Scientia 2021 by baylor_scientia

SPRING 2021

SCIENTIA THE BAYLOR UNDERGRADUATE RESEARCH JOURNAL OF SCIENCE AND TECHNOLOGY

Editor-in-Chief Isha Thapar Student Editorial Board Faith Abraham, Sanjana Ade, Shivani Ayalasomayajula, Sinchana Basoor, Rahel Burchardt, Timothy Domashevich, Joshua George, Tooba Haris, Tiffany Luan, Shawn Merchant, Andrew Munoz, Arvind Muruganantham, Jessica Ngo, Sai Sagireddy, Sam Shenoi, Shubhneet Warar Faculty Review Board Tamarah Adair, Ph.D.; Sarah Kienle, Ph.D.; Panos Koutakis, Ph.D.; Linda Olafsen, Ph.D.; Meredith Palm, Ph.D.; Hugh Riley, Ph.D. Publishing Advisor Rizalia Klausmeyer, Ph.D., Baylor Office of Undergraduate Research Design Team Faith Abraham, Sanjana Ade, Shivani Ayalasomayajula, Sinchana Basoor, Rahel Burchardt, Joshua George, Tiffany Luan, Shawn Merchant, Arvind Muruganantham, Jessica Ngo, Sai Sagireddy Funding and Support Baylor Student Government About the Covers The visual on the front cover of Scientia is a Transmission Electron Microscopy image of a spiral of hair cells in the cochlea. This image was taken in the Simmons Laboratory. The Simmons Lab focuses on the effects of aging and noise on hearing loss, and the role of calcium binding proteins in hair cells.

IN THIS ISSUE

A Letter From the Scientia Editor-in-Chief

Original Research

The Effects of Zero-Valent Zinc Nanoparticles on Zebrafish Embryonic and Larval Development Andrea Santa Cruz, Melinda Coogan, Ph.D.

Oviposition and OlfactometerBased Behavioral Responses in Aedes Aegypti

A Predictive Model Assessing External Suicide Risk Factors at the County Level

Batool Unar Syed, Jason Pitts, Ph.D.

Brandon Cunningham, Hannah League, Madelyn Olivas, Celine Rukiidi, Jonathan Wu

PDD in LEO Data Analysis and PDD 2.0 for Dust Confirmation in Lagrange Points L4 and L5 Davis S. Crater, Anthony Pelster, Jorge Carmona Reyes, Mike R. Cook, Kenneth Ullibari, Truell W. Hyde, Ph.D.

The Effects of Integrative Body-Mind Training on Motor Deficits, Demonstrated through Laparoscopic Task Performance Laxmisanjana Ade, Caleb Eliazer, Nikita Mukkamala, Sanjanaa Senthilkumar, Megan Hudson, Emmie Jenkins, Marty Harvill, Ph.D.

The Impact of a Father’s Adverse Childhood Experiences (ACEs) on the Relationship He Has with the Mother of His Baby Maquela Noel, Dawn Misra, Ph.D.

Discovering Phage Casserole: Using Microbiology Techniques to Isolate an Arthrobacter Bacteriophage Mary Mersereau, Sai Sagireddy, Tamarah Adair, Ph.D.

Phenotypic Behaviors of Fragile X Syndrome in Fmr1 mice on the C57BL/6 Background Strain Savannah S. Senger, Paige Womble, Samantha Hodges, Matt Binder, Suzanne O. Nolan, Andrew Kim, Ilyasah Muhammad, Joaquin N. Lugo, Ph.D.

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An Analysis of Physician Nutrition Through the Use of Laparoscopic Students Sowmya Duddu, Mahita Maddukuri, Abhinav Mehta, Arvind Muruganantham, Meredith Ehlmann, Marty Harvill, Ph.D.

Review Articles

Incompleteness and Not Just Right Experiences: A New Perspective on OCD Nicole Wire

Original Abstracts

The Effects of Photosynthesis and Respiration on Carbon Processing in Small Ponds Lacey Barnes, Robert Doyle, Ph.D.

URSA Award Winning Abstracts

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An Application of DFT for Characterizing the Energetics of HDX for Solvated Glucose Meg E. McCutcheon, Emvia I. Calixte, Emily D. Ziperman, Jamie H. Kim, Elyssia S. Gallagher, Ph.D.

Populating a Vacuum Ultraviolet Spectroscopy Library using Tandem GC/ VUV-MS and Chemometric Deconvolution of Real-World Sample Data Shubhneet Warar, Ian Anthony, Christina Gaw, Touradj Solouki, Ph.D.

Joseph Spear, Sae Hee Choi, Solomon Yared, Meshesha Balkew, Peter Mumba, Dereje Dengela, Gedeon Yohannes, Dejene Getachew, Sheleme Chibsa,Matthew Murphy, Kristen George, Cecilia Flately, Karen Lopez, Daniel Janies, Seth R. Irish, Tamar E. Carter, Ph.D.

Chemistry and Biochemistry

Biology

Bloodmeal analysis of wildcaught Anopheles stephensi in Ethiopia

Family & Consumer Sciences

Understanding the Effect of Shopping on Young Adults with Body Shame and Weight Preoccupation Simar Goyal, Jay Yoo, Ph.D.

A Letter From the Scientia Editor-in-Chief Dear Readers, It is my pleasure to present the 2021 edition of Scientia, the Baylor Undergraduate Research Journal of Science and Technology. The mission of Scientia is to provide our undergraduate researchers with a professional platform to share their research, and encourage other students to participate in the exciting research opportunities offered at Baylor and beyond. This year, Scientia received submissions from a diverse array of researchers, and we were pleased to accept papers from the fields of physics, biology, psychology, and environmental science. I am excited to report that Scientia has made great strides since it was first published in 2014. The journal has grown in both submissions and readership. Through Scientia, we are constantly striving to make the amazing research projects of Baylor students accessible to a wider audience. This year, we conducted video interviews with the student authors which can be accessed through QR Codes attached to each paper. I hope this will provide readers with meaningful insight into each paper. We are also very excited to bring the print version of the journal back after the pandemic prevented the distribution of print copies last year. Furthermore, a novel initiative we launched this year was the Conference of STEM Undergraduate Research Journals (CSURJ). With the aim of enriching the research environment at undergraduate institutions and promoting collaboration between various Texas undergraduate STEM research journals, the Scientia team was proud to virtually host the first conference held on March 27th, 2021. CSURJ 2021 was attended by student editors from universities across Texas including Texas A&M, Texas State, Texas Tech and SMU. This conference has opened an avenue for learning from each other through scholarly discussion and the exchange of ideas. Such interaction is sure to improve the content and presentation of journals at universities across the state of Texas. Scientia remains committed to promoting the work of undergraduate researchers at Baylor. The dedication of the editorial board and the mentorship of our faculty editors has enabled us to review a significant number of submissions and present papers that meet high standards. I am certain that you will enjoy reading these papers and take pride in the amazing work of our Baylor student researchers. -Isha Thapar, Editor-in-Chief

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Original Research

The Effects of Zero-Valent Zinc Nanoparticles on Zebrafish Embryonic and Larval Development Andrea Santa Cruz, Melinda Coogan, Ph.D. Department of Environmental Science, Baylor University, Waco, TX

Abstract Zinc oxide nanoparticles are commonly used as an antimicrobial agent in many consumer products. However, zinc oxide nanoparticle exposure has been shown to cause oxidative stress through the formation of reactive oxygen species, which can potentially contribute to the development of neurodegenerative diseases. Whether these effects are caused by the charged ion or the nanoparticle itself is still in question. This study examines the effects of zero valent zinc nanoparticles (Zn0 NP) on early zebrafish (Danio rerio) development and behavior. Zebrafish were exposed to six different Zn0 NP concentrations between 2-10 days post fertilization (dpf). Endpoints such as length, spinal angle, swim bladder area, and ocular distance measurements were observed. A behavioral assay was conducted observing zebrafish avoidance responses to a red ball. The data analysis demonstrated significant (α=0.05) decreases in zebrafish length and significant increases in ocular distance for the groups exposed to 0.5 ppm and 0.75 ppm Zn0 NP. The behavioral assay did not yield consistent trends and had a low Pearson correlation r value with ocular distance, indicating that the two variables are not correlated. Overall, it is evident that Zn0 NPs have the potential to cause abnormal development in zebrafish; this indicates that Zn0 NP exposure may play a role in the progression of neurodegenerative diseases in other animals, including humans.

Introduction Nanotechnology has achieved great momentum in research and industry applications. Metal nanoparticles (NP) have been applied in medicines, cosmetics, industry, agriculture, and are found in many consumer products because of their attractive antimicrobial properties (Sirelkhatim et al., 2015). The antimicrobial effects are due to the ability of the positively charged metal nanoparticles to disrupt the negatively charged bacterial cell walls (Sánchez-López et al., 2020). However, their ubiquity in the environment and consumer products has raised concerns in regard to human safety and health. The high surface area of metal nanoparticles makes them conducive to many oxidative reactions causing the production of reactive oxygen species (ROS) (Sánchez-López et al., 2020). These reactions result in oxidative stress, which can degrade cells and potentially cause neurodegeneration (Bai et al., 2009). Zinc Oxide nanoparticles (ZnO NP) induce developmental and hatching retardation, tail malformation, and mortality in zebrafish (Bai et al., 2009). These acute toxic endpoints and developmental abnormalities can be explained by observed ZnO NP-induced ROS formation and apoptosis (Zhao Xuesong, Rong, Zhouying & Baixiang, 2016). Genetic studies further indicate that zebrafish experience alterations in gene regulation in about 689 genes post-ZnO NP exposure. Six genes (aicda, cyb5d1, edar, intl2, ogfrl2 and tnfsf13b) are directly associated with the exposure to ZnO NP and code for inflammation and immune response in zebrafish (Choi, Kim, Yoon, & Kim, 2016).

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Other Zn compounds, such as ZnCl2, also appear to have similar adverse effects on the body. It has been shown that ZnCl2 can cause cellular damage from the generation of ROS and produce Alzheimer’s Disease-like symptoms (Sarasamma et al., 2018). With the similarities between various zinc-containing compounds, the literature indicates that the toxicity of ZnO NP is possibly mainly due to the dissociated Zn(II) ions, as opposed to the nanoparticles themselves (Brun, Lenz, Wehrli, & Fent, 2014). The elimination of the charged zinc ion yields a zero valent zinc nanoparticle (Zn0 NP). The use of Zn0 NP has provided promising results for the promotion of neuroglial cell proliferation and nerve regeneration. Zinc is an essential element in biological processes and also conducts electricity and transmits neural signals, making it a worthy candidate as a nerve treatment technique (Aydemir Sezer et al., 2017). However, the toxicity of the compound has yet to be sufficiently studied. Zebrafish are a model species for understanding biological concepts in diverse disciplines such as genetics, medicine, toxicology, development, and behavior. Zebrafish are physiologically similar to humans and contain about 70% genome similarity to humans (Howe et al., 2013). Thus, the research conducted on zebrafish can be effectively applied or translated. In addition, zebrafish are model organisms due to their relatively easy maintenance, rapid development, and

Materials and Methods 200 zebrafish embryos were procured from Carolina Biological. Additional embryos were ordered as contingency to ensure enough were of appropriate quality for the study. The aquarium water was prepared by placing 1.2 g Instant Ocean Salts and 10 mL Methylene Blue in 20 L DI water and housed in a 20 L carboy. Aquarium water was monitored to ensure that pH levels remained within the optimal range of 7.4-7.5 and bubbled with an aerator (Aquarium Systems, Mentor, OH) in a 14 hr/10 hr light dark cycle. The two groups of 96 zebrafish embryos were separated into eight sets of replicate six-well plates with four embryos per well. Each well contained 10 mL of aquarium water. Embryos were exposed to six dosing concentrations of Zn0 NPs at 2 dpf. Dosing exposures were renewed daily and measurement of light, pH, and temperature were taken prior to experimentation. Zebrafish Protocol for Zn0 NP in well plates (2dpf – 5 dpf) The 2 dpf-5 dpf dosing protocol took place in eight six-well plates. The stock solution used for dosing consisted of 50 mg of

Zn0 NP powder in 1 L of aquarium water to yield a stock concentration of 50 ppm. The stock solution was kept on a stir plate to minimize particle aggregation. Six 150 mL beakers were labeled with the corresponding dosing concentrations (0 ppm, 0.25 ppm, 0.5 ppm, 0.75 pm, 1.0 ppm, and 1.25 ppm). Then, the appropriate amount of aquarium water was removed from the beakers and the appropriate amount of Zn0 NP stock solution was added to yield the correct dosing concentrations. Zebrafish embryos were transferred to the wells and the plates were left uncovered for optimal oxygen exchange. Zebrafish Protocol for Zn0 NP in beakers (6 – 10 dpf) The same dosing concentrations were used as the 2-5 dpf protocol. Six 500 mL beakers were filled with 300 mL of aquarium water from the carboy. The appropriate amount of aquarium water was removed from the beakers and the Zn0 NP stock solution was added to yield the correct dosing concentrations. These six 300 mL solutions were further divided into three to yield a total of 18 100 mL solutions. (labeled as A1-6, B1-6, and C1-6). The zebrafish were fed with a Ziegler zebrafish pellet feeding solution prior to being transferred. After feeding, the zebrafish were transferred into their respective beakers. Group A and Group B beakers were photographed in an alternate fashion. Group C beakers were not photographed and were set aside for tissue splicing research for a related study. Photographs were taken using a light microscope and microscope camera to document morphological changes due to the exposure. The zebrafish from the correct alternating group were transferred to 24-well plates for observation and photography under the light microscope. Zebrafish that displayed hyperactive activity were difficult to observe and were treated with 3-5 drops of a buffered solution of 15-50 mg/L MS-222 Tricane Methanesulfate. Quantification of photographed morphological differences including spinal angle, swim bladder/yolk sack area, length, and ocular distance were made using ImageJ. The “image type” settings on the software were set to eight bit. The magnification level used throughout the study was 2.0, so the pixels were set to 764 pixels/mm. The protocol was repeated each day between six and ten dpf. The dosing concentrations were remade, fish were fed and then transferred to beakers, and pictures were taken. Health indictors such as presence of infections or abnormal heart function were examined under the microscope. Zebrafish that were deemed unhealthy or dead were recorded and placed in quarantine. Zebrafish Protocol for Zn0 NP Behavioral Assay (11-12 dpf) This assay did not involve the use of stock solutions and no photos were taken. Two sets (Groups A + B, and Group C) of six 150 mL beakers (12 total beakers) were labeled with the same number sequences as used for the previous dosing concentrations. Beaker groups A and B were combined into one group (Group A + B), and group C remained in a separate beaker (Group C). Only the fish in the Group A + B were used in the behavioral assay. A large stir bar, red magnetic ball, and six square petri dishes were obtained. These petri dishes were divided into four equal

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Original Research

embryonic transparency (Khan & Alhewairini, 2018). After about 1 day post fertilization (dpf), the embryo has almost completely formed organ systems and will hatch within the following 2-3 days (Singleman & Holtzman, 2014). At this point, the transparent embryo allows researchers to observe the developing organ systems and blood flow with ease (Khan & Alhewairini, 2018). This is followed by the larval stage, which lasts ~6 weeks. During this stage, fins, pigment patterns, and general body morphology are developed. The juvenile stage is reached at about 45 dpf and is followed by sexual maturity at about 3 months (Singleman & Holtzman, 2014). In addition to morphological and genetic analysis, zebrafish can also be used to assess the effects of cognitive function and brain developmental through behavioral assays. For example, a morphological change such as an increase in zebrafish eye distance, due to reduced surface area of the eyes, could indicate possible brain development abnormalities (Lutte et al., 2015). This physical manifestation of brain toxicity can be observed through zebrafish anxiety and avoidance behaviors as well. Zebrafish perceive the color red as “dangerous” and will tend to avoid red objects under normal developmental conditions. However, zebrafish that have experienced developmental toxicity will fail to exhibit this avoidance behavior (Collwill & Creton 2011). This study examines the neurodegenerative effects of Zn0 NP on zebrafish by observing morphological abnormalities and behavioral activity throughout embryo and larval stages of development. Key traits associated with development were measured, such as swim bladder area, spinal angles, and eye distance, to determine potential toxicity. We hypothesized that with increasing Zn0 NP concentrations, zebrafish will exhibit neurodegenerative effects in increasing severity via angled spines, undeveloped swim bladders, greater eye distance, and a lack of avoidance behaviors. This study can be potentially significant in determining contributors to the development of neurodegenerative diseases such as Alzheimer’s and Parkinson’s Disease in humans.

Original Research

quadrants and the quadrants were labeled A, B, C, or D (Figure 1) on the bottom of the dish. The six petri dishes corresponded to the six dosing concentrations used throughout the study. About 70 mL of water were added to each petri dish.

Figure 1: Behavioral Assay Methods Diagram

Eight zebrafish from Group A+B were transferred to their respective petri dish concentration groups. The red ball began in quadrant A. Data was recorded in a table separated by Time/Quadrant and Beaker. For every minute up to five minutes, the number of fish in each quadrant (A, B, C, and D) were counted and recorded on the data sheet. At the end of five minutes, the stir bar was used to move the red metal ball to the next quadrant in a clockwise fashion and the assay began again. Data was expressed in terms of fractions to demonstrate the number of zebrafish that were in the quadrant with the red ball out of the total fish in the group. The data obtained from ImageJ and the behavioral protocol were analyzed using data analysis tools in Excel. F-tests were conducted between all groups (0 ppm vs 0.25 ppm, 0 ppm vs 0.5 ppm, 0 ppm vs 0.75 ppm, 0 ppm vs 1.0 ppm, and 0 ppm vs 1.25 ppm) for each variable (α=0.05). Data sets with p values greater than 0.05 were analyzed using a t-test with unequal variances, while those with p values less than 0.05 were analyzed using a t-test with equal variances. A Pearson correlation analysis was conducted between ocular distance and zebrafish avoidance behaviors.

Results

Length (mm)

We demonstrate a significant reduction in zebrafish length, specifically for Zn0 NP concentrations of 0.5 ppm and 0.75 ppm (Figure 2, p values of 0.0060 and 0.0046 respectively). The average significant length reduction was 0.3 mm. The greater dosing concentrations did not appear to cause stunted zebrafish growth.

3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2

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Figure 2: Average length of the zebrafish 6-10 days post ferferfertilization (t-test; α=0.05) The zebrafish did not develop spinal angles that significantly differed from the controls (Fig. 3). The average spinal angles appear to range between 173 degrees and 177 degrees.

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Original Research

176

Angle (˚)

174 172 170 168

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Figure 3: Average spinal angles of zebrafish between 6-10 days post fertilization (t-test; α=0.05). The results for swim bladder area also demonstrated a lack of significance (Fig. 4). The areas were relatively uniform throughout all dosing concentrations. 0.12

Area (mm2)

0.1 0.08 0.06 0.04 0.02 0

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Figure 4. Average swim bladder area of zebrafish between 6-10 days post fertilization (t-test; α=0.05).

Distance (mm)

Ocular distance yielded inconclusive results. At 0.5 ppm and 0.75 ppm, ocular distance increased significantly (Fig. 5, p values of 0.0066 and 0.0349 respectively), which indicates that the surface area of the eyes was smaller during this developmental stage of growth. However, we observed a significant decrease in ocular distance at 1.25 ppm (Fig. 5, p value of 0.0423). A decreased ocular distance could mean that the zebrafish eyes were larger or swollen.

0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0

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Figure 5: Average ocular distance for zebrafish between 6-10 days post fertilization. The asterisk indicates significance from the control (t-test; α=0.05).

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Fraction of Zebrafish

Ocular Distance (mm)

Original Research

Pearson correlation analysis between zebrafish ocular distance and behavioral avoidance patterns demonstrated that the two variables were not related (Fig. 6). The avoidance behaviors of the zebrafish may have been affected by reduced locomotor abilities of the dosed fish. Buoyancy may also have been impaired by Zn0 NP dosing.

