. REFERENCES [1] Sowa, Gwendolyn et al. “Determination of Annulus Fibrosus Cell Response to Tensile Strain as a Function of Duration, Magnitude, and Frequency.” Journal of Orthopaedic Research (2011): Web. [2] Chen, Deheng et al. “Metformin Protects against Apoptosis and Senescence in Nucleus Pulposus Cells and Ameliorates Disc Degeneration in Vivo.” Cell death & disease (2016): Web. [3] Sowa, Gwendolyn, and Sudha Agarwal. “Cyclic Tensile Stress Exerts a Protective Effect on Intervertebral Disc Cells.” American Journal of Physical Medicine and Rehabilitation (2008): Web.

ACKNOWLEDGEMENTS Extended regards to the Ferguson Laboratory for Orthopaedic and Spine Research, Department of Physical Medicine and Rehabilitation, Swanson School of Engineering, and the Office of the Provost.

KINETIC CHARACTERIZATION OF NITRITE REDUCTION TO NO BY THE MOLYBDOPTERIN ENZYME MARC2 Eric Cecco, Jesus Tejero, Mark T. Gladwin, and Courtney Sparacino-Watkins Human Movement and Balance Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: emc112@pitt.edu, Web: http://www.vmi.pitt.edu/ INTRODUCTION Nitrite is a nitric oxide (NO) precursor that is endogenously produced in human tissues. Endothelial dysfunction is a condition involved in cardiovascular disease that is characterized by a decreased production of NO in the vascular endothelium. New therapeutic strategies using nitrite can enhance NO levels and potentially treat endothelial dysfunction. However, the mechanisms involved in NO production from nitrite are incompletely defined and the enzyme(s) responsible for biological activation of nitrite vary in each tissue. Our lab identified a novel human nitrite-dependent NO synthase, the mitochondrial amidoxime reducing component (mARC) enzymes [1]. The mARC enzymes exist in two forms that are encoded by two separate genes on Chromosome 1. Human mARC-1 and mARC-2 are molybdenum (Mo)dependent oxidoreductases that function as part of a three-enzyme metabolon with cytochrome b5 (CYB5) and cytochrome b5 reductase (CYB5R) (Fig. 1A). Still, the physiological function of the mARC/CYB5/CYB5R metabolon is still unknown. Screening of human vascular cells and lung tissue for both mARC-1 and mARC-2 revealed that mARC-2 is the prominent in the lung, while mARC-1 is undetectable. However, the kinetic parameters of mARC-2 nitrite reduction to NO were not completed defined. Therefore, this in vitro study was conducted to define the mARC-2 nitrite reduction kinetics, as well as determine the effect of oxygen and pH. We hypothesize that the mARC-2 enzyme metabolon would exhibit similar kinetic characteristics to that of mARC-1 and the previously characterized mammalian Mo-dependent nitrite reductases, which generate NO under low pH and low oxygen (hypoxic) conditions. To test this hypothesis, we measured NO production rates using NO chemiluminescence and isolated recombinant

human mARC-2, CYB5, and CYB5R enzymes to determine (1) for affinity for nitrite, (2) effect of pH, and (3) the effect of varying oxygen levels. METHODS Recombinant human mARC-2, CYB5, and CYB5R were isolated and NO formation rates were measured using a Nitric Oxide Analyzer (NOA, Sievers), as previously reported [1]. The mARC2/CYB5/CYB5 metabolon (Fig. 1A) was incubated in 50 mM Bis-Tris buffer with NADH at 37 °C with 1 mM NADH at a range of nitrite concentrations (10 pM to 100 µM) (Fig. 1B), pH (5.9, 6.4, 6.9, 7.4, and 7.9) (Fig. 1C), and oxygen levels (0%, 2%, 21% - balanced with Argon) in the presence or absence of superoxide dismutase (SOD) (Fig. 1D). The nitrite data was fit to the MichaelisMenten kinetic model to determine the affinity for nitrite (Km). Heat denatured mARC-2 enzyme was used as a negative control and human mARC-1 as a positive control. RESULTS AND DISCUSSION First, the mARC-2/CYB5/CYB5 metabolon’s affinity for nitrite was measured by varying the concentration of nitrite and fitting the data to the Michaels Menten kinetic model equation. We found that the NO-formation rates increased with increasing nitrite levels (Fig. 1B) and calculated the mARC-2 metabolon’s affinity for nitrite (Km of 12.0 ± 2.6 mM NO2-). While the measured affinity for nitrite is low, compared to microbial nitrite reductase, it is in agreement with the all human Modependent enzymes (e.g., xanthine oxidase [22.9 mM]; aldehyde oxidase [2.7 mM]; sulfite oxidase [80 mM]; and mARC-1 [9.5 mM]) [2]. Next, the effect of pH was evaluated. Our lab and others have observed that the rate of nitrite reduction to NO is enhanced under acidic pH levels. Thus, the effect of pH on nitrite reduction to NO by

the mARC-2/CYB5/CYB5 metabolon was also measured. We found that NO-production was elevated at low pH (Fig 1C). While the mARC-2 metabolon doesn’t have the same magnitude of change as heme-based nitrite reductases (slope of 1.0), our data is in line with the molybdenum enzymes (slope ~ 0.7).

Finally, the effect of oxygen on nitrite reduction to NO was measured by conducting the enzyme reactions under anoxia (0% O2), hypoxia (2% O2), and normoxia (21% O2). We found that the presence of oxygen strongly inhibits NO formation (Fig 1C). Typically, oxygen inhibits NO formation via a superoxide (O2-·) intermediate, as NO rapidly reacts with superoxide forming peroxynitrite. Therefore, we also tested the effect of superoxide dismutase (SOD) on NO production, finding no significant increase in NO-production (Fig 1C). The ability of oxygen to irreversibly inhibit NO production is specific to mARC-2 metabolon, as the addition of SOD to the mARC-1 metabolon partially recovered NO production. Further studies are needed to determine the mechanism of inhibition and determine if superoxide is involved. CONCLUSIONS This biochemical investigation defined the kinetic properties of nitrite-dependent NO production with human mARC-2 metabolon. The nitrite affinity is modest, but similar to previously reported Mo enzymes. Additionally, the mARC-2 metabolon nitrite reduction to NO is enhanced under acidic (low pH) and anoxic (low oxygen) conditions, also in line with mammalian nitrite-dependent NO synthase. The mechanism of oxygen inhibition and possible role of superoxide is still unclear. Thus, future work will explore the mechanism of oxygen inhibition.