0.2

Dosing Concentration (ppm) Ocular Distance

Fraction ZF in Quadrant with Red Ball

Figure 6: Correlation between behavioral assay and ocular distance of zebrafish (r = 0.153; Excel Pearson Correlation Test).

Discussion The data demonstrates that zebrafish exposure to Zn0 NP can potentially cause adverse morphological effects throughout the embryonic and larval stages of zebrafish development. Therefore, the nanoparticle itself, separate from an ion charge, must also be considered in future toxicological studies. This is supported by the data indicating Zn0 NP exposure induced significant changes in the average zebrafish length and ocular distance. These findings are similar to Bai et al. (2009), where ZnO nanoparticles were linked to stunted zebrafish larva body length. The two instances of increased ocular distance indicate potential neural development abnormalities (Lutte et al., 2015), which may implicate Zn0 NP as a neurodegenerative compound. Other studies observing effects of other metal nanoparticles on zebrafish eye development also report smaller eyes due to abnormal neural development along with downregulated gene expression and increased cell death in the eyes (Kim et al., 2013; Xin et al., 2015). However, the significantly reduced ocular distance at 1.25 ppm make the results for this variable slightly inconclusive in our study. Overall, the higher dosing concentrations of 1.0 ppm and 1.25 ppm did not yield statistically significant increases in morphological abnormalities, thereby refuting the hypothesis that increased Zn0 NP concentrations would result in more severe effects. The lack of an observed dose-dependent response may be due to greater particle aggregation at the greater dosing concentrations, which would affect uptake and bodily distribution. Another explanation may be that there were other health effects occurring at the higher levels that were not specifically analyzed as part of this study. For example, altered gene expression or heart edemas. The behavioral assay yielded inconsistent results and will require further testing as well. The Pearson correlation statistical

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analysis suggests that changes in ocular distance are weakly correlated with changes in zebrafish behavior. Therefore, the abnormal brain development indicated by the increased ocular distance did not affect the zebrafish’s ability to exhibit avoidance behaviors. Additionally, there do not appear to be any consistent increasing or decreasing trends in zebrafish avoidance behavior across the dosing concentrations. These findings coincide with a study conducted by Hua et al. (2014), where addition models were used to account for nanoparticle toxicity (no ion) of ZnO nanoparticles. Their study showed no consistent behavioral changes in a light/dark test, which serves to observe zebrafish activity and anxiety behaviors (Hua et al., 2014). It is important to note, however, that the results of the behavioral assay are inherently dependent on proper locomotive and eye function. It is possible that the results may have been impacted by Zn0 NP induced skeletal and morphological effects inhibiting the ability of the fish to physically exhibit a positive or negative behavioral response. A more comprehensive assessment of locomotive function would be needed to determine its impact. Overall, these findings are a critical stepping stone in determining the potential Zn NP health effects as they relate to neurodegenerative diseases in humans. Over 6.5 million people in the U.S. alone currently suffer from a neurodegenerative disease such as Alzheimer’s or Parkinson’s Disease (Harvard NeuroDiscovery Center, n.d.). Understanding the exposures and toxicity of these compounds may help to identify potential causal links for the development of these diseases. More broadly, continuing to adequately determine toxicity and safety levels of all compounds used in consumer products is necessary to ensure human health. Future research may entail further testing the extent of met-

References Aydemir Sezer, U., Ozturk, K., Aru, B., Yanikkaya Demirel, G., Sezer, S., Bozkurt, MR. (2017). Zero valent zinc nanoparticles promote neuroglial cell proliferation: A biodegradable and conductive filler candidate for nerve regeneration. J Mater Sci: Mater Med 28, 19. https://doi.org/10.1007/s10856-0165831-1 Bai, W., Zhang, Z., Tian, W., He, X., Ma, Y., Zhao, Y., & Chai, Z. (2009). Toxicity of zinc oxide nanoparticles to zebrafish embryo: a physiochemical study of toxicity mechanism. Journal of Nanoparticle Research, 12(5), 1645-1654. Doi: 10.1007/s11051-009-9740-9 Brun, N., Lenz, M., Wehrli, B., & Fent, K. (2014). Comparative effects of zinc oxide nanoparticles and dissolved zinc on zebra fish embryos and eleuthero-embryos:Importance of zinc ions. Science of the Total Environment, 476-477, 657666. https://doi.org/10.1016/j.scitotenv.2014.01.053 Chmielewska, K., Dzierzbicka, K., Inkielewicz-Stępniak, I., and Przybyłowska, M. (2020). Therapeutic Potential of Carnosine and Its Derivatives in the Treatment of Human Diseases. Chemical Research in Toxicology 33, no. 7: 1561– 78. https://doi.org/10.1021/acs.chemrestox.0c00010. Choi, J. S., Kim, R. O., Yoon, S., & Kim, W. K. (2016). Developmental Toxicity of Zinc Oxide Nanoparticles to Zebrafish (Danio rerio): A Transcriptomic Analysis. PloS one, 11(8), e0160763. doi:10.1371/journal. pone.0160763 Colwill, R. M., & Creton, R. (2011). Imaging escape and avoidance behavior in zebrafish larvae. Reviews in the neuroscience, 22(1), 63–73. https://doi.org/10.1515/RNS.2011.008 Harvard NeuroDiscovery Center (n.d.). “The Challenge of Neurodegenerative Diseases.” Accessed March 1, 2021. https://neurodiscovery.harvard.edu/challenge. Howe, K., Clark, M., Torroja, C., Torrance, J., Berthelot, C., Muffato, M., Collins, J. et al. (2013) The Zebrafish Reference Genome Sequence and Its Relationship to the Human Genome. Nature 496, no. 7446: 498–503. https://doi.org/10.1038/nature12111. Hua, J., Vijver, M., Richardson, M., Ahmad, F., & Peijnenburg, W. (2014). Particle-Specific Toxic Effects of Differently Shaped Zinc Oxide Nanoparticles to Zebrafish Embryos

(Danio Rerio). Environmental Toxicology and Chemistry 33, no. 12: 2859–68. https://doi.org/10.1002/etc.2758. Khan, F. R., & Alhewairini, S. S. (2018). Zebrafish (Danio rerio) as a Model Organism. Current Trends in Cancer Management. http://doi.org/10.5772/intechopen.81517 Kim, KT., Zaikova, T., Hutchison, J., & Tanguay, R. (2013). Gold Nanoparticles Development and Pigmentation. Toxological Sciences 133, no. 2: 275 https://doi.org/10.1093/toxsci/kft081. Lutte, AH., Capiotti, KM., da Silva, NL., da Silva, CS., Kist LW., Bogo, MR., Da Silva, RS. (2015) Contributions from extracellular sources of adenosine to the ethanol toxicity in zebrafish larvae. Reprod Toxicol. 2015 Jun;53:82-91. doi: 10.1016/j.reprotox.04.2015.001. Epub 2015 Apr 13. PMID: 25883026. Sánchez-López, E., Gomes, D., Esteruelas, D., Bonilla, L., Lopez Machado, A.L., Galindo, R., Cano, A. et al. (2020) “Metal-Based Nanoparticles as Antimicrobial Agents: An Overview.” Nanomaterials 10, no. 2: 292 https://doi.org/10.3390/nano10020292. Sarasamma, S., Gilbert, A., Juniardi, S., Putera Sampurna, B., Liang, S., Hao, E., Lai, H., & Hsiao, C. (2018). Zinc Chloride Exposure Inhibits Brain Acetylcholine Levels, Produces Neurotoxic Signatures, and Diminishes Memory and Motor Activities in Adult Zebrafish. International Journal of Molecular Sciences 19, no. 10: 3195 https://doi.org/10.3390/ijms19103195. Singleman, C., & Holtzman, N.G. (2014). Growth and Maturation in the Zebrafish, Danio Rerio: A Staging Tool for Teaching and Research. Zebrafish, 11(4), 396-406. http://doi.org/10.1089/zeb.2014.0976 Sirelkhatim, A., Mahmud, S., Seeni, A., Mohamad Kaus, NH., Ann, LC., Bakhori, S., and Mohamad, D. (2015). Review on Zinc Oxide Nanoparticles: Antibacterial Activity and Toxicity Mechanism. Nano-Micro Lett. 7: 219. https://doi.org/10.1007/s40820-015-0040 Tierney, Keith B (2011). Behavioural Assessments of Neurotoxic Effects and Neurodegeneration in Zebrafish. Biochimica et Biophysica Acta (BBA)- Molecular Basis of Disease, Including the Special Section: Zebrafish Models of Neurological Diseases, 1812, no. 3:381-89. https://doi.org/10.1016/j.bbadis.2010.10.011. Xin, Q., Rotchell, J., Cheng, J., Yi, J., & Zhang, Q. (2015). Silver Nanoparticles Affect the Neural Development of Zebrafish Embryos. Journal of Applied Toxicology 35, no. 12: 14481-92 https://doi.org/10.1002/jat.3164. Zhao Xuesong, X., Rong, Z., Zhouying, L., & Baixiang, R. (2016) Zinc Oxide Nanoparticles Induce Oxidative DNA Damage and ROS-Triggered Mitochondria-Mediated Apoptosis in Zebrafish Embryos. Aquativ Toxicology 180: 56-70. https://doi.org/10.1016/j.aquatox.2016.09.013.

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Original Research

-al nanoparticle-induced behavioral effects by means of engaging zebrafish locomotive function, swimming performance and balance, olfactory alarm pheromone response, and cognitive plasticity (Tierney, 2011). Additionally, the discovery of ion and particle-induced neurodegeneration incites the need for a mediating solution. Thus, future research may also entail exploring the potential reduction of the observed neurodegenerative effects by the compound carnosine, which has been shown to act as an antioxidant and reduce the effects of degenerative metal ions implicated in the development of various cancers and neurodegenerative diseases (Chmielewska et al., 2020). Perhaps carosine or other antioxidant compounds could be administered in conjunction with Zn0 NP during nerve regeneration treatments to reduce the effects of ROS production.

Original Research

Oviposition and Olfactometer-Based Behavioral Responses in Aedes Aegypti Batool Unar Syed, Jason Pitts, Ph.D. Department of Biology, Baylor University, Waco, TX

Abstract Oviposition assays are used to study the process of laying eggs. Although female oviposition has been studied in scientific literature, there are still a plethora of unanswered questions in this field. By studying the volatile odors that attract oviposition in female mosquitoes, better trapping and surveillance methods can be developed to prevent the spread of neglected tropical diseases and to encourage public health initiatives. With deionized water serving as the control, the three main volatile odors studied were beet root extract, oak leaf infusion, and geosmin. We hypothesized that the number of eggs oviposited by female Aedes aegypti mosquitoes in the laboratory setting would be significantly higher with beet root extract than with geosmin, deionized water, or oak leaf infusion. In addition to oviposition, olfactometer-based behavioral assays were conducted using the beet root extract and deionized water to test host-seeking in female mosquitoes. Through a better understanding of host and oviposition preference, the molecular basis of host preference can be identified in order to develop better preventative techniques and improve vector control. Key words: Aedes, oviposition, olfactometry

Introduction Around the world, there are around 900,000 different species of insects, and 3,500 of these species are mosquitoes. In the United States, one of the most prevalent mosquito genera in southern states like Texas is the Aedes genus. Aedes mosquitoes are found not only in Texas, but also across the Pacific Northwest, East Coast, Midwest, and Southern regions of America. They are uniquely characterized by their maxillary palps and the black and white bands on their legs. These mosquitoes pose significant threats to human life as they are vectors for dengue fever, eastern equine encephalitis, yellow fever, chikungunya, West Nile, and Zika virus. Among the Aedes genus, Aedes aegypti, the yellow fever mosquito, is a major contributor to mosquito season in warmer regions with higher temperatures because these mosquitoes cannot undergo winter diapause as eggs. The Ae. aegypti mosquito impacts human health and society by causing health risks, economic hardship, a decline in tourism, and detrimental effects on impoverished areas and healthcare systems. Ae. aegypti is the strongest vector for yellow fever, which causes over 200,000 cases and 30,000 deaths a year globally. The yellow fever virus is known to have various symptoms like nausea, headache, fever, and even fatal heart, liver, and kidney conditions. Currently there are no specific treatments that can cure the virus, but there are efforts to control the symptoms and manage any complications. Additionally, there are preventative measures such as the yellow fever vaccine which can provide lifelong protection in many populations. Another virus spread by this species is the Zika virus, a virus that can be asymptomatic or have mild symptoms such as a fever and rash. However, in pregnant women, the Zika virus can cause birth defects like microcephaly which affects the

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baby’s head size. The yellow fever virus predominantly affects countries in Africa, but over the past few years there have been local outbreaks of the Zika virus in the United States. Currently, the Zika virus has no vaccine and no treatment, so developing preventative measures is especially vital. In addition to impacting human health, these mosquitoes specifically pick hosts that they are more attracted to in order to feed and survive. Female Ae. aegypti mosquitoes can distinguish host preference through their neuronal responses when placed in areas with volatile odors. Disease transmission is primarily based on how female mosquitoes are able to select their host preference in order to blood feed (Chen et al., 2019). The host preference can be measured by different laboratory techniques. Oviposition assays are used to study the process of laying eggs. Of interest in this research study were beet root extract, oak leaf infusion, and geosmin, with deionized water serving as the control. Geosmin is an organic compound found in aquatic and soil-dwelling bacteria that contributes to the “earthy” odor after rainfall. Beet roots contain geosmin, but oak leaves do not; however, both volatile odors contain several other compounds that combine to attract mosquitoes. In this study, we expected the number of eggs oviposited by female Aedes aegypti mosquitoes in the laboratory setting to be significantly higher with beet root extract than with geosmin, deionized water, or oak leaf infusion. In addition to oviposition, olfactometer-based behavioral assays were conducted using beet root extract and deionized water to test host-seeking in female mosquitoes. Olfactometry is a standardized technique that utilizes a dilution instrument known as an olfactometer. An olfactometer is an instrument used to detect and measure odor dilution. The olfactometer based research allows certain odors to be assessed

Methods

placed on the top of the olfactometer for the entire assay time. The olfactometer trials were conducted over a two-week period and only tested beet root extract and the water control. For both the compounds, 50 mL was placed in an oviposition cup in the center of the collection chamber.

Oviposition Assays

There were several laboratory techniques used during this study. Oviposition assays were conducted in various forms. Three cup and four cup assays were attempted to test multiple compounds at a time. The position of the oviposition cups was rotated during these assays to account for a position-based preference. After four trials, no position-based preference was observed. There were several possible confounding variables that were accounted for with the change in cage set up. In the

preliminary phase of our trials, 8 mL glass vials hosted the compounds within the oviposition cups to eliminate the contact component. This method was utilized in a prior research study where geosmin was found to attract oviposition in Ae. aegypti mosquitoes (Melo et al., 2020). After multiple trials, stronger egg counts were found in the oviposition assays that incorporated the contact component within the oviposition cup. Once the preliminary trials were completed, the main method used throughout this study was the dual-choice oviposition assay. The dual choice assay consisted of two compounds being tested over a twenty-four-hour period with five female Ae. aegypti mosquitoes that were gravid, meaning they had taken a bloodmeal and were ready to oviposit. The dual choice assays were located inside the laboratory’s insectary room on a centered table with a white sheet covering the cages for the full assay time in order to eliminate the confounding variable of lighting in the trials. The geosmin concentration was 10-7 M and was diluted as a 50 mL mixture with deionized water. The beet root extract was created with 5 grams of beet root peel in 300 mL of deionized water, and then 50 mL of the extract was placed in the oviposition cup. The oak leaf infusion was diluted as 2 mL of pure oak leaf infusion in 48 mL of deionized water for a 50 mL total in the oviposition cup. Lastly, the control was 50 mL of deionized water in an oviposition cup. The egg counts were manually recorded the day after the assays were conducted. The dual-choice assays were repeated over a three-week period. Olfactometer Assays During the second half of the research study, olfactometer assays were conducted. The olfactometer was located outside of the insectary and tested a compound independently. The compound was placed in an oviposition cup inside the collection chamber. The fan was placed six inches away from the collection chamber with an average speed of 0.6 m/s. Each olfactometer trial was conducted for 15 minutes, and 10 female Ae. aegypti mosquitoes were placed in the release chamber. The total female count in the collection chamber was taken at the end of each trial and the same white sheet used in the oviposition assays was

W G

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BR OL

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Figure 1: A dual choice, three cup, and four cup oviposition assay model with five female Aedes aegypti, a sugar bottle, and oviposition cups. (BR: beet root extract; G: geosmin; OL: oak leaf infusion; W: deionized water).

collection chamber release chamber

4% CO2

Figure 2: An olfactometer model with female Aedes aegypti, a volatile odor in the collection chamber, and a fan and carbon dioxide for airflow.

Results Oviposition Assays Based on experimental trials in a laboratory setting, the dual choice assays resulted in clearer data in comparison to the three cup and four cup assays. The three cup and four cup assays depicted a preference for each compound in different trials, so no compound was repeatedly preferred over a four-week period of daily assays. The dual choice assays between oak leaf infusion and beet root extract did not display a strong preference, however the dual choice assays comparing geosmin and beet root extract displayed a preference for geosmin in four trials. Based on the data from four trials, two different unpaired t-tests were conducted in order to determine statistical significance from the p-values. For the first t-test, cages 1 and 2 were compared since they were both completed with oviposition cups contained a 50 mL mixture of the compound. In cages 1 and 2, the difference between geosmin and beet root extract was found to be statistically significant with a p-value of 0.0119. For the second t-test, cages 3 and 4 were compared since they were both completed with 8 mL glass vials hosting the compounds within the oviposition cups to eliminate the contact component. In cages 3 and 4, the difference was not found to be statistically significant with a p-value of 0.0597. In future trials, geosmin and beet root extract will be further studied, and these assays will be repeated in order to run a more thorough statistical

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for female host preference and can lead to a better understanding of the mosquito physiology. Through these methods and scientific literature review, research shows that female Ae. aegypti mosquitoes are able to distinguish host and oviposition preference, thus increasing the threat to human health globally. By identifying oviposition and olfactory attractants, new preventative measures and vector control techniques can be developed.

but with future repeated trials and the use of a dual choice olfactometer in later methods, the significance will be identified upon further data collection.

Geosmin vs Beet Root Beet Root 143

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Figure 3: This graph depicts four dual choice assays in four different cages. Each cage contained geosmin and beet root extract. In cages 1 and 2, each oviposition cup contained a 50 mL mixture of the compound. In cages 3 and 4, contact-free oviposition cups were used with glass vials that contained the designated compound. All four cages were located in the middle of the insectary with a white sheet covering the cages. Regardless of the oviposition cup method used in each cage, all four assays depicted a preference for geosmin over beet root extract because the number of eggs oviposited were higher for the geosmin-based oviposition cups. *** depicts statistical significance * depicts no statistical significance Olfactometer Trials 7 6

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analysis for the experimental data.