Figure 1 A. Diagram of the three-enzyme metabolon comprised of Cb5R, Cb5, and mARC (1 or 2). Redox cofactors in each enzyme are displayed; flavin adenine dinucleotide (FAD), b5 type heme (b5), and molybdopterin (MPT). NADH oxidation is coupled to nitrite reduction to NO. B. NO-formation by the mARC2 metabolon. Numbers represent the sequential addition of (1) nitrite, (2) 1mM NADH, (3) 4µM CYB5, (4) 1µM CYB5R, and (5) mARC-2 (4.5-6µM). C. Quantified data of log of maximum rate of NO production at increasing pH levels, plotted with linear trend line. D. measured NO formation rate data (n = 4) from experiments performed under anoxia (0% O2), hypoxia (2% O2), or normoxia (21% O2). Assays were carried out at 37ºC in Bis-Tris (50mM) pH 7.4, NADH (1 mM), nitrite (1mM), marc-2 (6 μM), CYB5 (2 μM), and CYB5R (1 μM). Where indicated, superoxide dismutase (SOD) was added to a final concentration of 120 units·ml . -1

Human mARC-2 is a catalytically competent nitritedependent NO synthase which acts at low oxygen and low pH, properties that render it a favorable target for nitrite-based NO replacement therapies. While the physiological function of mARC-2 in not defined, this study provides evidence that mARC-2 is a viable target for vascular-based NO replacement therapy. REFERENCES 1. Sparacino-Watkins, et al., 2. Maia, Moura. JBIC, 2015.

JBC,

2014

ACKNOWLEDGEMENTS I would like to acknowledge the members of the Gladwin laboratory in the Vascular Medicine Institute for helping me with my research, as well as the Swanson School of Engineering for funding me.

The Physiological Role of Mitochondrial Amidoxime Reducing Component 2 Jimmy Zhang, Bin Sun, Sruti Shiva, Mark T Gladwin and Courtney E Sparacino-Watkins Vascular Medicine Institute, Department of Medicine University of Pittsburgh, PA, USA Email: jiz127@pitt.edu, Web: http://vmi.pitt.edu/ INTRODUCTION Mitochondrial Amidoxime Reducing Component 2 (mARC-2) is a novel molybdenum-containing enzyme found in most eukaryotic organisms, including humans [1]. The mARC-2 enzyme is an oxidoreductase which metabolizes a broad range of substrate (e.g., N-hydroxylated amidoxime compounds [2] and nitrite [3]). Still, the physiological function of mARC-2 is unknown. While the exact function of mARC-2 is not clear, studies have suggested a role in lipid metabolism [4, 5]. Interestingly, the mARC-2 knock out (KO) mice, which do not express the protein, revealed decreased body weight phenotype. Our lab has recently established that mARC-2 KO in moderately aged (10-month-old) male mice maintained on a normal chow diet had reduction in body weight and fat mass, lower glucose and insulin levels, with increased feeding, activity, and energy expenditure. Based on the observed phenotype in mice and high expression of mARC-2 in liver hepatocytes [5, 6], we hypothesize that mARC-2 functions in lipid homeostasis (for example, the export and usage of fatty acids) in the liver. To test this hypothesis, we are tested the effect of mARC-2 KO in mice feed a high fat diet (HFD) on body weight and liver lipid accumulation. Additionally, the effect of the presence of the mARC2 protein on mitochondrial dysfunction was observed. METHODS Mouse model of diet induced obesity (DIO): Twenty-four male C57/BL6N wild-type (WT) and mARC-2 knockout (KO) mice were generated from filial heterozygote breeding. The WT and KO littermates were randomly selected to receive a highfat diet (HFD) or low-fat diet (LFD), which contained 60% or 10 % kcal energy from fat, respectively. Of the 24 mice, 14 mice (6 WT, 8 KO)

were feed the HFD and 10 mice (6 WT, 4 KO) were the LFD. Body weights were recorded weekly. All animal experiments were conducted in accordance with institutional animal care procedures at the University of Pittsburgh. Hepatocyte mitochondrial function: Oxygen consumption rate (OCR) in primary mouse hepatocytes was measured using the XF96 extracellular flux analyzer (Seahorse, Agilent). Primary hepatocytes were isolated from 12-week-old mARC2 WT and KO male mice using in situ liver perfusion following published protocols [7]. Briefly, the liver was perfused with wash buffer and subsequently collagenase digestion buffer via the inferior vena cava. The excised liver was then placed in ice cold Hank’s Balance Salt Solution (HBSS), prior to mechanical disruption of the liver. The homogenate was then passed through a cell strainer to remove undigested debris. Differential centrifugation was performed to separate the hepatocytes from other liver cells populations. Primary hepatocytes were seeded at a density of 1 x 104 cells/well onto a gelatin coated seahorse plate for OCR measurements. OCR was measured before and after the sequential addition of DMEM media, oligomycin (2µM), carbonyl cyanide- ptrifluoromethoxyphenylhydrazone (FCCP) (1µM), and rotenone A (1µM) were sequentially added to each well during the OCR assay. Crystal violet assay was used to quantify DNA in each well and used to normalize the OCR data. The OCR data were then used to calculate the basal respiration, ATP production, proton leak, maximal respiration, and non-mitochondrial respiration. All statistical and graphical analysis was accomplished in GraphPad Prism 7. RESULTS AND DISCUSSION

The longitudinal investigation of body weight in WT and maRC2 KO mice revealed that LFD feed mARC-2 KO mice maintained a lower body weights than WT littermates over the course of our investigation (Figure 1). However, the mARC-2 KO mice provided HFD do not maintain a low body weight over the entire 20-week experiment (Figure 1). Significant differences (p-value < 0.01) in body weight among the WT and KO mice were observed between 1 and 10 weeks of HFD exposure, but not at the latter time points.

While the mARC-2 KO mice do gain weight on the HFD, compared to LFD, but this is less than HET or WT on the HFD. Elucidating the role of mARC-2 in lipid metabolism could have important implications in obesity, diabetes, or fatty liver diseases. REFERENCES A.

Figure 1: mARC-2 deletion inhibits HFD induced weight gain in male mice. Male wildtype (WT, blue squares) and knockout (KO, red squares) littermates were randomly provided either HFD (solid squares) or LFD (empty squares). Special diet was started at 9 weeks old. Statistical analysis was performed at each time point to compare the KO and WT mice on HFD.

Primary hepatocytes isolated from the mARC2 KO mice exhibited increased basal and maximal respiration rates, as well as increases in ATP production (Figure 2). We also observed modest improvements in spare respiratory capacity. This data indicates that the mARC2 KO mice have improved mitochondrial function, possibly the mechanism of lean body weight and resistance to DIO (Figure 1); however, more experiments must be conducted to determine the function of mARC-2 in DIO and mitochondrial function. While our study and others have suggested that mARC-2 functions in lipid homeostasis, the exact mechanism is unclear. We have shown that deleting mARC-2 is protective against diet induced obesity.

Figure 2: mitochondrial function in primary hepatocytes is improved with mARC2 KO mice. A. Representative OCR data from mARC2 KO mice (Black) and WT (grey).

1. 2. 3. 4. 5. 6. 7.

Ott, et al. JBIC 20, 265-275, 2015.

Krompholz, et al. JBC 25(11), 2443-2450 2012. Sparacino-Watkins, et al. JBC, 2014. Neve, et al. JBC. 287(9), 6307-6317, 2012. Neve, et al. PloS one 10(9), e0138487, 2015. Su, et al. PNAS 101(16), 6062-6067, 2004. Kawano, et al. MCB, 01601-13, 2014.

ACKNOWLEDGEMENT Funding from the Swanson School of Engineering (JZ) and the Pittsburgh liver research center pilot award (CESW).

Utilizing Miniature Fluorescent Microscopy For In Vivo Calcium Imaging In The Nucleus Accumbens Xhoni Pashaj, Kinglun Li, Terra Schall, and Yan Dong The Learning and Memory Laboratory, Department of Neuroscience University of Pittsburgh, PA, USA Email: xhp1@pitt.edu, Web: http://www.learningandmemory.org INTRODUCTION The nucleus accumbens is a brain region that has a significant role in aversion, motivation, and reward. Neurons in the nucleus accumbens respond to cues from food, sex, addictive drugs, and exercise. Understanding neuronal activities in the nucleus accumbens is important for decoding the processes underlying reward and motivation driven behavior. The newly developed in vivo calcium imaging technique has made it possible to observe the spatial-temporal interactions of neurons. Observing calcium activity in specific neurons could potentially tell us how cells encode information that leads to reward and motivation driven behavior in the nucleus accumbens. In this study we attempted to capture neural activity in rats through calcium imaging utilizing a miniature fluorescent microscope (miniscope) in the nucleus accumbens. METHODS We conducted this study on Sprague Dawley rats. The miniscope itself was built based on the original guidelines of the miniscope project from Mark Schnitzer’s lab at Stanford. The miniscope’s main components were an emission bandpass filter (ET525/50m), excitation bandpass filter (ET470/40x), dichroic mirror (T495lpxr), half-ball lens, MgF2 coated achromatic doublet lens, as well as a GRIN lens. The excitation light came from an LED at a wavelength of 470nm. The emission light was released from the excitation of the neurons during an action potential when injected with a GCaMP6 adeno-associated virus (AAV). We injected 1µl AAV to the nucleus accumbens that contained the GCaMP6m calcium indicator. The animal’s bregma was used as a reference point for the coordinates that located the nucleus accumbens stereotaxically. The AAV injection coordinates were 1.7 anterior-posterior (AP), 1.5 medial-lateral (ML), and -7.5 dorsal-ventral (DV). The GRIN objective lens was implanted on the skull with coordinates 1.2 AP, 1.5 ML, and -7.2 DV.

Isoflurane and ketamine were used as anesthetics during the surgeries, and the rats were under isoflurane when the miniscope was mounted on their heads. The rats were left to wake up from the anesthesia and left to walk in their cages for 10 minutes while the miniscope recorded the neural activity at a rate of 30 frames per second. Results and Discussion A data analysis and processing software from Inscopix was used to locate and analyze the cells in the recordings.

Figure 1. The color highlighted ROIs are the recognized neurons. 41 ROIs were classified as neurons by the Inscopix software, while 79 ROIs were classified as nonneurons.

Figure 2. The baseline for the pixels is the mean of the pixel intensities for each frame. Each data point is the mean of the SNR for each frame. Graph 1, 2, and 4 were classified as neurons because of high SNR values per spike. Graph 3 had random fluctuations and was classified as a non-neuron.

As seen in figure 1, after applying a spatial filter which removes low and high frequency contents,

motion correction which removed frame-by-frame motions, delta F/F which normalizes the pixels, we found regions of interest (ROI) which were later classified as neurons or non-neurons. Figure 2 shows a signal-to-noise ratio (SNR) of the ROIs. Each spike in Figure 2 is an increase of the SNR of ROIs. Clear spikes show neurons firing while random fluctuations typically will not be classified as neurons. We were able of getting a clear view of the nucleus accumbens and clear classifications of neurons and non-neurons. The GCaMP6m AAV calcium indicator reliably detected single neuron action potentials in the field of view. CONCLUSION We were able to detect neuronal activity in the nucleus accumbens through the miniscope system. Recordings were processed and analyzed using the Inscopix software. All ROIs found in the field of view were classified as either neurons or nonneurons. The neuronal activity was recorded in the nucleus accumbens in both spatial and temporal dimensions. We were successful in identifying neurons in the nucleus accumbens using GCaMP6m as our AAV virus, as well as the miniscope for recording the neurons and the Inscopix software for processing and analyzing our data. By combining this in vivo calcium imaging with a training paradigm such as sucrose or drug selfadministration, we can understand how cells encode reward or addiction driven behavior in the nucleus accumbens. ACKNOWLEDGEMENTS Research was conducted at the Learning and Memory Laboratory, at the University of Pittsburgh, PA. Partial funding was awarded from the Swanson School of Engineering.

CONTROLLED RELEASE OPTIMIZATION OF CEFTRIAXONE AND N-ACETYL CYSTEINE FOR TRANSTYMPANIC DELIVERY Katherine Dunkelberger, Liza Bruk and Morgan V. Fedorchak Ophthalmic Biomaterials Laboratory, Department of Bioengineering University of Pittsburgh, PA, USA Email: ked98@pitt.edu, Web: http://www.fedorchaklab.com/ INTRODUCTION Otitis Media (OM), commonly known as middle ear infection, is a prevalent diagnosis for pediatric patients and the most common reason for antibiotic prescription in the United States [1]. Recurrent OM occurs in 14-22% of children under 1 year of age. In children diagnosed with OM before 6 months of age, 60% then experience recurrent OM [2]. Furthermore, the bacterial pathogens of OM readily form biofilms which, through unique metabolic pathways and gene expression, exhibit resistance to immune system clearance and antibiotic treatment [3]. Existing treatments for OM are systemic antibiotics and topical ear drops, both of which require daily dosing, creating risk for inconsistent dosing patient compliance issues. Systemic antibiotics may have harmful side effects, while topical ear drops present low efficiency and difficulty of administration. Development of a controlled release drug delivery system for the middle ear could address the challenges of patient noncompliance, recurrent OM, and biofilm formation by consistently releasing medication at clinically relevant levels from a single administration. This study investigated optimization of hydrolysable polymeric microspheres for encapsulation of ceftriaxone (CFX), an antibiotic used to clear pathogens prevalent in OM, and Nacetyl cysteine (NAC), a mucolytic agent capable of destroying biofilms such as those observed in persistent OM [4]. METHODS This project developed a procedure for the encapsulation of CFX and NAC in poly(lactic-coglycolic) acid (PLGA) microspheres (MS). Encapsulation methods included a double emulsion water-in-oil-in-water (W/O/W) procedure and a single emulsion oil-in-water (O/W) procedure. In brief, for the W/O/W procedure the drug of interest was dissolved in water as the first water phase and the PLGA was dissolved in dichloromethane (DCM)