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Figure 4: This graph depicts three trials containing dual choice olfactometer assays between beet root extract and water as the control. The trials had 10 female mosquitoes who were not blood-fed, and each assay was 15 minutes long. The oviposition cups had 50 mL of each compound placed in the collection chamber separately between trials. The mosquitoes were placed inside of the release chamber and were counted for preference in the collection chamber. In all three of the trials, beet root extract was preferred by the starved mosquitoes when compared to the control trials. Olfactometer Assays Based on the experimental trials in a laboratory setting, the olfactometer trials resulted in a preference for beet root extract as an attractant over the deionized water control. Due to the low number of trials, a statistical analysis has not yet been conducted

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Current scientific research supports the organic compound geosmin to be an attractant for female Ae. aegypti oviposition in an outdoor setting (Melo et al., 2020). The dual-choice oviposition assays conducted displayed a preference for geosmin over beet root extract in the laboratory setting and since this experiment has been conducted in an outdoor setting in previous research, with repeated trials and altered concentrations a preference can be established for geosmin (Melo et al., 2020). The cost component of Geosmin poses challenges when using the compound for baiting in surveillance and trapping methods. Thus, a beet root peel can be used as a substitution especially in low-income communities (Melo et al., 2020). In future studies the beet root peel can be analyzed through various laboratory techniques to account for the exact geosmin concentration. Based on current research, new changes can be implemented in order to improve public health outreach and preventative measures. By understanding and further researching host preference and oviposition attractants, new chemical repellants can be developed more effectively. Additionally, more studies comparing female oviposition preference would be beneficial to these initiatives. These studies could possibly determine which mosquito species have the strongest receptors, thus making them more likely to bite various hosts and transmit diseases. Conducting these studies can benefit entomologists, veterinarians, scientists, outdoor workers, and all human beings. Understanding which odors are volatile and why humans exhibit them strongly will provide information that is necessary to implement change. In addition to continuing future studies, it is important to develop current solutions and raise awareness. Surveillance and control methods are a vital public health measure in the field of entomology (Weeratunga et al., 2017). In order to limit the spread of human diseases and viruses transmitted by Ae. aegypti mosquitoes, public outreach programs need to be developed to teach the current preventative measures in place for vectorborne diseases. Schools, recreational centers, and neighborhood facilities should have public health posters and pamphlets sharing the top five preventative measures to take as warmer weather approaches so that children, adults, and the elderly can stay safe in local areas and prevent poor future health outcomes. Current research displays that Ae. aegypti mosquitoes have a human host preference, therefore it is important to implement public outreach programs for outdoor workers in construction, landscaping, and other professions so that effective repellants can be used directly in the field. Furthermore, outreach programs can include information regarding recommended outdoor dress, repellants, peak time of day for biting rates, and any research that presents the latest human odors that lead to the host preference. The educational curriculum should contain household tips to avoid maintaining breeding sites in kiddy pools, old tires, and still fountains that may be leading to disease

Attracting Aedes aegypti mosquitos with Geosmin

Trapping Aedes aegypti mosquitoes with Geosmin

Replacing Geosmin with beet root peels as a more affordable alternative

Figure 5: This figure represents the various possible methods where Geosmin can be used to attract and trap Aedes aegypti mosquitoes. In the research study “Geosmin Attracts Aedes aegypti Mosquitoes to Oviposition Sites”, by Melo et al. (2019) beet root peels were studied and considered as an affordable alternative for low-income communities where Geosmin is not financially accessible.

Acknowledgements I would like to thank Dr. Jason Pitts for the constant guidance and support as my research mentor. Also, I would like to thank him for inspiring me to continue my education in the future and to understand the importance of having a purpose and mission in scientific research. I would also like to thank Shan Ju Shih for her assistance and guidance throughout the entirety of this project. Additionally, I would like to thank Carla-Cristina Edwards and Melissa Norena Leon for their collaboration and support in this project. Finally, I would like to thank the Baylor Ronald E. McNair Scholars Program for providing this research opportunity and for funding this summer internship.

References Chadee D. D. (2012). Studies on the post-oviposition blood-

feeding behaviour of Aedes aegypti (L.) (Diptera: Culicidae) in the laboratory. Pathogens and global health, 106(7), 413–417. https://doi.org/10.1179/2047773212Y.0000000036 Chen, Z., Liu, F. & Liu, N. Human Odour Coding in the Yellow Fever Mosquito, Aedes aegypti. Sci Rep 9, 13336 (2019). https://doi.org/10.1038/s41598-019-49753-2 Day J. F. (2016). Mosquito Oviposition Behavior and Vector Control. Insects, 7(4), 65. https://doi.org/10.3390/ insects7040065 E. Barçin Dogan, Philippe A. Rossignol, An Olfactometer for Discriminating Between Attraction, Inhibition, and Repellency in Mosquitoes (Diptera: Culicidae), Journal of Medical Entomology, Volume 36, Issue 6, 1 November 1999, Pages 788–793, https://doi.org/10.1093/jmedent/36.6.788 Gaburro, J., Paradkar, P.N., Klein, M. et al. Dengue virus infection changes Aedes aegypti oviposition olfactory preferences. Sci Rep 8, 13179 (2018). https://doi.org/10.1038/ s41598-018-31608-x Geosmin. (n.d.). Retrieved July 28, 2020, from https://www. sciencedirect.com/topics/agricultural-and-biologicalsciences/geosmin Melo, N., Wolff, G., Costa-da-Silva, A., Arribas, R., Triana, M., Gugger, M., . . . Stensmyr, M. (2019, December 12). Geosmin Attracts Aedes aegypti Mosquitoes to Oviposition Sites. Retrieved July 28, 2020, from https://www.sciencedirect. com/science/article/pii/S0960982219314411 Okal, M. N., Lindh, J. M., Torr, S. J., Masinde, E., Orindi, B., Lindsay, S. W., & Fillinger, U. (2015). Analysing the oviposition behaviour of malaria mosquitoes: design considerations for improving two-choice egg count experiments. Malaria journal, 14, 250. https://doi.org/10.1186/s12936-015-0768-2 Omrani SM, Vatandoost H, Oshaghi MA, et al. Fabrication of an olfactometer for mosquito behavioural studies. J Vector Borne Dis. 2010;47(1):17-25. McBride, C., Baier, F., Omondi, A. et al. Evolution of mosquito preference for humans linked to an odorant receptor. Nature 515, 222–227 (2014). https://doi.org/10.1038/nature13964 Rogers, R., & Yee, D. (2019, January 22). Response of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) Survival, Life History, and Population Growth to Oak Leaf and Acorn Detritus. Retrieved July 28, 2020, from https://academic.oup. com/jme/article-abstract/56/2/303/5292477 Tallon, A.K., Hill, S.R. & Ignell, R. Sex and age modulate antennal chemosensory-related genes linked to the onset of host seeking in the yellow-fever mosquito, Aedes aegypti. Sci Rep 9, 43 (2019). https://doi.org/10.1038/s41598-018-36550What is Petrichor? That’s Geosmin You Smell. (2019, June 09). Retrieved July 28, 2020, from https://blog.indigoinstruments. com/geosmin-petrichor-earth-odor/ Weeratunga, P., Rodrigo, C., Fernando, S. D., & Rajapakse, S. (2017). Control methods for Aedes albopictus and Aedes aegypti. The Cochrane Database of Systematic Reviews, 2017(8), CD012759. https://doi.org/10.1002/14651858. CD012759 Weghuber, J. (2015, April 06). Compositional characteristics of commercial beetroot products and beetroot juice prepared from seven beetroot varieties grown in Upper Austria. Retrieved July 28, 2020, from https://www.sciencedirect. com/science/article/pii/S0889157515001003

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transmission in local communities. The outreach programs should be shared before warmer temperatures arise in the regions and in southern states the presentations should be offered multiple times a year because the weather conditions vary. In addition to educating the workers, public health measures can be implemented to the local communities so that they know the immediate signs and symptoms of dengue, yellow fever, and Zika virus and the transmission cycle of Ae. aegypti. Although educational methods are the simplest, they can be the most effective when utilized in outreach programs. Creating new curriculums and developing future studies that determine the significance of host and oviposition preference for Ae. aegypti mosquitoes will aid in vector control, sustainable preventative measures, and more findings regarding the biology of mosquitoes. Even though there are medical diagnoses and treatment to manage any symptoms of vector-borne diseases transmitted by Ae. aegypti, there is always a need for ipsum improvement in the field. It is important to identify theLorem unique characteristics of the species, understand the environmental factors that lead to oviposition and host preference, and develop public health programs to educate others in order to effectively understand the role of Ae. aegypti mosquitoes.

Original Research

A Predictive Model Assessing External Suicide Risk Factors at the County Level Brandon Cunningham, Hannah League, Madelyn Olivas, Celine Rukiidi, Jonathan Wu Office of Prehealth Studies, Baylor University, Waco, TX

Abstract Suicide rates have increased at an alarming rate over the past two decades. According to the CDC, from 1999 to 2018, the number of suicides in the U.S increased dramatically by 35%, elevating the current state of suicide as an epidemic. Mental illness, indicated as an internal risk factor (influenced by the level of an individual’s health) leading to suicide, plays a prominent role in this epidemic. In this study, we explore the relationship between the prevalence of mental health care facilities and the number of suicides reported within a county in 2018, along with the most prevalent external risk factors (not explicably linked to health but indirectly influence physical and mental health) linked to suicide. Data was drawn from the CDC and County Health Rankings to identify external risk factors, demographics, and suicide rates. The independent variables that were analyzed in the final multiple linear regression model included Mental Health Providers; percent estimate of Black and African American; age categories 25-35, 45-54, and 60-64 years; rural populations (2010); percent estimate of American Indian and Alaskan Native; and percent estimate of males. A model considering all the factors above yielded an R-square value of 0.571. Statistical analysis through the Statistical Package for the Social Sciences (SPSS) system revealed the largest contributing variables linked to suicide to be rural/urban residence and median income earnings per household. The population most at risk for suicide was identified to be white males with low socioeconomic status which is consistent with existing suicide literature. Counties with outlying residuals resulted from the multiple linear regression and might yield interesting findings for future research.

Introduction The act of suicide is often preceded by and classified as suicidal behavior, which refers to a range of behaviors including thoughts of suicide, to suicide attempts, to death by suicide (Mental Health Commission of Canada, 2018). In the United States, suicide occurs at a rate of 10.8 per 100,000 individuals and is the 11th leading cause of death (Nock et al., 2008). In 2018 alone, 48,344 Americans died from suicide along with an estimated 1.4 million attempted suicides (CDC Suicide Prevention, 2020). The impact of the loss of human life through suicide is felt throughout the millions of lives that have experienced the aftereffects of a life taken by suicide. Furthermore, those that struggle with suicidal ideation merit the prioritization of research and focused care to prevent continued loss of life. The impact of these losses are felt individually and nationally, leaving long lasting effects resulting from the vacancy left when the unfortunate act of suicide occurs. While the value of human life cannot be understated, there is also a significant economic burden that results from completed and attempted suicides. In the United States, the average cost of one completed suicide is $1,329,553 (D. S. Shepard et al., 2016). The estimated cost includes direct and indirect costs such as medical care, ambulance transport, investigations, net present value of future salaries and wages, and overall household productivity. In addition, taxpayers experience increased taxes to finance publicly funded medical costs related to suicide (Ashwood et al., 2015). The exact number of suiciderelated taxation costs is unknown but the estimated financial

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burden is significant enough to suggest that suicide prevention measures will benefit the taxpayer (Ashwood et al., 2015). The total national cost for suicides in a one-year span is an estimated 93.5 billion dollars, which can be largely attributed to a “loss of productivity” (D.S. Shepard et al., 2016). As this figure emphasizes, completed suicides produce a significant financial burden on multiple industries in the United States. Research on suicide has become more relevant in the past 15 years as researchers seek to understand current suicide trends (Mental Health Commission of Canada, 2018). Past suicide research has detailed the factors that heighten the risk of suicide among certain individuals. While previous studies have indicated mental illness as the most significant risk factor leading to suicide, they have lacked in their assessment of the external factors that contribute to mental illness. In light of the COVID-19 pandemic, suicide research, specifically on the effects of external risk factors, is needed to respond to the effects of social isolation and predicted spike in suicide rates. Recent studies have shown that the unprecedented events related to the COVID-19 pandemic could prove potentially detrimental for those with preexisting mental health problems (Reger et al., 2020). Specifically, the effects of economic stress, social isolation, decreased access to community and religious support, barriers to mental health treatment, illness and medical problems, and outcomes of national anxiety raise concern for potential increases in suicide risk (Reger et al., 2020).

Risk Factors Rural vs. Urban The disproportionate prevalence of suicide in rural counties is a well-documented phenomenon (Wilkinson et al., 1984) and is largely attributed to limited access to healthcare providers, greater access to firearms, and higher drug and alcohol use (Steelsmith et al., 2019) with Louma et al. (2002) finding that 50% of individuals who attempt suicide within rural areas contact their primary care provider within a month before their suicide. A decreased prevalence in access to mental health care in rural counties combined with exposure to social isolation contribute to the increased rates of suicide experienced in those counties, making rurality a target factor in this study. In addition, consistently higher poverty rates in rural areas have been recorded in comparison to urban areas according to the U.S. Census Bureau, linking rural citizens to the external risk factor of financial strain.

Age

While the incidence of suicide has increased consistently throughout all age categories in the U.S. over the past two decades, specific age groups have been at a disproportionate risk. According to the American Association of Suicide Prevention database, 45-54 and 55-64 year-olds report the highest overall suicide rates. As explored by the AARP, some researchers flag these age-groups at a higher risk for suicide due to unique mid-life stressors, access to firearms, increase in drug overdoses, and economic despair (Steelesmith et al., 2019). Another significant age group linked to the act of suicide is the 10-24 year-old age group. While the 10-24 year-old age-group has lower rates of suicide than their middle age counterparts, the incidence of suicide in this age group has increased by 56% between 2007-2017, with suicide rising to one of the leading causes of death among this age group, identifying this age category an important strata for suicide research (Sally C. Curtin et al., 2019). According to the National Vital Statistics Report in 2017, suicide was the second leading cause of death in the 10-24 year-old age group (Heron, 2019). Elderly Americans aged 60-64 are also at an increased risk for suicide; representing 12% of the general population and 18% of all suicide deaths (AAMFT). In response to rising suicide rates in the United States, researchers must continue to dissect relative age groups in order to accurately attribute age-related exposures to suicide and create effective targeted solutions. Median Household Income Median household income has been identified as a significant risk factor in relation to suicide, primarily affecting low-income individuals. According to the Journal of Epidemiology, there is a significant link between an increase in suicide rates and low socioeconomic status. Across high, middle and lower economic classes, research showed lower rates of suicide among the highest class (Lee et al., 2017). Factors such as depression, anxiety, and drug usage are most significant when researching teenage suicide risk, regardless of financial position. In older adults, household median income associated with financial strain has been identified as a major contributor to suicide. Therefore, in older age brackets, preventative measures should address economic position and the reduction of poverty rates more so than in younger age brackets (Choi et al., 2019). Overall, household income has been shown to have a tremendous influence on suicide risk across all age groups. Sex

Studies have shown biological sex as a significant variable that can influence an individual’s likelihood to complete suicide (Elflein, 2019). The overrepresentation of men in suicide data is a well-documented phenomenon worldwide, necessitating further research on the relation of gender to suicide. According to Statista, sex has been considered a highly predictive factor in measuring suicidal risk in the United States. Increased risk for suicide among men can be attributed to social factors such as social stigma, decreased access to mental health providers, and lowered social interaction compared to their female counterparts (C. Wyllie et al., 2012). Therefore, considering

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Suicide Risk Factors Internal risk factors for suicide are those influenced by the level of an individual’s health, including physical or mental diseases. Along the lines of mental and physical health, mental illness, clinical depression, anxiety, and alcohol and substance abuse are all internally experienced risk factors of suicide (CDC; Violence Prevention, 2019). In contrast, external risk factors for suicide are those that are not explicably linked to health but indirectly influence physical and mental health. Identified external suicide risk factors include but are not limited to social isolation, inadequate access to mental health care, physical illness, and financial strain (CDC; Violence Prevention, 2019). In addition, several protective factors have been identified to buffer the effects of internal and external risk factors. Protective factors are classified as effective mental clinical care, access to clinical interventions, family and community support, financial stability, and cultural and religious beliefs that discourage suicide (CDC; Violence Prevention, 2019). The expansion of knowledge of suicide protective factors has contributed to rising trends in mental health awareness and the inclusion of preventative practices in mental health care. In contribution to ongoing research relating to suicide risk and prevention measures, our study will identify those most at risk for suicide, the effectiveness of availabilty of mental health care on suicide rates, and the most prevalent external factors that contribute to suicide. By analyzing county-level data, we expect to find that external risk factors and prevalence of mental health care facilities will affect the amount of suicide rates experienced within a county. Specifically, we hypothesize that rural counties will report higher level of suicides than urban, higher prevalence of mental health care facilities will account for lower suicide rates, and those most at risk for suicide will be low-income non-white males, 40-years and above. In contrast to previous suicide research, our study will expand the scope of factors that outwardly influence the act of suicide through examining external risk factors such as mental health care accessibility (MH providers), financial strain (median household income), and social isolation (rural vs. urban).

Race Because of America’s racially diverse population, it is imperative that the racial distributions of suicide rates be observed and analyzed among our findings. From 1999 to 2017, the National Center for Health and Statistics recorded a disproportionate number of completed suicides for American Indians or Alaska Natives (AIAN) and non-Hispanic whites. As certain racial groups are associated with an increased risk of suicide, racial links to suicide have remained a main focus of suicide research. Considering racial identity in the analysis of U.S. suicide trends is exigent because of associating socioeconomic factors that are commonly observed in varying racial demographics. Since the increase in suicide rates nationally in 1999, AIAN populations in the U.S. have seen suicide increases at a far higher rate than the general population. According to a 2018 CDC study, since 1999, AIAN women and men have seen a 139% increase and a 71% increase respectively in their overall suicide rates (Curtin et al., 2019). The consistently disproportionate increases in suicide among certain racial populations require researchers to include the category of race in the discussion of suicide as it is an unmistakably significant contributing factor to the issue at hand.

Methods County-level data collected by the CDC was used as the primary source of data for this research study, accounting for the data relating to external risk factors (Boroughs were used for Alaska, and Parishes for Louisiana) (Ivey-Stephenson, CDC, 2017). Suicide rate data was collected from County Health

Rankings (County Health Rankings & Roadmaps, 2018). County-level data was missing among certain counties; missing data for any category examined resulted in deletion of the entire county for the study. Following data collection, each data set was cleaned and combined into one overarching data set which was then analyzed through the Statistical Package for the Social Sciences (SPSS, Version 27.0.0.0). Of the 3,142 counties, only 557 were able to be analyzed. The loss of data was largely due to privacy restrictions on the CDC’s databases. The data for the total amount of suicides for a given year by county was suppressed for those counties with nine or fewer suicides. The crude rate of suicide per 100,000 people by U.S. counties in 2018 was considered as the dependent variable. Independent variables were selected by comparing statistical variance inflation factors (VIF) values and deleting independent variables that were highly correlated. VIF values were considered to exclude any association between the variables. The following variables were excluded for collinearity: percent estimates of age groups (25-44 and 55-59) per county, and percent uninsured (similarity with median household income). The independent variables that were used in the final multiple linear regression model are as follows: number of mental health providers (mean across all counties: 257.45); percent estimate of Black and African American (mean across all counties: 9.06%); percent estimate of those aged 25-34 (mean across all counties: 11.729%), 45-54 (mean across all counties: 12.927%), and 6064 years (mean across all counties: 6.905%); percent estimate of rural area (2010) (mean across all counties: 58.669%); percent estimate of American Indian and Alaskan Native (mean across all counties: 1.967%); and percent estimate of males (mean across all counties: 50.087%). The percentages of race, age and rural area were based on all individuals in that county. All data (unless otherwise specified) was reported from the 2018 year.

Histogram Dependent Variable: Crude Rate per 100,000 120

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biological sex in the discussion of suicide and related risk factors will be a focus of our study.

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Figure 1: Histogram of standardized regression residuals with crude rate per 100,000 as dependent variable

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Unstandardized B

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.017

.245

6.378

.000

.521

1.918

Percent Estimate 20 to 24 years

-.551

.116

-.177

-4.756

.000

.555

1.800

Percent Estimate 45 to 54 years

-.958

.208

-.194

-4.598

.000

.433

2.308

Percent Estimate 60 to 64 years

1.619

.269

.219

6.010

.000

.580

1.725

Median household income (dollars)

.000

-.228

-5.654

.000

.473

2.113

1.464

.211

.231

6.951

.000

.701

1.426

.262

.038

.200

6.816

.000

.893

1.119

Number of MH Providers Percent Estimate - Black or African American 2010 Census Percent Rural

1339

Cochise

Arizona

100

1341

Gila

Arizona

195

1244

El Dorado

California

284

1374

Mesa

Colorado

319

3125

Sussex

Delaware

323

Bay

Florida

364

114

Monroe

Florida

380

128

Sumter

Florida

554

1387

Bear Lake

Idaho

1639

757

Ravalli

Montana

1804

1199

Eddy

New Mexico

2254

2080

Butler

Pennsylvania

-2631

1546

Hidalgo

Texas

2634

592

Hood

Texas

213 240

Percent Estimate - Male Percent Estimate - American Indian and Alaska Native

Dependent Variable: Crude Rate per 100,000

Table 1: Individual t-scores in multiple linear regression and levels of significance. Individual Independent Variable (all vs. Suicide Crude Rate per 100,000

)

Adjusted R-Square Value

2257

Percent Estimate American Indian and Alaska Native

.117

Percent Estimate Male

.089

2897

Percent Estimate Age 25 - 34

.054

2946

3114

.044

Roanoke City

Virginia

Percent Estimate Age 45 - 54 Percent Estimate Age 60 - 64

.151

2958

1315

Clallam

Washington

Median Household Income

.175

3130

658

Laramie

Wyoming

Percent Rural (2010)

.316

3132

1482 Natrona

Wyoming

Percent Estimate Black or African American

.101

Number of Mental Health Providers

.091

Table 2: Table of adjusted R-square values when compared individually to suicide crude rate per 100,000. Rural area data were extracted from 2010 because there was no available data for 2018. Upon further investigation, the rural population has only slightly decreased since 2010. A slight drop in rural population from 16% to 14% was seen from 2000 to 2012-2016 (Social & Demographic Trends, 2016).

Results All variables were put into a model of multiple linear regression to predict the relationship between those set variables and suicide rate in those counties. A multiple linear regression model was used in this circumstance because the crude rate of suicides by county was the only dependent variable in question. This enabled the use of all independent variables at once to be compared to suicide rates. The county residuals were

Figure 2: Key for Table 3 (on next page), colors are representative of t-score outliers. Figure 2 is a key to navigate Table 3. The red counties are counties that had a T score greater than or equal to 3.0, yellow are counties that had a T score of 2.5 or greater, and blue are counties that had a T score of 2 or greater. The larger the T score, the stronger the outlier was in our model. calculated by taking predicted value (that of which was estimated by the multiple linear regression analysis) subtracted from the actual value (observed crude rate per 100,000 suicides). Figure 1 on the last page illustrates an approximately normal distribution of data which enabled the use of multiple linear regression statistical analysis. All of the variables that were considered significant were placed into a model with an R-square = 0.578 and an adjusted R-square = 0.571 with a P < 0.000. The variables and results are included in Table 1 above. As seen in Table 1, the variables that were used in the model were all statistically significant as p < 0.05. The individual R-squared value of each independent variable in predicting the dependent variable (Crude Rate of Suicides in 2018 by 100,000) are located above (see Table 2).

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Coefficients

Original Research

These values were obtained by comparing the dependent variable with one independent variable at a time; R-Square values came from individual linear regressions. The percent rural from 2010 had the largest effect on suicide with an adjusted R-squared value of 0.316, suggesting that after accounting for all other factors, 32% of the variance in suicide rates could be attributed to rural area. Percent rural was one of the best predicting factors of suicide and percent of individuals by county aged 45-54 was one of the poorest. The type of relationship the variable has with suicide can be determined by referring to Table 1; Table 2 simply shows the level of correlation between the variables and suicide rate. Our final analysis took the residuals of suicide rate estimates from every county used in the study to generate a predicted number of suicides. These predicted numbers were subtracted from the actual numbers of suicides that took place in specific counties in 2018. Outliers in this case would represent any county that had significantly more or significantly less suicides than expected. The majority of the listed county outliers have higher rates of rural areas, yet our study did not explore the significance of the outlying suicide rates linked to rurality among the county outliers (Universal Service Administrative Company, 2021). The county outliers are shown on the last page. Casewise Diagnostics Case Number

Std. Residual

Crude Rate per 100,000

Predicted Value

Residual

-2.378

28.39

-10.991

100

5.996

33.48

27.717

195

-2.083

22.23

-9.631

213

-2.021

18.34

-9.342

240

-2.162

19.39

-9.993

284

3.171

21.24

14.659

319

-2.722

24.38

-12.583

323

2.048

18.63

9.465

364

4.742

23.38

21.920

380

-4.116

35.33

-19.027

554

3.613

22.30

16.702

1639

2.719

33.73

12.570

1804

2.706

21.99

12.510

2254

-2.059

21.22

-9.519

2257

2.214

24.06

10.237

2631

-2.486

16.69

-11.491

2634

2.195

24.55

10.147

2897

2.049

17.13

9.474

2946

2.615

15.91

12.088

2958

2.226

30.11

10.289

3130

2.920

20.90

13.500

3132

2.521

22.45

11.654

Dependent Variable: Crude Rate per 100,000

Table 3: List of county outliers

18 | Scientia 2021

Discussion Our final analysis concluded that the amount of mental health providers within a county has an impact on suicide rates with more mental health providers being linked to less suicides. Our study also revealed that older white males are at the most risk for suicide. The two external risk factors most closely linked to suicide were rurality and median household income, with those residing in rural counties and those with low economic status most at risk for suicide. One of the inherent limitations of this type of study is the distinction between correlation and causation, as well as how generalizable the results are to the entirety of the United States. While the data show some trends with respect to the variables and their influence on suicide rate at the county level, it is impossible for further analysis to be made from a statistical standpoint. Part of this limitation is a result of the data used for the statistical tests: we gathered various data from separate sources about the same counties and compiled them into one larger dataset. Due to certain U.S. privacy laws, including Title 42 U.S.C. § 242m(d), there is no single dataset telling us the specific demographic information of each individual suicide that was recorded in each county in the United States. Consequently, no conclusions can be made about the direct relationship between our measured variables and the suicide rate. The privacy laws also require the suppression of certain data concerning suicide count and suicide rate in counties that recorded less than ten suicides per year, which removed 2,138 counties from consideration, limiting the scope of our results. Moreover, counties with a population of approximately or less than 10,000 residents were considered “unreliable” for the data, given that the suicide rate was given per 10,000 residents, which further limited the scope of our results by 447 counties. In total, data from only 557 counties, with populations significantly greater than 10,000 and with more than 10 suicides in the year of 2018, were considered in our model. This excluded many majority rural counties from our model. Another important consideration for our project was the availability of data. While the factors that influence suicide rates continue to be investigated, these factors are often unable to be quantified at a scale as specific as the county level. Our model was therefore limited by the availability of relevant data. Furthermore, as discussed in the introduction, there are many factors that influence an individual’s decision to complete suicide, and thus this model does not account for every single factor that has been implicated in suicide risk. It is, however, a promising foundation for future research. Recent progress in suicide research and reform of prevention measures suggest a promising future in diminishing suicide rates in the United States. In moving forward, suicide must be recognized at an epidemic level which warrants the attention of the medical community and health officials. Suicide prevention awareness is a direct path to counteracting trends in suicide and normalizing treatment of mental illness (CDC; Violence Prevention, 2019). In order for further mental health awareness efforts to be shown effective, there must be an inclusive approach of targeting those populations who are at a higher risk for suicide through mental health services and

Conclusion The results of this study are promising, and we believe that they warrant further investigation. There are many factors that deserve more attention: one such example was the rate of problematic substance use at the county level. State and county reporting standards across the United States vary widely in this respect, but with more time and resources, it is possible that this factor could be quantified to fit in our model. An experienced investigation team with access to restricted databases, for example, might be able to find the data that we were unable to find and add to the model that we started. Another major factor that we believe would have had an effect on the model is the availability of and access to lethal methods used to complete suicide, the most prevalent of which being access to firearms. The closest dataset we were able to find were the Federal Firearms Listings provided by the United States Bureau of Alcohol, Tobacco, Firearms and Explosives, which may be able to provide an adequate approximation of firearm access given enough time to extract and clean the desired data. Perhaps the most elusive but most promising paths of further study would be the quantification of various factors related to an individual’s risk of completing suicide, including but not limited to previous suicide attempts, suicidal behavior and thoughts, and self-harm. These factors are incredibly difficult to quantify,

and, as far as we were able to find, completely unavailable at the county level. We believe that the recording of such data at the county level would present a beneficial step in the research and prevention of suicide and would provide an even more comprehensive picture of suicide rates in the United States. Further study could also focus on the peculiar outliers identified by the model. A statistical model comparing each outlier and the independent variables would elucidate these relationships. Exploration of why these outliers exist presents another promising area of future research, particularly concerning what the outliers have in common on both ends of the spectrum. Throughout this study, specific risk factors were highlighted in relation to suicide rates. Rural populations and median household income had the most significant correlation with suicide rates, warranting primary focus when considering prevention efforts. Although there is still a tremendous amount of research to be done on this critical issue, this study has identified several factors that can be used to help target suicide rates in at-risk counties and raise awareness for those populations most at risk for suicide.

References Ashwood, J., Briscombe, B., Ramchand, R., May, L., & Burnam, M. (2015). Analysis ofthe Benefits and Costs of CalMHSA’s Investment in Applied Suicide InterventionSkills Training (ASIST). RAND Health Quarterly, 5(2). Bachmann, S. (2018). Epidemiology of Suicide and the Psychiatric Perspective. International Journal of Environmental Research and Public Health, 15(7), 1425 doi:10.3390/ijerph15071425 Bilsen, J. (2018). Suicide and Youth: Risk Factors. Front Psychiatry, 9(540). doi:10.3389/fpsyt.2018.00540 Choi, J. W., Kim, T. H., Shin, J., & Han, E. (2019). Poverty and suicide risk in older adults: A retrospective longitudinal cohort study. International Journal ofGeriatric Psychiatry, 34(11), 1565-1571. doi:10.1002/gps.5166 Clay, R. (2014). Reducing rural suicide. American Psychological Association, 45(4), 36. Cummings, J. R., Allen, L., Clennon, J., Ji, X., & Druss, B. G. (2017). Geographic Accessto Specialty Mental Health Care Across High-and Low-Income USCommunities JAMA Psychiatry, 74(5), 476. doi:10.1001/jamapsychiatry.2017.0303 Curtin, S. C., & Heron, M. (2019). Death Rates Due to Suicide and Homicide Among Persons Aged 10–24: United States, 2000–2017. NCHS Data Brief, 352. Dudley MJ, Kelk NJ, Florio TM, Howard JP, Waters BG. Suicide among young Australians: an interstate comparison of metropolitan and rural trends. Med JAust. 1998;169:77–80. PubMed. Heron, M. (2019). Deaths: Leading Causes for 2017. National Vital Statistics Reports, 68(6). Ivey-Stephenson, A. Z., Crosby, A. E., Jack, S. P., Haileyesus, T., & Kresnow-Sedacca, M. (2017). Suicide Trends Among and Within Urbanization Levels by Sex,Race/Ethnicity,

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policy reform. Furthermore, practices that equalize financial inequalities in access to mental health care are suggested to reduce suicide rates. By examining county-level data, it is evident that certain counties are more heavily burdened with various external risk factors. We suggest that mental health care reform start at the county level and target those specific counties that contain populations at a higher risk for suicide. In contrast to previous research that largely focuses on internal suicide risk factors, this study draws attention to quantifiable external risk factors that influence the internal experience of suicide. We recommend an emphasis on mental health awareness programs within counties and equality of access to mental healthcare providers, specifically in rural counties. This study has primarily highlighted the external risk factors predicting suicide rates, but we are aware that internal risk factors significantly contribute to the increase in suicide rates. Therefore, we suggest that public health programs and mental health care facilities focus on the processes and influencing factors leading to suicide and recognize external risk factors as a major source contributing to suicide. Identifying external risk factors can help target specific locations that would benefit from preventive interventions, targeting populations in need before the development of mental illness or suicidal thoughts. Our studies link of mental health care providers to the lessening of suicide rates suggests that community-based interventions are a key component in suicide prevention efforts and reducing numbers of suicide. Furthermore, with expansion of healthcare reform, community awareness of prevention of suicide, and equality of access to mental health care, it is hopeful that we will begin to see a significant reduction in suicide rates and economic stabilization from the lessening of costs relating to suicide.

Original Research

Age Group, and Mechanismof Death -United States, 2001- 2015.Centers for Disease Control and Prevention, 66,1-16. McFarland, D. C., Walsh, L., Napolitano, S., Morita, J., & Jaiswal, R. (2019). Suicide in Patients With Cancer: Identifying the Risk Factors. Cancer Network, 33(6). Morrell S, Taylor R, Slaytor E, Ford P. Urban and rural suicide differentials in migrants and the Australian-born, New South Wales, Australia 1985–1994. Soc Sci Med.1999;49:81–91. Crossref. PubMed. Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2008). Suicide and Suicidal Behavior. Epidemiologic Reviews, 30(1), 133-154. doi:10.1093/epirev/mxn002 Lee, S., Oh, I., Jeon, H. J., & Roh, S. (2017). Suicide rates across income levels: Retrospective cohort data on 1 million participants collected between 2003 and2013 in South Korea. Journal of Epidemiology, 27(6), 258-264. doi:10.1016/j.je.2016.06.008 P. (December 2018). Research on Suicide and Its Prevention: What the current evidence reveals and topics for future research. Mental Health Commission of Canada. Piscopo, K. D. (2007). Suicide Trends Among Youths and Young Adults Aged 10-24 Years--United States, 19902004. PsycEXTRA Dataset.doi:10.1037/e669322007-002 Pompili, M., Vichi, M., Innamorati, M., Leo, D. D., & Giardi, P. (2013). Does the level of education influence completed suicide? A nationwide register study.J AffectDiscord, 147(437), 40th ser. doi:10.1016/j.jad.2012.08.046 Reger, M. A., Stanley, I. H., & Joiner, T. E. (2020). Suicide mortality and coronavirus disease 2019—a perfect storm? JAMA Psychiatry, 77(11), 1093. doi:10.1001/jamapsychiatry.2020.1060 Rural health care home. (n.d.). Retrieved March 02, 2021, from Universal Service Administrative Company Samson, L. F. (2019). Substance Abuse and Mental Health Services Administration (SAMHSA). Behavioral Health Services Information System Series: National Directory of MENTAL HEALTH TREATMENT FACILITIES. doi:10.4135/9781412971942.n379 Saunderson T, Haynes R, Langford IH. Urban–rural variations in suicides andundetermined deaths in England and Wales. J Public Health Med.1998;20:261–267. Crossref. PubMed. Mortality, 1970–1997. APHA Publications,92(7), 11611167. doi:https://doi.org/10.2105/AJPH.92.7.1161 Shepard, D. S., Gurewich, D., Lwin, A. K., Reed, G. A., & Silverman, M. M. (2015). Suicide and Suicidal Attempts in the United States: Costs and Policy Implications. Suicide and Life-Threatening Behavior, 46(3), 352-362.doi:10.1111/sltb.12225 Steelesmith, D. L., Fontanella, C. A., & Campo, J. V. (2019). Contextual Factors Associated With County-Level Suicide Rates in the United States, 1999 to 2016. Wilkinson, K. P., & Israel, G. D. (1984). Suicide and rurality in urban society. Suicide & life-threatening behavior, 14(3), 187–200. https://doi.org/10.1111/j.1943-278x.1984. tb00448.x

20 | Scientia 2021

Davis S. Crater, Anthony Pelster, Jorge Carmona Reyes, Mike R. Cook, Kenneth Ullibari, Truell W. Hyde, Ph.D. Department of Physics, Baylor University, Waco, TX

Abstract Space debris, such as that left by satellite collisions, has become a growing threat to spacecraft. To better map smaller debris and dust in orbits to which satellites and other spacecraft are placed, a dust detector onboard a cube-sat was sent to Low Earth Orbit, or LEO, to gather data on the population density of detritus. Data will be collected via voltage responses to impacts with a piezoelectric material, which will then be analyzed to find the impacting particle’s kinetic energy. Other areas in space where there is high demand to learn more about the dust populations are the Lagrange L4 and L5 points in the Earth-Moon system. For this, a second dust detector was designed, tested, and calibrated. This new device will be similar in function to the dust detector sent to LEO, but different in its design. It will gather data on the Kordylewski dust clouds recently observed. Index Terms: Cube-Sat, L4 and L5, Lagrange Points, LEO, Piezoelectric Effect, PZT

Introduction Satellites and other objects orbiting Earth do not always survive space in such a way as to remain functional in orbit, hence some fail to fall back into Earth’s atmosphere or be sent into a “graveyard orbit,” as is usually the plan for most project missions [1]. In recent years, this phenomenon has become more prevalent given the increase in space travel, GPS, and other industries very frequently sending satellites and other objects into Earth’s orbit [2]. When working space objects are impacted and break up, the clouds of fragments, referred to as space debris, contain particles down to the micrometer scale, yet are capable of doing significant damage to spacecraft due to the high velocities these particles travel. NASA’s Orbital Debris Observatory can monitor space debris larger than 10 cm in diameter, but smaller particles become difficult to track [3]. To better model the amount of currently untraceable space debris, Baylor University’s CASPER (Center for Astrophysics, Space Physics, and Engineering Research) collaborated with the University of Texas (UT) at Austin, to develop the Piezo Dust Detector (PDD) to be used on UT Austin’s Attitude Related Maneuvers and Debris Instrument in Low (L) Orbit (ARMADILLO), a cube-sat that would be placed in Low-Earth Orbit (LEO) and detect these micron-sized particles. Man-made debris is not the only type of debris posing a risk to spacecraft, as there also exists naturally occurring, yet equally harmful, dust. It has been observed recently that there are dust clouds, originally discovered by Kazimierz Kordylewski, at the Earth-Moon Lagrange points L4 and L5 [4][5]. A Lagrange

point is an area in space where the gravitational forces of two large masses, such as the Earth and Moon, are equal to the centrifugal force required for a far less massive object to move with them [6]. These dust clouds exist because the L4 and L5 points in the Earth-Moon system are more stable than the L1, L2, and L3 points shown in Figure 1 below [7]. While these Kordylewski Dust Clouds (KDC) have been observed, they have not been extensively monitored, nor have there been any probes sent to these points to gather data on the space dust. With a device such as the PDD, a cube-sat could be sent to the L4 and L5 points to gather information on and map the KDC.

Figure 1: A diagram of the Lagrange points of the Earth-Moon system. The Kordylewski clouds exist in the L4 and L5 areas.

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Original Research

PDD in LEO Data Analysis and PDD 2.0 for Dust Confirmation in Lagrange Points L4 and L5

Data Collecion The ARMADILLO, onboard a Space-X Falcon Heavy rocket, was recently launched from the Kennedy Space Center in Cape Canaveral, FL, and was successfully released at the predetermined orbit. When data is received from the PDD, interpreting it will be another challenge altogether. Because the data being received is just the data for a voltage vs. time plot (Figure 3), analyzing the data is not a simple task. There are a number of unknowns in calculating the possible kinetic energy at impact, which include all the basic parameters of the particle, such as size, mass, and impact velocity. PDD Signal Raw Data

50 40 30 20 10 0 -10 -20 -30 -40

500

1000

1500

2000

2500

Time (μs)

Figure 3: An example of the signal received from the PDD during testing.

22 | Scientia 2021

Figure 4: The drop tower setup with PDD, seen at the bottom in the middle of the drop zone, power supply for board stack to the right of the tower, oscilloscope and computer to the left. Filtered MDU Response

10 X 104

10 Frequency Voltage

PZT Plate

Amplitude

Figure 2: The MDU model not used onboard. The PZT plates are numbered in post to show orientation of the MDU and which plate is read as which by the computer. This is the outside of the cube-sat. For size reference, each of the numbered squares are about one square inch.

To determine a method of computing these unknown parameters, a set of data with known parameters was needed for comparison. To gather this data, a second PDD engineering model, which was originally developed and used for testing, has now been utilized for drop tests. Metal beads of approximately 2 mm in diameter were dropped at heights ranging from 20 cm to 70 cm via a drop tower instrument shown in Figure 4. The energy from the impact of the bead would be read as a voltage plot (Figure 5) and sent to a computer via the PDD’s board stack and program. The program used for the PDD receives the PZT’s electric charge, converts the voltage response from an analogue to a digital signal, then transfers this signal to the computer connected to the board stack where the signal can be analyzed.