as the oil phase. These two phases were combined and sonicated to form the first emulsion which was poured into the second water phase, a 2% poly(vinyl) alcohol (PVA) solution (w/w) and homogenized, creating the second emulsion. The double emulsion was transferred into a 1% PVA solution and stirred for 3 hours. Then, microspheres were washed via centrifugation, flash frozen, and lyophilized. The drug release of the microspheres was investigated in vitro by incubating the microspheres in 0.5 mL of PBS at 37Â°C to mimic the conditions of the middle ear. CFX and NAC were also encapsulated by an O/W single emulsion fabrication method. The procedure followed the same steps as the double emulsion procedure explained above except that drugs were dissolved in dimethyl sulfoxide (DMSO) in place of the first water phase. The encapsulation procedure was adjusted by varying the polymer composition and molecular weight, changing the solvent in the inner phase of the emulsion, altering the concentration of drug in the inner phase, and incorporating salt in the outer aqueous phase. Microsphere formulations were tuned to meet design criteria for ideal drug release: clinically relevant dosing levels through a linear release profile over a sufficient time interval. The drug release profiles of microspheres were characterized in vitro using high performance liquid chromatography (HPLC). Microspheres were further analyzed by measuring density, by conducting drug loading assays, and by imaging with scanning electron microscopy (SEM). The encapsulation efficiency (EE) of NAC microspheres was calculated by measuring NAC lost in the washing steps and subtracting that total mass from the mass of NAC loaded.

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release drug delivery designed for the middle ear. The CFX formulation provides an extended consistent release of an antibiotic that may be used to

NAC was detected in the washing fluid with a spectrophotometer by measuring the absorbance of washing fluid after reaction with Ellmanâ&#x20AC;&#x2122;s reagent, a compound that turns yellow in the presence of free thiol groups, in a phosphate buffered solution. RESULTS CFX was successfully encapsulated in PLGA microspheres using a W/O/W double emulsion fabrication procedure; however, 93.5% of the encapsulated CFX was released in a burst throughout the first 24 hours. To meet design criteria, the fabrication procedure was switched to an O/W single emulsion, NaCl was dissolved in the outer aqueous phases to balance osmolality, and the homogenization speed was lowered. These adjustments to the procedure reduced burst release

Figure 1: In vitro release profile of CFX from MS (n=3) over 14 days. Inset: SEM image of CFX-loaded MS (scale bar = 10Âľm).

for CFX to 58.4% and achieved a linear release profile over two weeks (Figure 1). Parameters that were adjusted in the design process of the NAC microsphere formulation included homogenization speed, concentration of NAC loaded, PLGA molecular weight, salt species in the outer aqueous phase, and inner phase drug solvent. NAC encapsulation efficiency was increased from 11.4% to 33.7% (Figure 2). However, no NAC could be detected in drug loading assays using Ellmanâ&#x20AC;&#x2122;s reagent and spectrophotometry as the detection method nor in release assays using HPLC as the detection method. DISCUSSION CFX can be successfully encapsulated in hydrolysable PLGA microspheres for controlled

Figure 2: Encapsulation efficiency of NAC MS formulations. All have an outer aqueous phase NaCl salt balance unless otherwise indicated.

treat otitis media. The CFX release profile indicates sufficient drug levels can be released to clear S. pneumoniae and H. influenzae, bacterial strains prevalent in OM [5]. Future studies will evaluate the cytotoxicity of the microsphere formulations and investigate in vivo efficacy in a chinchilla model of otitis media. NAC encapsulation efficiency was greatest when formulated with an O/W single emulsion procedure. However, NAC was not detectable after encapsulation in PLGA microspheres. Future studies will investigate other controlled release systems for NAC. REFERENCES 1.Gonzales et al. Clin Infect Dis 33, 757-762, 2001. 2. Dagan et al. Lancet Infect Dis 2, 593-604, 2002. 3. Bakaletz. Pediatr Infect Dis J 26, S17-S19, 2007. 4. Dinicola et al. Eur Rev Med Pharmacol Sci 18, 2942-2948, 2014. 5. Thornsberry et al. Antimicrob Agents Ch 43, 26122623, 1999. ACKNOWLEDGEMENTS Thank you to the University of Pittsburgh Swanson School of Engineering, the Office of the Provost, and the Department of Bioengineering, and to the Cystinosis Research Foundation and Dr. Fedorchak for funding and facilitating this research, and to all faculty and staff who make summer research internships at the University of Pittsburgh possible. Thank you to everyone in the Fedorchak lab for your guidance and support, especially Liza Bruk for your mentorship.

NEURAL STEM CELL PROLIFERATION IN THE SECONDARY NEURAL TUBE FOR LIZARDS VERSUS MICE Danielle Danucalov, Ricardo Londono, Megan Hudnall, and Thomas Lozito Center for Cellular and Molecular Engineering, Department of Orthopaedic Surgery University of Pittsburgh, PA, USA Email: dad145@pitt.edu INTRODUCTION Mice do not have spinal cords in their tails and are unable to regenerate their tails, while homozygous mutant HoxB13 mice have a spinal cord in their tail yet they also lack tail regenerative capability. In comparison, the mourning gecko has a spinal cord in their tail and they have tail regenerative capability. As time progresses during embryonic development, the cell survival of neural stem cells in the secondary neural tube differs between lizard and mice with regenerative and non-regenerative capability, and the HoxB13 gene is expressed in the most posterior region of the embryo during embryogenesis [1]. This expression of HoxB13 coincides with the formation of the secondary neural tube in the tail, which the caudal spinal cord derives from [1]. The neural stem cell survival is hypothesized to exist in lizard embryonic tails, but in mouse embryonic tails the amount of neural stem cells should decrease drastically during embryogenesis. This study is important to literature because there is the possibility to determine potential reasons why certain animals have regenerative capabilities. The impact on society includes the potential for bioengineers to develop technology to replicate spinal cord regeneration in humans. METHODS The main tissue sample involved is the embryonic tail collected from various timepoints during embryonic development in both mourning geckos and mice. Embryogenesis was recorded for mourning geckos, and the development of limbs, fingers, tail, and head were compared to that of mice. Comparable timepoints are established, including 1, 2, 4, 6, and 8 weeks for lizard and 8.5, 11.5, 13.5, 15.5, and 19.5 days for mice. 8 weeks and 19.5 days are the maximum embryo timepoint collected, as egg hatching or birth occurs.

HoxB13 homozygous mice are unlike other mice as their tail contains a spinal cord, but they still lack tail regeneration capabilities. The homozygous HoxB13 mouse data is not examined in this paper. A general workflow includes pairing mice, collecting embryos from both lizards and mice, and staining with DapI, Tunel, PHH3, and Sox2. DapI is utilized to detect cells, Tunel to detect apoptosis in cells, PHH3 to detect proliferating cells, and Sox2 is a neural stem cell marker. The fluorescence microscope is utilized to capture images of the crosssectioned tails, and the program Image J is utilized to overlay the images. DATA PROCESSING The HoxB13 gene accession numbers are obtained from NCBI, and input into the program CLC Main Workbench. This program is utilized to compare HoxB13 gene sequences for mRNA and protein between a variety of phylogenetically similar species. RESULTS Lizard neural stem cells persist during embryogenesis, and until adulthood. Mouse neural stem cells do not persist, and these differences are possibly responsible for tail regeneration capabilities.

neural tube for lizards, due to the Tunel stain. This confirms the hypothesis that the neural stem cells in the secondary neural tube do not undergo apoptosis and instead persist. This proves the neural stem cells in the secondary neural tube play a role in regeneration of the tail. We can conclude that these are indeed neural stem cells, as Sox2 is a neural stem cell marker.

Figure 1. Cross-sections of embryonic tails at 20x zoom using a fluorescence microscope. The first row is mourning gecko embryos at 2 week, 4 week and 6 week from the left. The second row includes a mouse embryo at 13.5 days.

It is also important to note that percent homology of mRNA and protein sequences illustrate a low similarity between mouse and lizard, at 52.69% and 54.16% respectively (Figure 2).

A possible reason neural stem cells persist in the secondary neural tube for lizards coincides with the patterning of the HoxB13 gene. Figure 2 illustrates the vast differences between the HoxB13 gene sequence for species that are phylogenetically similar. is only 52.69% homologous between mouse and lizard. Currently we are in the process of creating a homozygous HoxB13 knockout mouse colony. Future studies pertaining to this HoxB13 mouse would be to recreate Figure 1 to include a third row comparing homozygous HoxB13 mice. REFERENCES 1. Kyriakos D. Economides, Science Direct, 2003, 256, 1 ACKNOWLEDGEMENTS I would like to acknowledge Dr. Thomas Lozito and the Lozito Lab at the Center for Cellular and Molecular Engineering, the Swanson School of Engineering, and the Office of the Provost for funding this project.

Figure 2. A. Sequenced HoxB13 gene for the mourning gecko. B. Graphic generated from CLC Main Workbench comparing the percent homology of HoxB13 sequences between species. Dark red and red exemplify high homology, while light red, light blue, blue, and dark blue cells have low homology. The top table represents the homology of the coding region of mRNA, while the bottom is the protein sequence homology.

DISCUSSION We found little apoptotic cells in the secondary

Inhibition of Biochemical Signals Affects Tail Regeneration in Lepidodactylus lugubris Christian DeMoya, Thomas Lozito, Ricardo Londono, Megan Hudnall, Sara Kenese, Danielle Danucolouv Center for Cellular and Molecular Engineering, Department of Orthopedic Surgery University of Pittsburgh, PA, USA Email: cdd32@pitt.edu INTRODUCTION Cartilage damage is a serious injury that humans face. Unfortunately, cartilage is an avascular tissue and thus has a limited chance of repair or regeneration [1]. While medical interventions exist as treatments for cartilage damage, the effectiveness is limited, and the failure rate is high [2]. In contrast to humans, lizards show the remarkable ability to regenerate tails, which include a cartilage tube that resists ossification in place of the original spinal column [3]. This shows that cartilage regeneration is possible in at least some species. While much is known about the regeneration of lizard tails, the biochemical cues that lead to their regeneration are still unknown. We hypothesize that identification of lizard tail regeneration signals will highlight therapeutic targets for improving human cartilage healing. Based on previous work done with wound healing signals, it is hypothesized that matrix metalloproteases (MMP), fibroblast growth factor (FGF), and WNT are necessary for tail regeneration [4, 5].

expression and activity, respectively. During IHC, two antibodies were used to identify two different MMPs, Anti-MMP2 antibody (ab97779, Abcam) and Anti-MMP9 antibody [56-2A4] (ab58803, Abcam). During ISZ, DQ gelatin (D12054, Thermo Fisher) was incubated on tissue sections to identify areas of MMP activity in the regenerating lizard tails. DATA PROCESSING The length and area of each regenerated tail was measured using ImageJâ&#x20AC;&#x2122;s measure function. Excel was then used to calculate the average tail lengths and areas for each group. The averages for each experimental group were compared to

METHODS All experiments were done on the mourning gecko, Lepidodactylus lugubris. To test whether MMP, FGF and WNT have roles in tail regeneration, inhibition studies were conducted. The specific inhibitors used were GM6001 for MMP, SU5402 for FGF, and XAV939 for WNT. For the inhibition studies, lizard tails were cut and daily intraperitoneal (I.P.) injections of the respective drugs listed above were started. Vehicle control groups were paired with each experimental group. After four weeks, the regenerated lizard tails were collected for processing, which included pictures being taken and histological preparation. Immunohistochemistry (IHC) and in situ zymography (ISZ) were used to localize MMP

B. Figure 1. Size comparison between experimental tails and control tails for FGF, WNT and MMP inhibition studies, p < 0.05. A. FGF, WNT and MMP inhibited tails had smaller average lengths than their respective controls. B. FGF, WNT and MMP inhibited tails had smaller average areas compared to their respective controls.

the averages for the corresponding control group. Excel conducted one tailed t-tests to determine the significance based on a p-value of 0.05.

RESULTS Figure 1 shows the difference in average length and average area between each group compared to their control. The FGF, WNT and MMP inhibited tails were found to have decreased average lengths and areas compared to their respective controls. IHC and ISZ were used to localize MMP expression and activity in the regenerating lizard tail. Figure 2 shows the location of both MMP2 and MMP9. MMP2 expression is found in the ependymal tube whereas MMP9 expression is found in the more advanced position of the blastema. MMP activity co-localized with both MMP2 and MMP9 regions. IHC and ISZ was also used to compare control tails to MMP inhibited tails from the inhibition study. Figure 3 shows the differences in structure between the two tails. The MMP inhibited tail has much less MMP signal present. Also, the MMP inhibited tail has a significantly smaller ependymal tube that lagged further away from the leading edge of the regenerating blastema. DISCUSSION The present study showed that it is possible to inhibit key signaling pathways through I.P. injections in the lizard. WNT, FGF, and MMPs were shown to be necessary for proper tail regeneration in lizards. The localization of MMP activity demonstrates the importance the ependymal tube plays in regeneration. It is also evident that remodeling of the extra cellular matrix is an important step during tail regeneration. Future studies should focus on immunohistochemistry protocols for more

biochemical molecules to reveal the origin of other signaling pathways.