Freq (Hz)

The ARMADILLO uses the onboard PDD to detect any collisions it may encounter with man-made space debris or natural dust while in its orbit. It can detect this debris with the use of its Main Detector Unit (MDU) consisting of nine leadzirconate-titanate, or PZT, plates as seen in Figure 2. Certain materials, as is the case with PZT, exhibit a phenomenon known as the piezoelectric effect. This causes any externally applied mechanical stress to generate an electric charge [8]. The design of the Piezo Dust Detector takes advantage of this property, transforming the mechanical energy from the dust impacts into electrical energy recognized and recorded by the cube-sat’s onboard computer.

Voltage Response (mV)

Original Research

ARMADILLO and PDD

Figure 5: An example of the voltage response received from the MDU. In this case, the bead hit PZT 5 and generated a voltage with an amplitude of about 70 mV. The average recorded voltage from each drop height was compared to the known impact kinetic energy of the bead from the same height using a ratio of energy to voltage response. This known kinetic energy was found using simple Newtonian kinematics. To simulate a real scenario, a set of random drop data from a certain height was given, and the task was to find the height from which the drops occurred. The recorded voltages from these drops were averaged and then compared to the ratio of known kinetic energy to voltage, allowing the unknown kinetic energy to be found. Assuming the drops were done with the same sized beads of the same material, the drop

Debris in LEO This simplicity will not apply to the real collection and analysis of data, as the particles’ size, mass, and velocity at impact are not known. To make this method of finding the particles’ impact energy more applicable to the real world, a range of different particle masses and velocities were used which might be found in LEO. NASA’s DAS 2.0.2 is a space debris program which models the amount of space debris of different sizes. A past research fellow in this same CASPER program, Frank Odom, used the DAS 2.0.2 software and found the results summarized in Table 1 [9]. Odom performed a simulation with this software “using the parameters of an orbit in LEO as well as the physical dimensions of ARMADILLO,” to calculate the likely particle sizes that the PDD will impact, and their likelihood of impacting. Particle Diameter (m)

1.00E-03 1.00E-04 1.00E-05 1.00E-06 1.00E-07 1.00E-08

Impacts per year

0.000562 0.316 15.85 63.10 1778.3 501187

Impacts per week

0.000011 0.00606 0.304 1.21 34.10 9612

Impacts per day

0.0000015 0.00087 0.043 0.17 4.87 1373

Table 1: The results of the DAS 2.0.2 simulation. (Note: this simulation was done with the assumption that the orbit of ARMADILLO would be at about 400 km. This is not always the case.) For velocity approximations, we know that the ARMADILLO’s orbit is elliptical with an apogee of approximately 850 km and a perigee of approximately 300 km. Based on this and the characteristics of orbit, the velocity of the cube-sat at apogee is calculated to be approximately 7.4 km/s and the velocity at perigee is approximately 7.8 km/s. For the debris velocity, once the particles are in orbit, they become trapped in Earth’s gravitational pull and hence have a very low-eccentricity elliptical orbit. Due to the objects’ circular orbit, a simple calculation can be made to find their average velocities. At a height of 400 km, it was found that the velocity will be between 7 km/s and 8 km/s. The assumption that the impacts are head-

on with angle of incidence zero implies the average impact velocity will be between 15 km/s and 16 km/s. This claim is made with the best assumptions possible for the expected environment. As for mass approximations, an assumption can be made that most debris in LEO is man-made. Satellites use aluminum because it’s a strong and reliable, yet lightweight, material; therefore, the average density of small debris in LEO will be that of aluminum, which is 2.8 g/cm3. Assuming spherical particles, the mass can be calculated by multiplying density and volume.

Data Results Using parameters that are the most likely scenario for impacts in LEO, the most feasible kinetic energies can be calculated. Because the minimum energy necessary to generate voltage for the PZTs used is 1 µJ, any particle 1 µm or smaller in diameter traveling at the assumed velocity will not have enough kinetic energy to trigger a response from the MDU. The following Table 2 is a list of reasonable kinetic energies resulting from their corresponding particles’ velocity and diameter. The calculated kinetic energies, as seen in Table 2 below, are the expected results from the voltage responses to impacts.

Impact Kinetic Energy (J) Particle Diameter (m) Impact Velocities (m/s)

0.01 mm

0.1 mm

1.0 mm

15000 m/s

1.596E-04

1.596E-01

1.596E+02

15500 m/s

1.705E-04

1.705E-01

1.596E+02

16000 m/s

1.816E-04

1.816E-01

1.596E+02

Table 2: The far-left column is the assumed impact velocity of the particle colliding with the dust detector in meters/second. The top row is the particle diameter in millimeters. The resultant impact kinetic energies were calculated in Joules.

Lagrange Points L4 and L5 The parameters and properties of the dust at the Lagrange L4 and L5 points are unknown and have not been studied extensively enough to provide accurate information. No characteristics of this dust, such as the clouds’ population, density, nor dust particle masses and velocities, are known. Hence, the goal of designing, testing, and calibrating a new dust detector was formulated.

Designing a New Dust Detector This new device would utilize the same impact-sensing PZT technology as the first iteration of the PDD. This dust detector, however, will be different in many ways. The first difference is in its PZT; the new detector is planned to use only one PZT per side, while the ARMADILLO PDD used 9 on its single side. The old PDD used square PZT plates, while the new version will use circular PZT disks with one of these disks pictured in Figure 6. Along with a difference in shape, the

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height can easily be computed. Using this procedure, the computed answer was within three centimeters of the correct drop height, with the error likely being the product of not having enough drop data. The standard deviation of the set of known data was 8.177 cm, while the standard deviation of the set of data with the unknown parameters was 17.62 cm. The set with known parameters and the set with unknown parameters had a skewness value of 0.093 and 0.112, respectively, meaning both data sets were close to normally distributed, but skewed slightly to the right. As for the error on the final height calculation, the correct height measurement was within the calculated value’s standard deviation.

dimensions 10x10x10 cm3) cube-sat made of aluminum was used, pictured in Figure 8. On one side of the proto cube-sat, a metal plate was mounted. Below the metal plate, the PZT disk (Figure 6) is sandwiched and pressed against the plate. Thus, when a particle impacts anywhere on the plate, triggering the PZT, an electrical signal will be delivered. This response voltage signal is relayed to an oscilloscope via a probe attached to the leads on the disk.

Figure 6: Pictured above is the PZT disk used in the testing of the new dust detector. This disk is 20 mm in diameter, with leads attached to the piezo material and a resonant frequency of about 2 kHz. Fourier Transform

1.2 1

Voltage RMS (mV)

Original Research

PZTs also have distinctive specifications, such as sensitivity and resonant frequency. The resonant frequency is a natural frequency at which an object or material vibrates, determined by physical parameters of the material. To test for the resonant frequency of the newer PZT, the same method of drop tests was used. Ball bearings were dropped using the drop tower pictured in Figure 4 and the response signal from the PZT collected via oscilloscope (the oscilloscope used is a Tektronix Series TDS 3032). Then, a Fast Fourier Transform (FFT) was applied to the raw data. An FFT converts a raw data signal into a representation of the signal in the frequency domain, giving an amplitude of the signal at each frequency. The resonant frequency of this new PZT was found to be approximately 2 kHz, as seen in Figure 7. This experimental measurement matches the manufacturer’s claim of a resonant frequency of 2 kHz.

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Figure 8: The prototype cube-sat. As seen in the photos, the PZT disk is pressed against a metal plate fixed to the model cube-sat (top), and the entire cube hooked up to the oscilloscope is pictured next to a ruler for scale.

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Figure 7: A Fourier Transform performed on raw PZT response data. As shown in the graph, the resonant frequency (seen as the frequency at which the highest amplitude peak occurs) is about 2 kHz. The voltage RMS (root mean square) response amplitude is shown to be 1.125 mV. Another way the new dust detector is different from the PDD is in the way the PZT itself is mounted. On the old PDD, the PZT plates were mounted to a frame with brackets and exposed to the elements. This design would prove to be fatal to the PZT plates if an impacting particle traveled with a high enough velocity. Thus, the design on the newer dust detection device protects the PZT from particles. For the testing, a prototype 1U (a cube with

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Testing and Calibration Using the drop tower instrument, steel ball bearings of 4.75 mm in diameter were dropped from many different heights and the response voltage recorded and relayed to the oscilloscope. Because there is only one PZT disk, there was assumed to be some sensitivity and loss of response amplitude if the drop impacts occurred farther from the center of the metal plate (where the PZT disk was mounted). To test this, a grid was drawn on the metal plate with lines separated by 2 cm, seen in Figure 8 (bottom). The particles would be dropped at each point where the lines on the grid intersected, with accuracy provided by a laser pointer positioned directly above the particle release point. Once the impacts at a certain height were completed, with enough drops to get statistically significant data (in some cases

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Figure 9: Pictured is the heat-map showing the average of the voltage amplitudes the PZT disk registers when a particle is impacted at each grid square. The square (3, 3) is the center, above where the PZT disk is mounted. This map is plotted with drops from 20 cm.

[3] Garcia, M. (2015). Space Debris and Human Spacecraft. https://www.nasa.gov/mission_pages/station/news/orbital_ debris.html [4] Sliz-Balogh, J., Barta, A., & Horvath, G. (2018a). Celestial Mechanics and Polarization Optics of the Kordylewski Dust Cloud in the Earth-Moon Lagrange Point L5 – Part I. Threedimensional celestial mechanical modelling of dust cloud formation. Royal Astronomical Society, MNRAS 480. [5] Sliz-Balogh, J., Barta, A., & Horvath, G. (2018b). Celestial Mechanics and Polarization Optics of the Kordylewski Dust Cloud in the Earth-Moon Lagrange Point L5 – Part II. Imaging Polarimetric Observation. Royal Astronomical Society, MNRAS 482. [6] Cornish, N. J. (2018). What is a Lagrange Point? https:// solarsystem.nasa.gov/resources/754/what-is-a-lagrangepoint/ [7] Wikipedia. (2020). Lagrange Point Colonization. https:// en.wikipedia.org/wiki/Lagrange_point_colonization [8] APC International, Ltd. (n.d.). What is “PZT”? https:// www.americanpiezo.com/piezo-theory/pzt.html [9] Odom III, F., Richter, G., Martinsen, B., Schmoke, J., Cook, M., Reyes, J. C., & Hyde, T. W. (2014). Piezo Dust Detector.

As expected, the center of the plate directly above the PZT had the highest average response amplitude, with the voltages dropping off as the impacts became farther away from the center.

Conclusions The ARMADILLO with the Piezo Dust Detector onboard was launched June 25, 2019 and is orbiting in LEO. Unfortunately, it started tumbling soon after being released and has yet to be corrected. Due to its tumbling, no data has been relayed from the PDD, and if there was data coming in, it would not be trusted since it is not traveling as designed. As for the new dust detection device, the next step in design and testing is to use a light gas gun to fire small particles at the detector to simulate higher velocity impacts. Configuring an onboard computer will be the final priority, which will operate using the same structures and algorithms as the ARMADILLO PDD.

Acknowledgements This research was supported by NASA JPL Contract No. 15 Grant No. 1571701, and the NSF Grant No. 1740203.

References [1] NASA. (2019). Where Do Old Satellites Go When They Die? https://spaceplace.nasa.gov/spacecraft-graveyard/en/ [2] Witze, A. (2018). The quest to conquer Earth’s space junk problem.https://www.nature.com/articles/d41586-018-

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this meant as many as 30 drops), an average of the peak response amplitude was recorded. These averages were plotted in a heatmap with the higher voltages showing as the darker colors, as seen in Figure 9.

Original Research

The Effects of Integrative BodyMind Training on Motor Deficits, Demonstrated through Laparoscopic Task Performance Laxmisanjana Ade, Caleb Eliazer, Nikita Mukkamala, Sanjanaa Senthilkumar, Megan Hudson, Emmie Jenkins, Marty Harvill, Ph.D. Department of Biology, Baylor University, Waco, TX

Abstract Surgeons are exposed to many stressors in their occupation which often results in burnout and can lead to deficits in motor function. The present study aimed to identify ways to negate the effects of stress on surgeons who are experiencing these motor deficiencies. Mindfulness meditation may help combat the negative effects of stress on surgical task performance. This study utilized Integrative Body-Mind Training (IBMT), a relevant form of mindfulness meditation. IBMT involves centering one’s focus on their body, breathing, and surrounding environment. Two mechanisms of this technique– improving attention and emotion regulation– have been shown to mitigate stress-related motor detriments. The participants (n=24), students in Baylor University’s laparoscopy course, performed a peg transfer task as a way to quantify task performance before and after a 20-minute IBMT session. The statistical analysis of the data, using dependent t-tests, resulted in a p-value of 0.097. There was no significant difference in performance after IBMT. Additionally, the study looked at differences among participants, including the effect of any previous exposure to meditation. IBMT showed a positive influence on laparoscopic task performance in participants without previous exposure to meditation (p=0.018). Given the rising rates of stress in the healthcare industry, IBMT may offset motor deficits, primarily caused by decreased attention and emotion regulation, in the context of laparoscopic surgery.

Introduction Numerous surveys indicate that stress in American adults has recently escalated. The American Psychological Association found that 44% of Americans believe that their stress levels have increased over the last five years (Clay et al., 2011). Stress is caused by a multitude of events called stressors— situations that provoke an aversive psychological or physiological response. These stressors range from difficult daily tasks to unexpected catastrophic events (Schniederman et al., 2005). Stressors can be divided into two different categories; those that are psychogenic and those that are neurogenic. Psychogenic stressors are associated with psychological stimuli, whereas neurogenic stressors are associated with physiological stimuli. An example of a psychogenic stressor is experiencing the death of a loved one, while an example of a neurogenic stressor is a headache (Anisman & Merali, 1999). The effect of the stressor is an adaptive process referred to as the “stress response.” The stress response involves a chain of behavioral, neurochemical, and immunological processes that serve to protect the individual from a stressful event (Anisman & Merali, 1999). Although most stress responses are beneficial, several studies indicate that some stress responses are correlated to a decline in mental and

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physical health. Several experiments have been conducted on animals and humans to investigate these various negative consequences. One study conducted on cynomolgus monkeys indicated that induced social stress accelerates the progression of atherosclerosis (Kaplan et al., 1982). Another investigation revealed that stressors can lead to immune dysregulation, causing the individual to become vulnerable to infectious agents (Glaser et al., 2005). Furthermore, a major consequence of stress that is necessary to explore is the decline of both awareness and motor function (Savtchouk & Liu, 2005). Stress can be detrimental in the workplace— especially in high-intensity professions. Physicians are exposed to high levels of stress which frequently results in burnout. Burnout is defined as a psychological symptom of chronic stress, and is composed of three dimensions: exhaustion, depersonalization, and reduced personal accomplishment (Maslach et al., 1996). When assessed using the Maslach Burnout Inventory, physicians have nearly twice the risk of burnout and work-life dissatisfaction compared to other professions (Tait et al., 2012). Stress may have a negative impact on physician

to improve attention. Attention consists of 3 discrete networks, the alerting, orienting, and executive networks, each activating distinct brain regions. These three networks have varying levels of importance when discussing the relevance of IBMT on task performance. The alerting system is involved in the production of vigilance in task performance, while the orienting system focuses on incorporating sensory input. The most relevant to the current study, the executive network, is primarily responsible for error detection and resolving conflicts when there are multiple potential behavioral responses (Peterson & Posner, 2012). Brain structures within this network, including the prefrontal cortex (PFC), inferior parietal lobe (IPL), and cerebellar regions, integrate feedback signals in response to past errors in order to continuously adjust decision-making. This mechanism is thought to lead to the reduction of errors (Dosenbach et al., 2007). Additionally, this network contains regions, such as the anterior cingulate cortex (ACC) and anterior insula/frontal operculum, that have been implicated in ensuring high levels of accuracy and speed in task performance (Braver & Barch, 2006). Cognitive tests have further emphasized the importance of the executive attention network in relation to IBMT. While cross-sectional studies have shown associations in the alerting and orienting networks, a longitudinal study showed that 5 days of IBMT led to a significant increase in activation of the executive attention network (Tang, 2011). This observed improvement is parallel to increased activation in certain brain regions during IBMT. A study that used functional imaging to investigate mindfulness found increased activation in the PFC for mindfulness in general, and the ACC for IBMT specifically (Tang et al., 2015). As mentioned earlier, these regions are involved in the executive attention network. IBMT has also been observed to affect emotion regulation. This technique has been associated with decreased activation of the amygdala, which is hypothesized to decrease emotional arousal (Tang et al., 2015). Together, the improvement of executive attention and increased emotion regulation are key factors in understanding how IBMT may be a relevant technique in improving task performance. The current study evaluated the efficacy of IBMT in combating the negative effects of chronic stress on task performance. As previously mentioned, there is an important link between chronic stress and errors, which can cause lethal mistakes in the medical field. Surgeons are under a great deal of stress which can lead to burnout; burnt out surgeons then commit major medical errors at a higher rate (Shanafelt et al., 2012). A potential explanation of the higher prevalence of major medical errors in physicians experiencing burnout could be the link between stress and decreased attention and emotional regulation. Chronic stress has been shown to affect brain regions involved in cognition and emotion regulation (McEwen & Morrison, 2013). The PFC, an affected region, has been linked to goal-directed behavior and is important for planning specific approaches/sequences (McEwen & Morrison, 2013). These sequences are critical to executive function and relate back to the executive network of attention. Additionally, the amygdala is also affected by chronic stress, potentially leading to dysfunction of emotion regulation (McEwen & Morrison, 2013). This dysfunction has the potential to worsen

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wellbeing and patient care. Around 9% of surgeons who participated in a national survey given to the members of the American College of Surgeons claimed to have made at least one major medical error in the past three months. Of those surgeons, those who are characterized as “burnt out” were more likely to have made errors. The study utilized burnout subscales from the Maslach Burnout Inventory to further analyze survey results. 59% of surgeons with emotional exhaustion reported an error, 71% struggling with depersonalization reported an error, and 27% of those lacking a sense of personal accomplishment have reported errors (Shanafelt et al., 2012). Burnout-related errors are attributed to the immense psychogenic, or cognitive stress, that surgeons experience. There are numerous mechanisms that have been observed to increase medical errors. Among these, a study conducted on rats observed that exposure to chronic stress resulted in limited rotatory limb movement and disrupted interlimb coordination (Metz et al., 2005). This study demonstrated that elevated levels of stress alter the execution of motor function. Surgeons cannot afford to make medical errors based in motor deficits as this can cause irreparable harm to a patient. The present study aimed to identify ways to improve task proficiency in surgeons who are burnt out and consequently experiencing motor deficiencies. Mindfulness meditation is a potential method to mitigate the motor deficits that surgeons experience due to stress. The tradition of mindfulness meditation relies on a deep awareness of internal and external environments (Chiesa & Malinowski, 2011). However, in recent years, clinicians have looked to mindfulness practices to combat a wide variety of issues. When looking to utilize mindfulness, it is prudent to consider the main components of the technique: attention control, emotion regulation, and altered self-awareness. Combined, improvements in these areas have the potential to subsequently increase selfregulation (Tang, 2015). Mindfulness meditation can be divided into two categories: group-based mindfulness interventions and mindfulness-related interventions. The group-based mindfulness interventions include: “mindfulness-based stress reduction” (MBSR) and “mindfulness-based cognitive therapy” (MBCT). The mindfulness-related interventions include: “acceptance and commitment therapy” (ACT), “dialectical behavior therapy” (DBT), and “integrative body-mind training” (IBMT) (Tang, 2017). These mindfulness techniques require varying levels of exposure, time commitment, and facilities. The current study chose to focus on IBMT. Consistent with all forms of mindfulness meditation, IBMT emphasizes increased awareness; however, in this technique the attention is focused on an individual’s body, external instructions, and breathing. IBMT has been observed to specifically increase activation in brain regions involved in attention and self-regulation (Tang, 2011). One study assessing the effects of IBMT on attention, found an improvement of Attention Network Test (ANT) scores following 5 days of 20-minute IBMT sessions (Tang, 2011). The ANT has become a standard test used to quantify attention in neuropsychological research (Posner, 2017 & MacLeod et al., 2010). These observed effects lay the framework for the usage of IBMT in the current study as a prime candidate in neutralizing the motor deficits of surgeons. The efficacy of IBMT in the current study lies in its ability

Original Research

task performance. Surgical techniques, which at a base level are simply motor tasks, are reliant on a series of cognitive processes. Among these processes are attention and emotion regulation, which IBMT directly affects (Song, 2019; Beatty and Janelle, 2019). The current study investigated the ability of IBMT to improve surgically relevant task performance.