Figure 3. Immunohistochemical comparison between A. control tail and B. MMP inhibited tail. Dapi is gray, MMP2 is red, MMP9 is blue and DQ gelatin is in green.

REFERENCES 1. Hunter. Clin. Orthop. Relat. Res. 9, 3–6. 2. Lewis et al. J. Orthop. Sport. Phys. Ther. 36, 717–727. 3. Lozito, and Tuan. Dev. Biol. 399, 249– 262. 4. Lin and Slack. Dev Biol. 2008;316(2):323.-35 5. Gill and Parks. Int J Biochem Cell Biol 40, 1334-1347. ACKNOWLEDGEMENTS This project was funded through the Swanson School of Engineering, University of Pittsburgh Office of the Provost, and the Lozito Lab. This research was done at the Center for Cellular and Molecular Engineering in the department of Orthopedic Surgery.

Figure 2. Immunohistochemistry and in situ zymography reveal the location of MMP activity in the regenerating lizard tail. A. MMP2 activity localized around the ependymal tube. B. MMP9 activity localized in the blastema. C. In situ zymography shows the location of MMP activity. The hollow arrows show where MMP activity overlaps with MMP2 localization and filled arrows show where MMP activity overlaps with MMP9 localization. D. Overlay of MMP2, MMP9, and DQ Gelatin images. E. The MMP inhibitor GM6001 was incubated during the in situ zymogram as a control.

Mammalian Cells injected into lizard tails survive and reconstitute regenerated tissues Sara Kenes, Ricardo Londono, Thomas P. Lozito Center for Cellular and Molecular Engineering, Department of Orthopedic Surgery University of Pittsburgh, PA, USA Email: sak218@pitt.edu INTRODUCTION Lizards exhibit the amazing natural ability to regrow amputated tails, and a branch of regenerative medicine seeks to recreate these healing abilities in humans. Lizards are the only adult organism able to regenerate tissue, due to their blastema- based regeneration. The blastema refers to a mass of cells that contains progenitor-like qualities and can differentiate into a specific type of cell. When a tail is amputated or lost, an apical cap, made of wound epidermis, forms over the tail stump. The blastema forms 7-10 days later underneath the cap, and later differentiates into cartilage, muscle, and the spinal column [1]. Cells that are localized superficial to the stump are incorporated into the blastema and can differentiate into specialized cells. Sonic hedgehog pathway has been shown to be the inducer of differentiation of cartilage in regenerated tail [1]. Lizards and mammals are believed to have a highly sensitive immune system, rejecting any foreign cells. This immune-sensitivity is due mostly in part to major histocompatibility complex molecules (MHC). MHCs are the group of genes that code for proteins found on the surfaces of cells that help the immune system recognize foreign substances [2]. The MHC loci are sensitive to grafts from the same organism and reject grafts from foreign loci [3]. This sensitivity prohibits mammals from incorporating regenerative DNA from other organisms. The MHC molecules fall under the innate immunity category and are the first stage of defense of the immune system. Innate immune cells can differentiate between host cells and foreign cells. Adaptive immunity plays a lesser role, acting as the second line of defense against infection, mostly located in fluids in the organism. Here we test the ability of mammalian cells to survive, proliferate, and differentiate within regenerating lizard tails towards creating a model of

appendage regeneration to be translated to human wound healing. METHODS Lizard (Anolis carolinensis-Anolis) embryonic fibroblasts or human mesenchymal stem cells (MSCs) were labeled with the fluorescent dye CMDiI and injected into the stumps of amputated lizard (Lepidodactylus lugubris- LL) tails. In order to label the cells, they were washed in HBSS to flush any unwanted medium. They were then resuspended in an HBSS- CM-DiI solution for 20 minutes at 37Â°C, followed by 10 minutes on ice, gently flipping the tube every five minutes. The cells were washed again three times with HBSS to prevent unbound CM-DiI from damaging the cell structure. Cells were injected at concentrations differing from 1.2-10 million/animal. Lizards with already regenerated tails were used to provide consistent results in the entirety of the tail. The tails were amputated with a scalpel blade and the cells were injected in the muscle mass surrounding the spinal cord of the fresh stump. The lizards were monitored daily post-injection. Samples were collected from lizards at 4, 7, 12, 14, 22, and 28-day periods of regeneration. The tails were collected and processed the following way: fixed in 4% paraformaldehyde, washed in HBSS three times then decalcified in ethylenediaminetetraacetic acid, washed three times in PBS and sent through a sucrose wash, and flash frozen using tri-methyl butane and dry ice. They were then cryosectioned at 16-micron sections at 25Â°C, and immunostained for expression of macrophage marker CD68, proliferation marker PHH3, and muscle differentiation marker myosin heavy chain (MHC). Regenerated tail tissues were imaged with a fluorescence microscope.

RESULTS The injected cells were able to be isolated, labeled, and injected without dying. CM-DiI did not damage or hinder the cell’s ability to survive or differentiate. All foreign injected cells, including Anolis embryonic fibroblasts and human MSCs were shown to survive and proliferate in regenerating lizard tails, which was not expected. Cells were found incorporated into higher-order cellular structures such as cartilage tubes and muscle. Macrophages were not detected in areas containing the DiI-labeled cells, indicating a lack of rejection. DiI-labelled cells contributed to the most important structure in regeneration-- the blastema.

The function of the lizard immune system becomes unclear with the survival of foreign cells. The innate immune system of the lizards, including MHCs, should prohibit the cells from not only surviving but proliferating as well. In this instance, the adaptive immune system would have minimal impact, as the injected cells weren’t infected, just foreign to the lizard. It is still unclear whether the immune system differs in the tail versus other parts of the lizard. A model has been developed in which xenogeneic cells, including mammalian cells, are introduced into the regenerating lizard tail environment, in which they survive, proliferate, and contribute to regenerated tissues. This model will not only shed light on the process of lizard tail regeneration but will also be used to test the ability of mammalian cells to contribute to appendage regeneration. Moving forward, a Col2 GFP mouse will be used for mammalian cell studies. This will allow for more precise results, since there will be no background noise from labeling the cells. This will also allow for a more finite answer on differentiation of the cells. A sonic hedgehog pathway inducer will also be used to promote differentiation into cartilage. This is to induce chondrogenesis of injected cells. REFERENCES

FIGURE 1: Blue = DAPI, Green = DiI, Red = PHH3, White = MHC. In A/C, muscle in B/D, PHH3 = red. (A) Lizard tail regenerated from stump injected with xenogeneic lizard cells. (B) Regenerated lizard tail injected with MSCs. DiI-labeled cells survived, proliferated, and differentiated into muscle. (C) Anolis cells injected into LL stumps that have survived and proliferated. (D) Human MSC cells which have proliferated in muscle cells.

DISCUSSION Lizards have been proven to not only accept foreign cells but incorporate them into different cellular structures. When injecting lizard cells into mice in the exact way as described, there was no sign of survival, proliferation, or differentiation. This result leads to more questions about the uniqueness of lizards to have no reaction to foreign cells, and what signals initiate regeneration in lizards, but are missing in mammals.

1: Lozito, Thomas P., and Rocky S. Tuan. “Lizard Tail Skeletal Regeneration Combines Aspects of Fracture Healing and Blastema-Based Regeneration.” Advances in Pediatrics., U.S. National Library of Medicine, 15 Aug. 2016, www.ncbi.nlm.nih.gov/pmc/articles/PMC5004880/. 2: Britannica, The Editors of Encyclopaedia. “Major Histocompatibility Complex.” Encyclopædia Britannica, Encyclopædia Britannica, Inc., 15 Mar. 2017, www.britannica.com/science/major-histocompatibilitycomplex. 3: Abbas, Abul K., and Andrew H. Lichtman. Basic Immunology: Functions and Disorders of the Immune System. Elsevier, 2009.

ACKNOWLEDGEMENTS This project was funded through the Swanson School of Engineering, University of Pittsburgh Office of the Provost, and the Lozito Lab. This research was done at the Center for Cellular and Molecular Engineering in the department of Orthopedic Surgery.

Isolation and Comparison of Super-Healing Mouse and Lizard Macrophage Phagocytic Capability Sean P. Tighe, Ashley T. Martier, Danielle Danucalov, Megan Hudnall, Ricardo Londono, Thomas P. Lozito Center for Cellular and Molecular Engineering, Department of Orthopaedic Surgery University of Pittsburgh, PA, USA Email: spt17@pitt.edu INTRODUCTION Lizards are the closest relatives to mammals that show significant tissue regeneration. While significant damage to mammalian tissue leads to scarring, certain lizard species can regrow their tails after amputation. Macrophages have been shown to be necessary for wound healing and tissue regeneration in various animal models, including limb regeneration in axolotls and ear pinna regeneration in the African Spiny mouse [1,2]. Macrophages act as sentinels in the innate immune system and phagocytose, or eat, foreign particles and alert the adaptive immune system to infiltration by pathogens. They originate in the bone marrow as monocytes, but macrophages circulate through the blood and can infiltrate tissues. The aim of this project is to compare the phagocytic capacity in macrophages derived from regenerationcapable super-healing mice and lizards. METHODS Anolis carolinensis were procured from Connecticut Valley Biological Supply Company. We also tested three strains of mice, C57BL/6J (wild type, WT), p21CIP1/WAF1 (P21), and MRL/MpJ (MRL). All mice were procured from Jackson Laboratories. The P21 and MRL mice are both “super-healing” strains which can heal wounds with less scarring than the WT strain, but still lack the ability to regrow entire limbs, and thus are a middle ground between the lizard and the standard mouse models. P21 and MRL have different mechanisms of regeneration; the P21 mutation is related to cell cycle control while the MRL mutation is immunomodulatory. AC tails were amputated distal to the fracture planes and animals were either intraperitoneally injected with clodronate liposomes or PBS liposomes twice weekly. Clodronate liposomes serve to deplete circulating macrophage populations and PBS liposomes serve as a vehicle control. Liposomes are from the Encapsula Nano Sciences Full Macrophage

Depletion Kit. Tails were collected at four weeks past injury and stained with CD68, PHH3, and DAPI to mark macrophages, proliferating cells, and nuclei respectively. Bone marrow derived monocytes were isolated from the long bones in the fore and hind limbs. Cells were plated at 1 million cells/ml in macrophage maturation media and incubated 7 days with media change every other day, leaving only adherent macrophages. Macrophage maturation media consists of: Dulbecco’s Modified Eagle Medium, 10% Fetal Bovine Serum, 10% L929 supernatant, 1% Penicillin/Streptomycin, MEM Non-Essential Amino Acids, HEPES Buffer, L-Glutamine, and 2mercaptoethanol. L929 supernatant was harvested by allowing confluent L929 fibroblasts acquired from the Badylak lab to incubate for two weeks in antibiotic free media; this media was then removed and frozen at -20 ℃ until needed. On day 7, cells were tested for phagocytic capability. Cells were scraped and resuspended at 10^7 cells/ml and introduced to fluorescent, FBS-opsonized beads at 10^7 beads/ml and incubated for 30 minutes at 37 ℃. Beads are from Polysciences, Inc. Cat# 17154. The assay was stopped by addition of 2 ml of ice cold PBS. Cells were then labeled with Ghost Dye™ 510 to label dead cells, then were fixed in paraformaldehyde, stored in bovine serum albumin at 4 ℃, and sent for flow cytometry (FC) using the FACS Aria Cell Sorter. RAW 264.7 macrophages were obtained from the Badylak lab and cultured in standard conditions and used as gating controls for FC. DATA PROCESSING RAW macrophages were analyzed in 4 different groups: -/- no beads and no Ghost Dye™, +/+ beads and Ghost Dye™, +/- beads and noGhost Dye™, and -/+ no beads and Ghost Dye™. This allows the machine to discern between living and dead cells, as well as phagocytic and non-phagocytic cells. These

RESULTS Figure 1, shows 40x fluorescent images of the AC tails collected four weeks post injury. The figure shows that AC tails have CD68+ populations which are reduced in the clodronate-injected animals. PBS injected tails (left) also show stronger staining for PHH3, implicating macrophage involvement in tissue regeneration.

mouse cells may be showing higher fluorescence because of false positive readings from beads stuck to the membrane of the cell. Phagocytic Capability of Naive Macrophages

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groups were sent for FC on two separate dates to test if cells stored longer showed less incorporated beads. At the same time, cells isolated from test animals were also sent for flow, all treated with beads and Ghost Dye™.

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Days between Phagocytosis Assay and Flow Cytometry Figure 2: RAW 264.7 cells lost fluorescence in about 1% of the total population over the course of two days in bovine serum albumin at 4 ℃.