Method Study Population The participants (n=24) of the study were 66% female, 33% male, and within the age range of 17 to 21 years old. The participants were students in Baylor University’s laparoscopic surgical training course. In the first half of the course, participants were taught foundational information required for the performance of laparoscopic surgery as detailed in the Fundamentals of Laparoscopic Surgery (FLS). In addition, participants learned performance tasks assessed by the manual skills component of the FLS exam; these include peg transfer, precision cutting, and ligating loop. After this training, participants were recruited for the current study. Materials Endopath Xcel Trocars (48 count), surgical marylands (48 count), Sterilite Ultra Latch Box (46 cm x 31.1 cm x 17.8 cm) (24 count), pegboard (24 count), HDMI cord (24 count), Logitech Camera (24 count), timer clock, laptop. These materials were obtained through Baylor University’s Laparoscopy Class. Professor Mike Whitenton executed the IBMT-based meditation session. Study Design Student participants performed the peg transfer task in a within-groups design. Task Description The peg transfer task was performed within a box imitating a body cavity. This box had two holes for trocar insertion; and within the trocars, two surgical marylands were inserted. Surgical marylands are used to maneuver within the abdomen in laparoscopic procedures. The box contained an FLS certified pegboard with six rubber triangular pieces. In the exercise, the triangles were lifted from a peg by one maryland and transferred mid-air to the other maryland. They were then placed onto a peg on the other side of the board. All six triangles must be set down on one side of the pegboard and then transferred back via the same technique. Task performance time began when the first triangle was touched with the maryland and ended when the last triangle was transferred back to the original side. The box also contained a camera attached to the bottom of the lid, enabling a full view of the pegboard. Control Day The participants entered the task performance room and sat in their randomly assigned seats. They then performed ten timed trials of the pegboard exercise. This data provided baseline results to compare to experimental data.

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Treatment Day Seven days after the control day, the participants entered the task performance room to set up their boxes, in a randomly assigned seat, in order to complete the task immediately following the IBMT session. The participants then entered the treatment room where they were led through a period of mindfulness meditation. The session lasted twenty minutes and participants performed IBMT under the direction of Dr. Whitenton (Fig. 1). Following the treatment, participants performed five timed trials of the pegboard exercise. In regard to the timed trials, participants were randomly assigned to a seat/box located in the task performance room via a random number generator. Participants followed a detailed instruction sheet in order to replicate the same set-up and performance from the control day. Notably, the instruction sheet requested silence during the entirety of the experiment. The treatment day instruction sheet differed slightly from that of the control day, in that it contained detailed instructions for both pre- and post-IBMT procedures. Each participant was assigned to a timer who ensured their compliance to instructions, recorded their performance, and scored them according to the equation: 1,000 - [(time in seconds) - 40 x (#errors)]. Scores were recorded on a data sheet. After completing the trials, students were instructed to remain in their assigned spots until all individuals completed their tasks. Background Data Collection After the treatment day, the study required participants to respond to a survey. The survey questions were as follows: 1. Gender 2. Were you feeling well today? 2a. If you answered no the previous question, please explain. 3. Describe your typical caffeine intake. 4. Describe your caffeine intake today. 5. Do you have previous experience doing yoga/meditation? 5a. If you answered yes to the previous question, was it self-guided or were you guided by a professional? 6. Do you believe meditation is effective? 7. Are you spiritual? 8. How many hours of sleep do you normally get? 9. How many hours of sleep did you get last night? 10. What time did you wake up this morning? 11. Describe your level of stress pre-IBMT session. 12. Describe your level of stress post-IBMT session but before starting pegboard trials. 13. Describe your level of stress during pegboard trials. 14. Describe your experience during the IBMT session. 15. Did you know who Dr. Whitenton was prior to today? 16. Did the silence make you feel uncomfortable throughout the session and pegboard trials? 17. Did you talk to anyone during the pegboard trials on the control day or the experimental day? 18. Please explain any extraneous problems that may have impacted your ability to relax during IBMT meditation. 19. Please explain any extraneous problems that may have impacted your trial times.

Results The study compared the mean task performance scores of the control day to that of the treatment day. Task performance was quantified according to the following equation, 1000(T+40E), where T represents the time (in seconds) and E represents the number of errors. Each participant’s “control day mean” was compared with their “treatment day mean”; there was no significant difference in task performance between control and treatment day (n=24; p=0.097). At an α value of 0.05, the study did not have enough evidence to suggest an improvement in task performance after IBMT. The independent variables used to create a regression model were caffeine, sleep, and previous exposure. The model failed to show an association between the variables (p=0.794). Further analyses were conducted by separating participants into a group with previous exposure to meditation (n= 10) and a group without previous exposure (n=14). Two separate dependent t-tests were then conducted to see if there was an increase in means (p=0.320 and 0.018, respectively). At an α value of 0.05, the study did not have enough evidence to suggest an improvement in task performance after IBMT in participants with previous exposure to meditation. However, there was sufficient evidence to suggest an improvement in task performances of the group without previous exposure.

Discussion The results of the current study fail to provide sufficient evidence that IBMT improves laparoscopic task proficiency, disallowing the rejection of the null hypothesis. In the study’s interpretation of data, higher scores are indicative of better laparoscopic performance and represent the combination of an efficient pace along with a small number of errors. The study evaluated the collected data using a dependent t-test. This method maximizes statistical power and eliminates individual differences by comparing control and experimental data of the same individual. This statistical method resulted in a p-value of 0.097. The study interprets the p-value of 0.097 as an indicator that there was a 9.7% chance that this data was purely coincidental. Accordingly, taking into account the p-value and the preset α value of 0.05, the study was not able to reject the null hypothesis that, on average, the mean scores of the control data were lower than the mean scores of the experimental data. The study also investigated how previous exposure to meditation may alter the efficacy of IBMT on improving task performance (Fig. 2). The participants were divided into two

groups: those who had previous exposure to meditation and those who did not. The study conducted additional dependent t-tests. The t-test corresponding to the group that had previous exposure to meditation resulted in a p-value of 0.32; while the group with no previous exposure resulted in a p-value of 0.018. For the group with previous exposure to meditation, the study does not have sufficient evidence to reject the null hypothesis. However, for the group of individuals without previous exposure to meditation, the p-value of 0.018 is less than the α value of 0.05. This allows the study to reject the null hypothesis that IBMT does not have a positive influence on laparoscopic task performance on those without exposure to meditation. Accordingly, the study predicts that not having any exposure or expectations toward an IBMT session may have a positive influence on the efficacy of IBMT on improving task performance. This indicates that IBMT may be useful in the field of laparoscopy for surgeons who have no previous experience with the technique. In the current study, the pegboard task and its associated scores were used to assess task performance of each individual before and after IBMT. It is evident that there are potential benefits that meditation, specifically IBMT, provides for those in high-stress professions; the current study focuses on the implications of these findings as they relate to laparoscopic surgery. Stressors potentially cause a decrease in the efficiency and accuracy of procedures executed by surgeons. IBMT serves as an avenue to mitigate the effects of stressors by improving attention and emotion regulation. In terms of the pegboard exercise, higher scores after IBMT could indicate that IBMT has stimulated these functions. Therefore, IBMT may allow surgeons to improve motor function in the context of laparoscopic surgery given their stressful profession. There are some considerations to be made when the findings of this study are applied to laparoscopic surgery. The study lacked precise, surgery-grade equipment, laparoscopic trainer boxes distributed by the Fundamentals of Laparoscopic Surgery (FLS) Program, and wasn’t conducted in an operating room which a surgeon would be used to. The boxes used in this study were made by multiple people which inevitably caused slight variations in the laparoscopy box; additionally, the boxes are less sophisticated than those used in FLS training. Furthermore, the pegboard task was set up in a standard classroom. This study would have a more practical application in the operating room if participants were surgeons rather than college students. Laparoscopic surgeons are exposed to many profession-specific stressors compared to university students. Additionally, in order to obtain the most accurate results, it would be ideal to have surgeons complete the task in a surgical setting in order to minimize extraneous variables impacting the effects of IBMT. Apart from the physical differences between the rooms, the operating room will be louder due to conversations and machines. The subjects in the study, however, were instructed to stay silent in order to preserve the effects of the IBMT. Since engaging in conversation could unravel the focus that was established in the meditation session, the study chose to make the testing environment completely silent, under the guidance of Dr. Whitenton. This was done to provide a foundational understanding of IBMT without confounding va-

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Statistical Analysis Dependent t-tests were used for significance testing and regression models were used to isolate functional relationships between various independent variables and task performance. The study conducted these tests assuming there was an adequate sample size, normally distributed data, and equal variance in standard deviation. These analyses were conducted, with the null hypothesis that IBMT has no positive effect on laparoscopic task proficiency, at an α value of 0.05.

Original Research

riables; however, this limits the practical significance of the study. Additionally, it is important to note that the sample size was limited to 24 participants. For future studies, a larger sample size would better represent the population, and potentially detect an effect of IBMT on task performance. There is still much work to be done to better assess the applications and efficacy of IBMT. In addition to having a higher-powered experiment, the current study has isolated two future directions: understanding the efficacy of IBMT on improving attention and emotion regulation and understanding the effects of these improvements on surgical task performance. In regard to the first direction, functional imaging must be used to isolate changes in activation levels before and after IBMT. The use of functional imaging may also reveal neural networks currently overlooked in IBMT literature. Isolating the specific networks affected is key to using the technique as an effective therapy. In regard to the second direction, the current study recommends using surgeons as participants. If the laparoscopic task performance of surgeons is compared before and after IBMT, this would be a better representation of the effects of the technique on surgical performance. Additionally, more longitudinal studies need to be completed in order to truly understand this technique. As evidenced by other research in the field, variation in the duration and repetition of IBMT sessions can lead to different effects. These variations need to be isolated and further studied. Ultimately, these two directions do not have to be distinct. It is the belief of the current study that the impact of future research would be maximized if there is integration between these two directions. One of the principles behind mindfulness is that our bodies and our minds kind of reflect the same attitude. So, our minds are going to be aware so our body posture should also match that. So what I want you to do is put your feet flat on the floor and I want your back to come off of the backrest. And if you need to help yourself you can come forward. You can let your hands rest on your lap. Whatever is comfortable and whatever gets out of the way. The whole idea is that your body is in a posture that is not burdensome, it’s alert. So take a few deep breaths, in through your nose and out through your mouth. This is going to be really the only time that we try to control our breathing. Just a couple more. And then on this next one, either if you haven’t already done so, let your eyes close, or if that is weird for you for some reason then that is totally fine, just kind of let your gaze soften right in the front of you. Like you are not really focusing on anything, but your eyes are open enough for light to come in. I am going to take your attention and make sure you are aware of where your body is. See if you can put your whole body in your field of awareness. Sit and know that you are sitting. Nothing magical about that. Nothing mystical about that. Just simply paying attention to the fact that you are sitting. How does it feel like in your body that you are sitting? See if you can move your attention from your whole body to the top of your head. Just that space right between your scalp and the hair. And just hold your attention there. You feel that? That spot right above your scalp. You might note what that feels like. You might note something like tingling. Not, oh I feel tingling, just tingling. It kind of separates you from the part of you that reserves from whatever you think you are when you think of you as a person. Tingling or warm, cold. And then progressively let your attention move down the top of your head, down toward your neck including your face. Run briskly down through the whole body in this way. So, noting any kind of tension or sensation. Oftentimes we carry a lot of tension in our eyes, our eyebrows and our forehead. And, see if you can just let that go. You might consider imagining ice melting in the water. And on the next breath water melts into gas. As the tension releases. And in all of this when your mind is distracted, when you realize your mind is caught up in thinking or that your attention goes to a sound in the room. Those actual moments when you realize that you are, oh there is thinking. Those are the moments conditionally called modes of mindfulness. Cause you are aware of what you are doing in this very moment. And then you bring your attention back to the feeling of your body in the chair, or the sound of my voice, or wherever your attention was in the body before your mind began to wander. So there is nothing really, technically that is a distraction. There are just different anchors of the attention that arise and pass away in the field of awareness. So we were up at the eyes, so let your attention fall to the shoulders, feeling the weight of your shoulders as they hang on your torso. If you haven’t already done so you can also stop trying to control the breathing. Your body breathes all the time. Most of the time we are not thinking of it at all because it does it all by itself. So you can kinda set the breath aside for now. Let the tension go in your shoulders. If there is any tension or sensation in your chest or the back you can imagine that also floating off out of your field of awareness as it softens. Then letting your attention move down to your torso and to the stomach. Here see if you can breathe into the belly. Most of the time our breathing takes place up in the shoulders, up in our chest. The thing about that is when we

breathe that way ordinarily that’s because our body is under stress. We are working hard and that cues our body to trigger a cascade of neurotransmitters and hormones we call the stress response. So our bodies at rest breathe most easily into the belly at least once our bodies are used to it. See if you can do that. Send that message to your brain that there is no reason to be amped up right now. See if you can take your attention from your belly to your sacrum to your pelvic bones to the base of your spine. And the feeling of your legs on your chair. The feeling of your feet on the floor, grounded. In yoga, you are in what we call a chair pose, a very supported chair pose. You might call the mind and consider what it is that, what quality the chair has that would be really great for you to embody. So maybe you do something like stability, strength. Chairs can withhold an incredible amount of weight because of the way they are designed. See if you can embody that quality, strength, stability whatever it is that comes up for you. You might notice your body sitting up a little straighter; you might notice your shoulders squaring. Just imagine that you are becoming that quality of strength, stability, rest. What does that feel like in your body? And again when your mind wanders, when you realize the mind has been lost in thought, gently, with our judgement, simply note and then bring your mind back to the feeling of your body on the chair. If that happens a thousand times in the next one minute that’s fine. The purpose of mindfulness is not to calm the mind, if by calm the mind we mean to cease thinking. It is not to empty our minds. It is simply to be aware that we are thinking. And there is a difference between thoughts and the world as it is. Thoughts come and go. They do not carry within themselves any particular value or any particular validity. They are simply thoughts. And so you can imagine that you are sitting in a chair but the chair is by a nice river. And as you watch fish swim by, those are sort of like your thoughts, they just come and go. Now see if you can find where in your body the breath is most. Maybe you feel it in the tip of your nose as the temperature cools. It could be at the very front of your nose. Or maybe it swirls in the back of your throat. Maybe you are most aware of the rising and falling of your chest or the expanding of your belly. Or maybe it is the whole body. THe whole body kind of feeling alive on the in breathe on the out breathe. Wherever it is you can follow one cycle of breath. From the very beginning of the in breathe to where ends. The pause, right, separating the in breathe and the out breathe. ANd then follow the breath with your attention all the way out, through the cycle. See if you can do that. There is no such thing as failing in mindfulness. Simply begin again whenever it is that you notice you are thinking. A lot of ways of noticing that you are thinking is the point of mindfulness. We have a tendency to think that we are not good at it if we notice that we are thinking, but it is actually the opposite. The more that you notice that you are thinking the more progress you are making. Being aware of the present moment really helps. If you find it helps you can start counting. If you are doing that I would recommend you only count the in-breath. And when you get to ten if you get to ten you start over. If your mind wanders you bring it back, to simply begin where you left off. There is nothing to do but simply be present with the moment that actually is. Including the rising and falling of different sounds in awareness. And noticing that the sounds that come and go are like the thoughts that come and go. Really everything that in our awareness comes and goes. We’ve had a lot of rain and dreary weather lately and you

might consider that our mind sometimes can look like that. That our minds sometimes are full of thoughts that refine upset or worrisome things. Those in this kind metaphor would be something like dark clouds, rain clouds, thunderstorm clouds. But we know that even beyond the thunderstorm clouds, or always beyond the thunderstorm clouds there is in fact a blue sky. And a mindfulness practice like this one underscores the fact that such a calm state of awareness as awareness of the world as it is. Of your place in it as it is, is always available to you. But the way to do that is to let your thoughts come and go and bring you attention back. So as we get close to the end of our meditation practice, as you begin to bring your attention away from the breath to viewing of your body on the chair to the sensation of your feet on the ground in front of you to your upright back to make sure you are embodying the supportive chair pose, to the sound of my voice, to the sound of the lights, to subtle sounds of shifting in the room. And in a minute I’ll ask you to open your eyes and get up and quietly go to your stations. I ask that you maintain this sense of calm awareness as you go over there as you conduct the laparoscopy part of this experiment. Because we want to be able to maintain and protect as much as we can the sense of awareness we cultivated. And not just you but cultivated by the people around you as well. So whenever you are ready, you can open your eyes, you can connect with the world around you and head over to your station. Thank you.

Figure 1: The figure above is a transcribed version of the IBMT session Dr. Whitenton conducted on the study’s “Treatment Day”.

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Normal Probability

0.025

0.030

Original Research

Difference

0.015

Densities

0.020

0.000

0.005

0.010

-20

850

900

950

1000 0

Scores

Figure 2: The figure above shows the normal distribution curves in relation to the normal distribution curves of the experimental units with previous exposure to meditation vs. those without exposure. The control data is represented by a dashed line, the data from the experimental units with previous exposure is represented by a solid black curve and the data from the experimental units with no previous exposure is represented by a solid green curve. p-value: 0.327, 0.018 (respectively).