Figure 2 shows RAW 264.7 macrophages lose fluorescence when stored for two days longer. All proceeding experiments underwent FC on the same day as the phagocytosis assay. Figure 3, below, shows that AC cells are less phagocytic than all mice tested, which suggests that the lizard regenerates using a different mechanism than either super-healing mouse. Conversely, the

DISCUSSION Future experiments in this project will undergo several changes in protocol. First, cells will be sent for FC as soon as possible after the phagocytosis assay to ensure that there is minimal release of beads during storage. Second, cells will be stained with CD68 so that data is specifically representative of the cells of interest. Third, cells will be harvested from animals of similar ages. Fourth, the beads currently used will be replaced with pHrodo™ Red E. coli BioParticles™ Conjugate for Phagocytosis which fluoresce in acidic conditions, such as inside of the endosome, but will not fluoresce if the bead is not inside the cell. Finally, the in vivo portion of the project will expand to include mice, and more time points. REFERENCES 1. Godwin et al. “Macrophages are required for adult salamander limb regeneration.” 2013. 2. Simkin et al. “Macrophages are necessary for epimorphic regeneration in African spiny mice.” 2017. ACKNOWLEDGEMENTS Thanks to Dr. Lozito, The Swanson School of Engineering, and the University of Pittsburgh Office of the Provost for funding this research, as well as the Badylak lab for their aid.

KINETICS OF MALIGNANT AND BENIGN MINERAL DEPOSITION IN COLLAGENMIMETIC HYDROGEL MATRICES Nithya Narayanan, Akhil Patel, and Shilpa Sant Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA Email: nin28@pitt.edu Web: http://santlab.pharmacy.pitt.edu/ INTRODUCTION Breast cancer is the most common cause of cancer deaths. Early diagnosis and histological characteristics can help better manage treatment and improve patient outcomes [1]. Microcalcifications that result from pathological mineralization are insoluble deposits of calcium minerals; are also considered as one of the hallmarks of ductal carcinoma in situ (DCIS); and are commonly used as a diagnostic tool in breast cancer [2]. Microcalcifications are of two types: type 1 (calcium oxalate) and type II (calcium phosphate, hydroxyapatite), associated with the benign and malignant breast tumors, respectively. The type of minerals are believed to contribute to the disease progression; however, the extent of their contribution is unknown [1]. We have developed hydrogel matrices to recapitulate the aligned fibrous structure and nano- to microscale hierarchy of collagen, an extracellular matrix protein implicated in cancer cell invasion. The collagenmimetic hydrogel matrices develop type I and type II minerals observed in DCIS when incubated in phosphate and oxalate buffer, respectively. To further understand the role of the microcalcifications in disease progression, the material characteristics of malignant calcium phosphate and benign calcium oxalate deposited in the hydrogels must be understood. Hence, the goal

Figure 1- SEM of mineralized matrices: Type I at day three (a), seven (b), and ten (c). Type II at day three (d), seven (e), and ten (f).

of this project was to study the composition and kinetics of deposition of different types of microcalcifications in the collagen-mimetic hydrogel matrices. METHODS Natural polysaccharides (chitosan and gellan gum) were self-assembled in a microfluidic chamber to fabricate hydrogel matrices to mimic the structure of collagen. These hydrogel matrices were incubated in simulated body fluid (SBF) or oxalate buffer over a period of three, seven or ten days. Scanning Electron Microscopy (SEM) was used to investigate surface morphology of the mineralized matrices and deposited minerals. X-ray diffraction a

Figure 2- SEM of mineralized matrices on day ten of a) Type I minerals b) Type II minerals (XRD) and Fourier Transform Infrared (FTIR) were also used to determine the crystallinity and chemical nature of the minerals, respectively. In addition, we compared the mineral characteristics with those reported in the literature from the breast cancer biopsies with microcalcifications [3]. RESULTS SEM images (Figure 1) confirmed presence of minerals on the surface of hydrogel matrices starting at three days with distinct morphological features appearing around day seven. Figure 1 shows major difference in extent of mineralization for both type I and type II minerals at three, seven, and ten days. Few minerals are visible at day three for both, type I and type II minerals. The individual crystal shapes and edges of type I minerals are more distinct at day seven. Type II minerals also show

greater feature development and are rounder at day ten as opposed to day seven, as shown in figure 2. The XRD (Figure 3) for type I minerals displayed peaks at 31.5º and 45º, which match known peaks X R D o f O x a la t e ( T y p e I M in e r a l)

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Figure 3- XRD Spectra for a) Type I minerals and b) Type II minerals for type I minerals [7]. The XRD for type I minerals is also missing a typical peak at 15º, which may have been obscured by the XRD spectra of the organic scaffold. The XRD of type II minerals displayed peaks at 31.7º and 45.5º across all time points, matching known peaks of hydroxyapatite microcalcifications and confirmed the formation of calcium phosphate crystals found in breast cancer patient biopsies [3]. The XRD peaks for both, type I and type II minerals showed varying intensity across time points, with the least intense peaks at day three and the most intense peaks at day ten. The FTIR for type I minerals showed peaks 1394 cm-1, 1603 cm-1, 3381 cm-1, which correspond to known peaks for type I minerals [5]. The FTIR spectra also showed increasing intensity over time. The FTIR for type II minerals showed a peak at 600 cm-1, 1014 cm-1, 1597 cm-1, 2906 cm-1, and 3403 cm-1, corresponding to known functional groups of type II minerals [6]. The FITR peaks for type II minerals were also in the signature area for calcium phosphate. The FTIR peaks were consistently found across all time points and showed decreased H y r o x y a p a t it e ( T y p e II M in e r a l)

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Figure 4- FTIR spectra for scaffolds calcified with a) Type I minerals and a) Type II minerals

intensity at day three as opposed to those displayed on day seven and ten. CONCLUSIONS There is a visible difference between mineralization at seven and ten days, especially of type II minerals. This indicates that there is significant change that takes place over the entire ten-day period. This is also indicated by the increase in XRD peak intensity in both, type I and type II minerals over the ten-day period. The structure of both types of minerals was confirmed by matching of XRD spectra to reference spectra. FTIR spectra displayed peaks corresponding to functional groups of type I and type II minerals, further confirming the structure of both types of calcifications. In summary, we have developed hydrogel matrices mimicking soft tissue microenvironment of the breast tissue and developed a protocol to deposit microcalcifications in these matrices. Understanding the kinetics and characteristics of the calcifications involved in breast cancer will help us design a more effective protocol for calcifying collagen-mimetic hydrogel matrices. These results will further guide our studies to delineate the mechanistic role of microcalcification in breast cancer progression. REFERENCES [1] Cox and Morgan. Bone 53, 437-450, 2013. [2] Hofvind et al. Acta Radiologica 52, 481-487, 2011. [3] Scott et al. NPJ breast cancer 2, 16029, 2016. [4] Keshavarzi et al. Environmental Geochemistry and Health 37, 377-389, 2015. [5] Shall et al. Cryst. Res. Technol 39, 214-221, 2004. [6] H. Ghelsari et al. Ceramics International 41, 5967-5975, 2015. ACKNOWLEDGEMENTS We thank the Swanson School of Engineering and the Office of the Provost, University of Pittsburgh for Summer Award (NN). The project was supported in part by award number P30CA047904 from the National Cancer Institute, and with support from the Henry L. Hillman Foundation (SS).