Student

Figure 3: Describes individual score differences- the difference was calculated by subtracting experimental score from control score. The green dots indicate a negative difference and the black dots indicate a positive difference. The negative difference indicates a better score. p-value: 0.097

Participant Scores on Experiment and Control Days 980 960

Scores

940 920

experimental

900

control

880 860

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Participant IDs

Figure 4: The figure above shows two lines differentiating control and experimental scores. Participants were randomly numbered from 1-24. p-value: 0.097

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References Anisman, H., & Merali, Z. (1999). Understanding stress: Characteristics and caveats. Retrieved from https://www. ncbi.nlm.nih.gov/pmc/articles/PMC6760382/ Braver, T. S., & Barch, D. M. (2006, October 30). Extracting core components of cognitive control. Retrieved from https://www.sciencedirect.com/science/article/pii/ S1364661306002737?via=ihub Chiesa, A., & Malinowski, P. (2011, January 19). Mindfulness‐ based approaches: Are they all the same? Retrieved from https://onlinelibrary.wiley.com/doi/full/10.1002/jclp.20776 Clay, R. A. (2011). Stressed in America. PsycEXTRA Dataset. doi:10.1037/e667212010-024 Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., . . . Petersen, S. E. (2007, June 26). Distinct brain networks for adaptive and stable task control in humans. Retrieved from https://www.ncbi.nlm. nih.gov/pmc/articles/PMC1904171/#B5 Glaser, R., & Kiecolt-Glaser, J. K. (2005). Stress-induced immune dysfunction: Implications for health. Nature Reviews Immunology, 5(3), 243-251. doi:10.1038/nri1571 Kaplan JR;Manuck SB;Clarkson TB;Lusso FM;Taub DM;. (n.d.). Social status, environment, and atherosclerosis in cynomolgus monkeys. Retrieved from https://pubmed.ncbi. nlm.nih.gov/6889852/ Macleod, J. W., Lawrence, M. A., Mcconnell, M. M., Eskes, G. A., Klein, R. M., & Shore, D. I. (2010). Appraising the ANT: Psychometric and theoretical considerations of the Attention Network Test. Neuropsychology, 24(5), 637-651. doi:10.1037/ a0019803 Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach Burnout Inventory Manual (3rd ed.). Mountain View, CA: CPP, Inc. Mcewen, B., & Morrison, J. (2013). The Brain on Stress: Vulnerability and Plasticity of the Prefrontal Cortex over the Life Course. Neuron, 79(1), 16-29. doi:10.1016/j. neuron.2013.06.028 Metz, G. A., Jadavji, N. M., & Smith, L. K. (2005). Modulation of motor function by stress: a novel concept of the effects of stress and corticosterone on behavior. The European Journal of Neuroscience, 22(5), 1190–1200. https://doi.org/10.1111/ j.1460-9568.2005.04285.x Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Retrieved from https://www. ncbi.nlm.nih.gov/pmc/articles/PMC3413263/#R20 Posner, M. I. (2017). Attentional Mechanisms. Reference Module in Neuroscience and Biobehavioral Psychology. doi:10.1016/ b978-0-12-809324-5.04323-6 Savtchouk, I., & Liu, S. J. (2011, January 12). Remodeling of Synaptic AMPA Receptor Subtype Alters the Probability and Pattern of Action Potential Firing. Retrieved from https:// www.jneurosci.org/content/31/2/501 Schneiderman, N., Ironson, G., & Siegel, S. D. (2005). Stress and health: Psychological, behavioral, and biological determinants. Retrieved from https://www.ncbi.nlm.nih. gov/pmc/articles/PMC2568977/ Song, J. (2019). The role of attention in motor control and

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learning. Current Opinion in Psychology, 29, 261-265. doi:10.1016/j.copsyc.2019.08.002 Tait D. Shanafelt, M. (2012, October 08). Burnout and Satisfaction With Work-Life Balance Among US Physicians Relative to the General US Population. Retrieved from https://jamanetwork.com/journals/jamainternalmedicine/ fullarticle/1351351 Tang, Y., Jiang, C., & Tang, R. (2017, May 26). How Mind-Body Practice Works-Integration or Separation? Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445124/ Tang, Y. (2011). Mechanism of Integrative Body-Mind Training. Neuroscience Bulletin, 27(6), 383-388. doi:10.1007/s12264011-1141-2 Tang, Y., Hölzel, B. K., & Posner, M. I. (2015, March 18). The neuroscience of mindfulness meditation. Retrieved from https://www.nature.com/articles/nrn3916

Maquela Noel¹ and Dawn Misra² 1 Health, Kinesiology, Leisure Studies, Baylor University, Waco, TX 2 Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI

Abstract The relationship between the father and mother during pregnancy has been linked to pregnancy outcomes. However, factors that influence this relationship have not been studied in depth. Adverse childhood experiences (ACEs) of the father have been associated with poor health and functioning in adulthood, and may be important factors that impact the prenatal relationship between the parents. We recruited 117 fathers from a study that examined the risk of preterm birth in pregnant Black women. Of the fathers recruited, 113 completed questionnaires and provided survey data to determine behavioral, health, psychosocial, social, and sociodemographic characteristics. The mean ACEs score represents 107 fathers with 6 missing. The fathers’ ACEs scores were measured using a 10-item self-report survey assessing whether each of the presented ACEs were experienced by the participant prior to the age of 18 years. Perceived conflict in the relationship between the father of the baby and mother of the baby was measured using a 5-item questionnaire scored on the 5-point Likert scale. For the Likert scale, 112 fathers provided data with 1 missing. A t-test was then used to compare the average score on the conflict scale with the mean ACEs of 67 fathers with 2 or fewer ACEs, and 40 fathers with 3 or more ACEs (6 missing). This study hypothesized that fathers with higher rates of exposure to ACEs would have higher scores on the conflict scale, indicating more conflict with the mother. The average score on the conflict scale was significantly higher (difference in means, 2.20 points, p=0.023) for fathers with an ACEs of 3 or more (mean± standard deviation: 12.2±4.6) relative to those with an ACEs of 2 or lower (mean± standard deviation:14.4±5.0). Keywords: father of baby (FOB), mother of baby (MOB), conflict, support, pregnancy, prenatal period

Introduction The relationship between the father and mother throughout the prenatal stage of pregnancy may present both risk factors and protective factors that impact the likelihood of the mother and baby experiencing adverse outcomes during and after pregnancy. Previous research has examined how the father of the baby’s (FOB’s) involvement and contact with the mother of the baby (MOB) throughout pregnancy results in reduced depressive symptoms and a higher degree of psychological wellbeing (Giurgescu et al., 2018; Giurgescu and Misra, 2018; Misra et al., 2010). A closer relationship between the FOB and MOB could also reduce the mother’s stress during the pregnancy. In a report by Eboh et al. (2018), women who reported a mostly close relationship with the FOB prior to pregnancy had perceived stress scores about 3 points lower (p<0.01) than women who characterized the relationship with the FOB as any response other than mostly close. Conversely, mothers with less-involved FOBs have reported higher levels of depressive symptoms and other stress-related symptoms compared to mothers with moreinvolved FOBs (Giurgescu and Templin, 2015). Given that high

stress-levels, low social support, and high depressive symptoms are linked to increased risk of adverse pregnancy outcomes, investigating how dissatisfying partner relationships and poor perceived support can affect the quality of the mother-father relationship could lead to possible interventions that improve outcomes for both the mother and the baby (Biaggi et al., 2016). The construct of adverse childhood experiences (ACEs) was derived from a study that began in 1995 and included 17,000 participants from Southern California. Participants in the initial study completed surveys and received physical examinations to provide information on each person’s childhood experiences and health behaviors (CDC, 2020a). ACEs were defined as traumatic events that occurred during an individual’s childhood including violence, abuse, neglect, mental health issues, suicide of a parent and/or family member in the same household, and household dysfunction (e.g. parent and/or family member being in prison or parental divorce) (CDC, 2020a). ACEs are described as stressful and potentially disturbing events experienced during childhood that are associated with long-term behavioral issues

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Original Research

The Impact of a Father’s Adverse Childhood Experiences (ACEs) on the Relationship He Has with the Mother of His Baby

Original Research

and chronic conditions later in life (Schickendanz et al., 2018). In a research study conducted in 2013, Mersky et al. showed a strong correlation between higher levels of childhood adversity measured on the ACEs scales and poor health outcomes, dissatisfaction in life, depressive symptoms, tobacco usage, and alcohol consumption. In this study, individuals exposed to several adverse childhood experiences were more likely to have 3 or more negative outcomes (odds ratios (OR) range = 2.7510.15). Other investigations also report that a person’s exposure to ACEs can predict future physical and psychological health (Danese et al., 2009; Schilling, Aseltine, & Gore, 2007). A study conducted by Afifi et al. in 2008 focused on examining the prevalence of psychiatric disorders and suicide among a population that was exposed to childhood abuse and violence. Of the individuals who experienced ACEs in the studied population, 22% to 32% of the women and 20% to 24% of the men were diagnosed with psychiatric disorders (Afifi et al., 2008). In summary, ACEs appear to have long-lasting effects on both physical and psychological health. While more studies have begun to examine the impact of the father’s involvement during and after pregnancy, studies do not appear to have examined how a father’s childhood could play a role in his level of involvement. However, ACEs might influence the relationship the FOB has with the MOB. A limited number of peer-reviewed papers published prior to June 2020 proposed ACEs or other measures of childhood adversity as possible influences on the FOB’s relationship with the MOB. This study examines if traumatic events experienced by the FOB during childhood are associated with conflict in his relationship with the MOB during pregnancy.

Methods Study Design and Population This is an analysis using data from the Fathers Matter study, a prospective study of fathers who were identified through the pregnant Black women participating in the Biosocial Impact on Black Births (BIBB) study. The pregnant women were recruited from prenatal clinics in the Detroit metro area of Michigan and the Columbus metro area of Ohio. Women in Metro Detroit were recruited from two hospitals in the Ascension Health System: Providence Hospital (Southfield, MI) and St. John Hospital (Detroit, MI). The mothers from Columbus were recruited from the Ohio State University prenatal clinic. Women included in the study identified themselves as Black, older than 18 years of age, experiencing a first-time pregnancy, and less than 29 weeks into the pregnancy. Women who had multiple pregnancies were not eligible and were not included in the study. Women who were incarcerated or had a known, serious cognitive deficiency were excluded as they could not provide informed consent. The fathers included in this analysis were recruited from May 2018 to March 2020. Fathers included in the study were recruited through their association with the mothers as the women provided the contact information for the FOBs. At the time the FOB data was analyzed, 610 mothers were enrolled in the Biosocial Impact on Black Births (BIBB) study, but the father recruitment started after about the first 30 moms were enrolled and we started with only the MI sites, and added Ohio

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later. 117 fathers provided informed consent and participated in the study. Of the 117 fathers who provided consent to participate, 113 of the fathers provided survey data. Fathers included in the study were required to be 18 years of age or older but did not necessarily have to identify as Black. Fathers who were incarcerated or had a known, serious cognitive deficiency were excluded as they could not provide informed consent. Fathers were asked to complete two questionnaires: prenatal and postpartum. A saliva specimen was collected from each participant to measure telomere length. The primary focus of the study was to determine the impact of the father’s involvement on maternal risk factors and protective factors, the quality of the mother-father relationship, and how these characteristics influence birth outcomes. In this paper, the framework of the original study was incorporated to examine the father’s ACEs and how they may affect his relationship with the mother of the baby during her pregnancy. Independent Variables Measure: Adverse Childhood Experiences (ACEs) Scale The adverse childhood experiences (ACEs) scale measures abuse, neglect, and household dysfunctions experienced prior to the age of 18 years (Schickendanz et al., 2018). The ACEs score was collected as a continuous variable in this study. The scale included 10 questions that assessed the father’s ACEs. Examples of the questions presented in the survey include: “Did a parent or other adult in the household often swear at you, insult you, put you down, or humiliate you?,” “Did you often feel that you didn’t have enough to eat, had to wear dirty clothes, and had no one to protect you?,” and “Were your parents ever separated or divorced?.” The options for each of these questions were a “yes” (1 point was added to the score total) or “no” (0 points were added to the score total). The theoretical range of ACEs scores was 0 to 10. Participants were then assigned into 2 groups based on their total score obtained from the survey. The first group contained individuals who scored 0 to 2 points, indicating a low level of adversity experienced by the father during childhood. The second group consisted of individuals who scored a 3 or higher, reflecting a higher number of adverse events occurring during the father’s childhood. Outcome Measure: Father of baby (FOB) Prenatal Relationship with Mother of baby (MOB) The degree of conflict in the FOB’s relationship with the MOB— the perceived conflict— was measured using a 5-item survey with 5 categorical Likert responses ranging from “strongly disagree” to “strongly agree.” The participants were asked to respond to statements including: “MOB is often critical (disapproving) of me,” “I sometimes fight or argue with MOB,” and “My relationship with MOB sometimes makes me feel tense.” Based on the father’s responses, a conflict score was calculated to quantify the level of conflict in the relationship between the FOB and the MOB. Numerical values ranging from 1 to 5 were assigned to each of the Likert responses with 1 representing “strongly disagree” and 5 representing “strongly agree”. The theoretical range of the full scale (5 items summed) was between 5 and 25 points. Relationship characteristics including the nature of the relationship (e.g., married), frequency of contact

with the MOB, and frequency of FOB accompanying MOB prenatal care visits were also examined.

Statistical Data Analysis The ACEs score was a continuous variable and had a theoretical range of 0 to 10. Based on the ACEs distribution in our sample, two dichotomous variables were created: 0 to 2 versus 3 or higher. A t-test was used to determine the statistical difference between the means in the conflict scale (Kim, 2015).

Results Table 1 describes the sample of fathers in the study. 69.9% of the fathers reported were Black/African American. There is a large proportion of missing data on race/ethnicity because this question was not included in the early phase of the study. There was a high rate of chronic conditions with nearly half of the fathers reporting that they had asthma. 55.8% of the fathers reported “ever smoking” and 80.5% reported “ever consuming alcohol.” The FOBs in the sample reported their individual household income. Approximately 51.3% of FOBs reported an annual income of $10,000 or less. In terms of perceived financial situation, 61.1% reported that they had “enough to get by but no more.” 23.0% reported that they had “barely enough to get by” and 6.2% “not enough to get by.” Of note, the fathers’ childhood financial situations were generally below the poverty line, but this information was not compared to financial situations at the time of the study to determine the degree of social mobility. Most fathers were married or living with a partner, who was not necessarily the mother of the baby. With regard to their involvement with the MOB and their relationship, nearly all of the fathers reported that they were in daily contact with the MOB and about two thirds attended most of the prenatal visits with the MOB. The fathers were categorized into 2 groups based on their ACEs scores (see Methods): 2 or fewer (n=67), and 3 or more (n=40), with approximately 62.6% of fathers having a score ≤2 and 37.4% having a score ≥ 3. The conflict scale for the relationship with the MOB had a range of 5 to 25 with an average of approximately 12.97 points. We compared average scores on the conflict scale by separating FOBs into two separate

Discussion Based on the study findings, a father’s adverse childhood experiences may be associated with more conflict in his relationship (during pregnancy) with the mother of the baby. Results reflected a correlation between higher ACEs (3 or more) and higher levels of conflict in the MOB and FOB’s relationship. An ACEs score of 2 or less indicates little exposure to adversity in childhood. A high ACEs score reflects greater exposure to adversity in childhood. Prior studies have related the motherfather relationship to birth outcomes. A limited number of peer-reviewed papers prior to June 2020 have examined ACEs with regard to the prenatal mother-father relationship, especially among Black families. Additionally, relationship dissatisfaction is a pertinent risk factor that can be studied in more detail. Twenge et a. (2003) conducted a meta-analysis that demonstrated that married couples with no children had higher levels of relationship satisfaction relative to married couples with children. This meta-analysis examined relationship satisfaction among couples and focused on comparing the level of relationship satisfaction of the mother and father. The analysis determined that mothers with infants reported 38% relationship satisfaction, whereas women without children reported 62% satisfaction. Additionally, Meyer et al. (2016) conducted a cross-sectional study that focused primarily on the factors of parenthood, the number of children parents have, and the children’s ages. The study found that couples with no children have higher levels of cohesion in their relationship, more relationship satisfaction, and greater levels of affection relative to couples with 2 or 3 children. Moreover, the Meyer et al. (2016) study revealed that couples who have children demonstrate lower levels of relationship satisfaction as the couple’s child(ren) age. The Twenge et al. (2003) and Meyer et al. (2016) studies support the idea that couples with multiple, older children have lower rates of relationship satisfaction than couples with younger or no children. Mitnick et al. (2009) also conducted a study that examined couples’ relationship satisfaction as the couple prepared for parenthood. The study revealed relationship satisfaction declined from the time of pregnancy to the time the child reaches 14 months of age. Thus, relationship dissatisfaction may be an important risk factor that can be identified prenatally in order to provide assistance to couples and thus improve their relationship and pregnancy outcomes.

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Measures of Socio-demographic Characteristics and Lifestyle Behaviors The information collected in the fathers’ questionnaire included race and/or ethnicity, age, highest level of education, marital status, annual household income, current financial situation, and financial situation of the father in childhood. Lifestyle behaviors were assessed using survey questions about whether the FOB smoked cigarettes, consumed alcoholic beverages, and engaged in physical activity. The FOB’s health was measured using questions regarding chronic health conditions (asthma, diabetes, hypertension, and thyroid problems). These variables were used to describe the sample. The variables in this study provides context of which type of participants are being studied, relating to external validity. These variables are not identified as potential confounders of the association under study, specifically the association between relationship conflict between the MOB and the FOB, and the FOB’s ACEs.

ACEs groups and using a t-test to determine whether differences were statistically significant. The average score on the conflict scale was significantly higher (difference in means, 2.20 points, p=0.023) for FOBs with an ACEs score of 3 or more (mean±standard deviation: 12.2±4.6) in contrast to FOBs with an ACEs score of 2 or lower (mean±standard deviation:14.4±5.0).

Original Research

FOB Variable Results Table Variable

Response

Percentage (%)

Sample Size (n)

Race/Ethnicity

Black only Black, white Black, Native American Black, Middle Eastern White only White, Native American Other only Unknown

69.9% 0.9% 0.9% 0.9% 0.9% 0.9% 5.3% 20.4%

Yes No Yes

55.8% 44.2% 80.5%

79 1 1 1 1 1 6 23 113 63 50 91

No Less than High School Graduated High School or GED Technical/Vocational Training Some college Associate degree Less than $10,000

19.5% 12.4% 69.9%

22 14 79

2.7%

12.4% 2.7% 51.3%

14 3 58

$10,000-$19,999 $20,000-$29,999 $30,000-$39,999 $40,000-$59,999 $60,000-$79,999

15.9% 16.8% 10.6% 3.5% 1.8%

18 19 12 4 2

Age (years) Ever Smoked Ever Consumed Alcohol Highest Education

Household Income per year ($)

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Mean ± Standard Deviation

27.1±7.2

Very poor, not enough to get by Barely enough to get by Have enough to get by but no extras Have more than enough to get by Well to do Childhood Financial Situ- Very poor, not enough to ation get by Barely enough to get by Have enough to get by but no extras Have more than enough to get by Well to do 1 Marital Status Married Living with Partner Divorced Separated Never Married Married/living with mother Yes of baby (MOB)2 No Contact with MOB Nearly everyday At least once a week A few times a month Attend prenatal visits with All of the time MOB Most of the time Some of the time None of the time FOB ACEs Total3 FOB High More Conflict4 1

6.2%

23.0% 61.1%

26 69

7.1%

2.7% 12.4%

3 14

30.1% 42.5%

34 48

10.6%

4.4% 10.6% 44.2% .9% 1.8% 41.6% 42.5%

5 12 50 1 2 47 48

29.2% 97.3% .9% 1.8% 62.8%

33 110 1 2 71

18.6% 16.8% 1.8%

21 19 2

Original Research

Current Family Financial Situation

107 112

Missing n=1, 2 Missing n=32, 3Missing n=6, 4Missing n=1

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Original Research

Limitations The limited demographic range studied as well as possible selection bias may limit the generalizability of the information. Our study examined fathers linked to Black mothers in Detroit, Michigan; Southfield, Michigan; and Columbus, Ohio. While the study largely focused on Black families (most fathers whose race was provided identified as Black) in a specific region, our findings may be applicable to families of different racial backgrounds or to families in different parts of the U.S. In terms of selection bias, only fathers interested in participating were studied. The fathers surveyed may be different from fathers who did not participate. There is a possibility that certain groups were overrepresented or underrepresented. For example, our study may be biased towards fathers with lower rates of conflict and adverse childhood experiences. Due to the small sample size, study findings may be affected by confounding factors.

Conclusion Based on our study, fathers who experience 2 or fewer ACEs have less conflict in their relationship with the MOB compared to fathers with 3 or more ACEs. Fathers with higher ACEs scores may have experiences that produce long-lasting harm in their ability to develop good relationships as adults. Health care and social services providers may need to provide special services to these fathers to ensure a better relationship with the mother of the baby. Possible childhood interventions could include school counselors providing guidance and psychoeducation that focuses on establishing healthy relationships and developing effective stress management and parenting skills to parents and children (Griffith, 2018). To combat the negative effects of ACEs in adulthood, possible intervention measures could include parenting programs, relationship enhancement programs, and establishing a healthy father-child relationship (Charles et al., 2006; Conger et al. 1999). The establishment of healthy relationships can potentially reduce the possible inequalities associated with childhood adversity (Child Welfare Information Gateway, n.d.; Madsen & Abell, 2010). Hawkins et al. (2005) revealed that enhancement programs on an individual or relationship level can be preventative. Relationship programs could also educate couples and individuals in ways to properly communicate and resolve conflict (Hawkins et al., 2008; Stanley et al., 1998). The intervention measures mentioned can help combat the negative effects associated with exposure to hardships and allow improvement in relationships (Charles et al., 2006; Karney et al., 2005). Future research could measure ACEs in mothers and how the ACEs can influence her involvement and/ or parenting with her baby.

Acknowledgements We would like to thank the men and women who participated in the study. We also want to extend gratitude to the Michigan State University Summer Research Opportunities Program, the Department of Epidemiology and Biostatistics at Michigan State University, and Dr. Dawn Misra and her laboratory research group.

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References Afifi, T. O., Enns, M. W., Cox, B. J., Asmundson, G. J., Stein, M. B., & Sareen, J. (2008). Population attributable fractions of psychiatric disorders and suicide ideation and attempts associated with adverse childhood experiences. American journal of public health, 98(5), 946–952. https://doi. org/10.2105/AJPH.2007.120253 Biaggi, A., Conroy, S., Pawlby, S., & Pariante, C. M. (2016, February). Identifying the women at risk of antenatal anxiety and depression: A systematic review. Journal of affective disorders. https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC4879174/#bib88. Centers for Disease Control and Prevention. (2020, April 13). About the CDC-Kaiser ACE Study |Violence Prevention|Injury Center|CDC. https://www.cdc.gov/ violenceprevention/aces/about.html. Centers for Disease Control and Prevention. (2020, April 3). Preventing Adverse Childhood Expriences |Violence Prevention|Injury Center|CDC. https://www.cdc.gov/ violenceprevention/aces/fastfact.html. Charles, P., Orthner, D. K., Jones, A., & Mancini, D. (2008, October 17). Poverty and Couple Relationships. https:// www.tandfonline.com/doi/abs/10.1300/J002v39n01_03. Conger, R. D., Rueter, M. A., & Elder, G. H., Jr. (1999). Couple Resilience to Economic Pressure. Journal of Personality and Social Psychology, 76(1), 54–71. https://doi. org/10.1037/0022-3514.76.1.54. Danese, A., Moffitt, T., Harrington, H., Milne, B., Polanczyk, G., Pariante, C., . . . Caspi, A. (2009, December). Adverse Childhood Experiences and Adult Risk Factors for Agerelated Disease: Depression, Inflammation, and Clustering of Metabolic Risk Markers. https://pubmed.ncbi.nlm.nih. gov/19996051/. Eboh, R. N., Giurgescu, C., & Misra, D. P. (2018). Relationship with the Father of the Baby and Perceived Stress Among Black Women. MCN. The American journal of maternal child nursing. http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC6118217/. Giurgescu, C., & Misra, D. P. (2018). Psychosocial Factors and Preterm Birth Among Black Mothers and Fathers. MCN. The American journal of maternal child nursing. https:// www.ncbi.nlm.nih.gov/pubmed/29944478. Giurgescu, C., & Templin, T. N. (2015). Father Involvement and Psychological Well-Being of Pregnant Women. MCN. The American journal of maternal child nursing. http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC4617560/. Giurgescu, C., Fahmy, L., Slaughter-Acey, J., Nowak, A., Caldwell, C., &amp; Misra, D. (2018, March 30). Can Support from the Father of the Baby Buffer the Adverse Effects of Depressive Symptoms on Risk of Preterm Birth in Black Families?, https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC6070463/. Griffith, S. (2018). The Relationship Between Childhood Adversity and Adult Relationship Health for Economically Marginalized, Racially and Ethnically Diverse Individuals. https://digitalcommons.odu.edu/chs_etds/22/.

Original Research

Hawkins, A. J., Blanchard, V. L., Baldwin, S. A., & Fawcett, E. B. (2008). Does Marriage and Relationship Education Work? A Meta-Analytic study. Journal of Consulting and Clinical Psychology, 76(5), 723-734. Kim, T. (2015, December). T test as a Parametric Statistic. http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC4667138/. Lu, M. C., & Chen, B. (2004). Racial and Ethnic Disparities inPreterm Birth: The Role of Stressful Life Events. American Journal of Obstetrics and Gynecology. https://pubmed.ncbi. nlm.nih.gov/15467527/. Madsen, M. D., & Abell, N. (2010). Trauma Resilience Scale: Validation of Protective Factors Associated with Adaptation Following Violence. Research on Social Work Practice, 20, 223-233. Mersky, J. P., Topitzes, J., & Reynolds, A. J. (2013, November). Impacts of adverse childhood experiences on health, mental health, and substance use in early adulthood: a cohort study of an urban, minority sample in the U.S. Child abuse & neglect. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4090696/. Meyer, D., Robinson, B., Cohn, A., Gildenblatt, L., & Barkley, S. (2016, September 21). The Possible Trajectory of Relationship Satisfaction Across the Longevity of a Romantic Partnership: Is There a Golden Age of Parenting? . SAGE Journals. https:// journals.sagepub.com/doi/abs/10.1177/1066480716670141. Mitnick, D., Heyman, R., & Smith Slep, A. (2009). Changes in Relationship Satisfaction across the Transition to Parenthood: A Meta-Analysis. Journal of Family Psychology, 23, 848– 852. doi:10.1037/ a0017004. Karney, B. R., & Bradbury, T. N. (2005). Contextual Influences on Marriage: Implications for Policy and Intervention. Current Directions in Psychological Science, 14, 171-174. Misra, D. P., Caldwell, C., Young, A. A., & Abelson, S. (2010, February). Do fathers matter? Paternal contributions to birth outcomes and racial disparities. American journal of obstetrics and gynecology. https://www.ncbi.nlm.nih.gov/ pubmed/20113687. Schickedanz, A., Halfon, N., Sastry, N., & Chung, P. J. (2018, August). Parents’ Adverse Childhood Experiences and Their Children’s Behavioral Health Problems. Pediatrics. http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC6317990/. Schilling, E. A., Aseltine, R. H., & Gore, S. (2007, March 7). Adverse childhood experiences and mental health in young adults: a longitudinal survey. BMC public health. http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC1832182/. Stanley, S. M., & Markman, H. J. (1998). Acting on What We Know: The Hope of Prevention. In Strategies to Strengthen Marriage: What We Know, What We Need to Know. Washington DC: The Family Impact Seminar. Twenge, J. M., Campbell, W. K., & Foster, C. A. (2003). Parenthood and Marital Satisfaction: A Meta-Analytic Review. Journal of Marriage and Family, 65, 574–583. http:// dx.doi. org/10.1111/j.1741-3737.2003.00574.x. U.S. Department of Health & Human Services. (2015). Protective Factors to Promote Well-Being. https://www.childwelfare. gov/topics/preventing/promoting/protectfactors/.

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Discovering Phage Casserole: Using Microbiology Techniques to Isolate an Arthrobacter Bacteriophage Mary Mersereau, Sai Sagireddy, Dr. Tamarah Adair, Ph.D. Department of Biology, Baylor University, Waco, TX

Abstract The applications of bacteriophages are endless. They are important to pharmaceutical companies, the food industry, and agriculture, and researchers have barely scratched the surface of potential uses. For this reason, discovering new bacteriophages is important. The Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science program (SEA-PHAGES) explores the bacteriophage population through observing evolutionary mechanisms and performing numerous protocols with the purpose of isolating and amplifying a bacteriophage. In this study at Baylor University, bacteriophage Casserole (host: Arthrobacter sp.) was isolated from sandy clay soil. Through multiple rounds of purification, Casserole proved to be a virulent phage that undergoes the lytic cycle. After amplification, a titer of 5.2 x 109 pfu/mL was obtained, and the concentration was measured to be 163.7 ng/ µl. Polymerase chain reaction, gel electrophoresis, and transmission electron microscopy revealed that Casserole is a part of the AV cluster and is Podoviridae in morphology. After sequencing, bacteriophage Casserole will be the sixth phage in the AV cluster to be recorded in the Actinobacteriophage Database. All the data captured within this experiment will be made available to the Actinobacteriophage Database for use in future research projects.

Introduction Bacteriophages, viruses that infect bacteria, are extremely abundant and diverse inhabitants of the microscopic world.1 Phages work to infect bacteria in order to reproduce. Interestingly, the vast majority of phages are host-specific because distinct species of bacteria thrive in different environments.2 Therefore, researchers frequently tailor their hunt for a phage by using a specific host. For example, a scientist working to find the bacteriophage T4, an E. coli infector, begins the search in the intestinal tract of mammals since E. coli is most likely found in this environment. Similarly, in this experiment, arthrobacterfriendly soil was searched for the presence of phage. Bacteriophage research promises exciting advancement in a range of different fields. For example, The World Journal of Microbiology and Biotechnology published an article describing phages as a possible solution to the antibiotic resistance crisis observed in both humans and agriculture.2 Their research highlights the diverse applications of phages; replacing antibiotics with efficient viruses could increase the success of medical treatments as well as food production. In another ground-breaking project, researchers documented the use of bacteriophages in treating various chronic orthopedic infections such as hip prostheses infected with gram-negative bacteria, tibial osteomyelitis due to Proteus, septic arthritis due to Enterobacter, and Staphylococcus aureus, etc.3 The Howard Hughes Medical Institute (HHMI), which hosts SEA-PHAGES, seeks to gain knowledge about the endless

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applications of these viruses. The goal of this global program is for students to learn important scientific methods and concepts vital to research while contributing to scientific understanding of the bacteriophage population.4 Students experimentally isolate, purify, and amplify a unique phage through microbiology techniques, electron microscopy, and DNA analysis–thereby discovering the characteristics and genomic sequence of a new phage. The hope is to expand the Actinobacteriophage Database, leading to increased knowledge of the characteristics, properties, and uses of phages within the scientific community. This experiment was performed at Baylor University during the Fall 2020 semester as part of the SEA-PHAGES program. The chosen host, Arthrobacter sp., is from the phylum Actinobacteria and it is from the American Type Culture Collection.4 It is a convenient host to work with because it grows at room temperature (22-28°C) and easily forms a thriving lawn in 1-2 days.4 The goal of this study was to add information to the Actinobacteriophage Database by isolating a new phage located in soil found on Baylor’s campus.

Materials and Methods The four major parts of this experiment are Isolation, Purification, Amplification, and Characterization, all of which were outlined in the Baylor University BIO 1405 SEA-PHAGES Lab Manual. Each category included several steps and protocols.

Host The host used in this experiment was the bacterial strain Arthrobacter sp. It is a gram-positive soil organism which easily breaks down hydrocarbons and one of over seventy species in the genus Arthrobacter. In this experiment, Arthrobacter sp. was grown on 0.015 L of peptone, 0.001 L of yeast, 40% dextrose, and 4.5 mM calcium chloride (CaCl2) media (PYCa media) at 22-28°C. Under these conditions, it takes 24-48 hours for a lawn of bacteria to grow. The bacteriophages’ interaction with the host bacteria gave insight on the morphology of the phage. Isolation: Soil Collection and Metadata A soil sample was collected from several inches below the ground surface (to avoid gravel and mulch) in an area in which Arthrobacter sp. seemed likely to be found. The date, time, group name, GPS coordinates of the location, description of the soil area and physical characteristics, depth, and weather at the time of collection were recorded (metadata). The soil’s percent sand, silt, and clay levels were measured and classified using a USDA soil texture calculator triangle. The pH of the soil extract was then recorded using deionized water and pH paper. Finally, the percent of the water was measured by subtracting the weight of the sample after drying out for 48 hours from the weight of the sample in its original state. Isolation: Soil Washing For soil washing, 15 mL of the soil sample (30% of tube) and 20 mL (40% of tube) of PY broth were transferred to a 50 mL conical tube. The tube was vigorously shaken by hand for 15 minutes to loosen and mix the contents. The supernatant was then centrifuged for two minutes at 1000 relative centrifugal force (rcf) to quicken the separation of components, and the lysate was vacuum filtered using 0.22 μm filter. Finally, 500 μL of the filtered supernatant was collected to create a direct sample, and the remainder of the lysate was enriched with the addition of Arthrobacter sp. Isolation: Plaque Assays Several plaque assays were conducted to test the filtered sample for phage. Plaque assays were performed with 0.5 mL Arthro hosts, which had been incubated with 10 µL of lysate, 1x PY Broth, and 1x PYCa top agar. The plaque assays were then incubated for 48 hours at 23°C and plaques were picked for purification purposes. Purification In this study, three rounds of purification were conducted. For each round, a single plaque was picked from a plate and placed in 100 µL of phage buffer. Using the new sample, serial dilutions were conducted and plated. The plaques were analyzed after 48 hours. Finally, results from the three rounds of purification were used to calculate the titer of the final lysate.

Because it is possible to obtain more than one type of phage within an environmental sample, the purification protocol confirmed that a sample contained a single clonal phage population. Amplification During this study, the lysate recovered from purification was insufficient to produce a webbed plate. This problem was addressed by picking 44 plaques from a single plate and adding them to 100 μL of phage buffer. This new lysate was used for a serial dilution and plaque assays. The procedure led to a webbed plate which then produced a high titer. Characterization: DNA Extraction In order to study the phage’s genetic material, its DNA was extracted from the high titer lysate. First, 4 µL of nuclease mix was added to two tubes containing 1 mL of the high titer lysate. This degraded nucleic acids into shorter polynucleotides. Both tubes were incubated for ten minutes in a heating block at 37 °C, and then 2 mL of DNA resin was added to each tube. Resin removed any contamination such as lipids, proteins, or salts. The contents of both tubes were filtered through two wizard mini-columns on a vacuum manifold block (“DNA Wizard Kit”). Each filter was washed with 6 mL of 80% isopropanol to remove the resin. Then, the filters were centrifuged at 8000 rcf for 5 minutes. Both columns were placed in the heat block for 1 minute, and 50 µL of heated quality water were transferred to each column. The columns were centrifuged again at 8000 rcf for 1 minute, and the centrifuge products from both tubes were combined in a fresh centrifuge tube containing isolated phage DNA. Finally, 50 µL of quality water were transferred to each filter, and both filters were centrifuged at 8000 rcf for 1 minute. The centrifuge products from this second round were combined in another fresh centrifuge tube. The isolated DNA sample was placed in a freezer. Characterization: Nanodrop and Polymerase Chain Reaction Following DNA Extraction, a NanoDrop was used to calculate the concentration of proteins and nucleic acids in 2 µL of the DNA. Next, Polymerase Chain Reaction was used to exponentially amplify the DNA in the sample. The materials required were as follows: extracted DNA (DNA template that is copied), the forward and reverse DNA primers which are required to replicate the Tape Measure protein gene sequence, Taq DNA polymerase (enzyme required for DNA replication), deoxyribonucleotide triphosphates (dNTPs) (help expand the growing DNA strand via binding with the help of Taq DNA Polymerase), and finally a reaction buffer. All of these components were added to a microcentrifuge tube and placed in a thermocycler. The thermocycler cycled through denaturation (unwinding of DNA double helix) at 94°C for thirty seconds, annealing (primers bind to complementary sequences) at 55.1°C for thirty seconds, and elongation (which occurred through the addition of nucleotides) at 72°C for forty-five seconds. This cycle repeated thirty-five times and the final elongation period lasted five minutes. Characterization: Gel Electrophoresis and Imaging The PCR products were analyzed through gel electrophoresis,

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Isolation confirmed the presence of phage. Purification ensured that the morphologies of the plaques on the plate were similar, confirming that only one phage was present. Amplification produced a large volume of high titer lysate. Finally, characterization revealed the isolated phage’s morphology and certain characteristics of its genome.

sand (4.25 mL) 9%

clay (3.00 mL) 38%

silt (0.75 mL) 53%

Soil Metadata for Sandy Clay Figure 1.1: The sandy clay soil composition is made up of 38% clay, 9% sand, and 53% silt.

Characterization: Electron Microscopy In order to magnify and photograph the bacteriophage, phage samples were stained with uranyl acetate and mounted for transmission electron microscopy (TEM). First, 100 μL of the high titer lysate were centrifuged for twenty minutes. Then, 80 μL of lysate were removed from the top and replaced with 80 μL of fresh phage buffer. This combination was slowly pipetted up and down to mix. An electron microscopy grid was placed in 20 μL of the lysate for 5 minutes, moved to 20 μL quality water for 2.5 minutes, transferred to a new 20 μL drop of quality water for 2.5 minutes, and finally placed in 20 μL of uranyl acetate for 1 minute. Uranyl acetate served as a negative light-absorbing stain that absorbed electrons. Without this, the fine details of a biological sample would not be seen under an electron microscope. Once the grid underwent this procedure, it was mounted for TEM, which clearly showed the phage’s morphology. A photograph was taken of the phage sample along with measurements of the capsid.

Purification The sample created from the enriched lysate was positive for phage. Following this initial positive, the three rounds of purification successfully isolated phage Casserole (see Fig. 2.1), which displayed two different uniform plaque sizes, the larger having an average diameter of 1.0 mm.

Archiving All of the phage data gathered in this study was archived in the Actinobacteriophage Database to allow any researcher within the bacteriophage community to access the results. The high titer lysate was also stored at Baylor University and the University of Pittsburgh. Archiving the phage data also ensured that the high titer lysate and extracted DNA can be stored for future use.

Amplification Calculations after the third round of purification indicated that 1680 µl of lysate would be required to flood a plate. After picking 44 plaques from a single plaque assay, the titer increased to a high titer of 5.2 x 109 pfu/mL, which was sufficient for DNA extraction. Using the NanoDrop, the extracted DNA sample’s concentration was determined to be 163.7 ng/µl.

Bacteriophage Casserole After Three Rounds of Purification Figure 2.1: A serial dilution was performed up to 10-6 using a plaque picked from Round 2.

Results Through the three steps (Isolation, Purification, and Amplification) a single clonal phage population, Phage Casserole, was identified and analyzed. The following results provide more explanation. Isolation The soil sample was collected by Dr. Tamarah Adair from a Bald Cypress tree near Waco Creek (Latitude: 31.548586, Longitude: -97.114371, Range: 260.051645 m, Altitude: 113.8694669 m). The soil was moist from rain and high humidity. The sample was later measured to be 37.5% clay, 9.4% silt, 53.1% sand, and 16.67% water (See Fig. 1.1). These percentages were used to determine that the soil was sandy clay. Additionally, the sample’s pH was measured to be 5.5.

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10 mm Absorbance

Original Research

which separated DNA fragments to visualize the genetic fingerprint of the phage. It distinguished macromolecules like DNA, RNA, and proteins. First, the gel was cast and poured into the electrophoresis apparatus. A fluorescent DNA-binding dye, ethidium bromide, was added to the gel prior to pouring. This allowed the visualization of the location and amount of DNA with the addition of ultraviolet (UV) light. During this experiment, gel electrophoresis was used to determine what primer group the phage belonged to, and then the individual clusters of that primer were tested. Once the DNA and primers ran on the gel for thirty minutes at 100 V, the gel was viewed under a UV light.

10 5 0 -5

225

250

275

300

325

350

Wavelength (nm)

Sample 2 Nanodrop dsDNA Concentration Figure 3.1: Sample 2 was measured to have a DNA concentration of 163.7 ng/µl. PCR and Gel Electrophoresis The PCR products were analyzed through gel electropho-

Gel Electrophoresis Figure 3.2: Wells are numbered 1-8. 1: Cluster AN, 2: Cluster AN Control, 3: Extracted DNA Sample, 4: 200 Molecular Weight Marker, 5: Cluster AV, 6: Cluster AV Control, 7: Cluster AP, 8: Cluster AP Control. TEM TEM showed that phage Casserole had a short tail–suggesting that it is a Podoviridae. It had an average capsid size of 0.540 µm.

TEM Image of Phage Casserole Fig. 3.3: The average width of phage Casserole’s capsid is 0.054 µm. Archiving Phage Casserole was archived at Baylor University and the University of Pittsburgh. A high titer lysate of 5.2 x 109 µl was obtained with plaques of two different sizes, the larger being approximately 1 mm. The average width of the bacteriophage Casserole was 0.540 µm. All the properties of the phage and experimental data can be found in the Actinobacteriophage Database.

Discussion: This experiment successfully achieved the goal of the

SEA-PHAGES program: to isolate a bacteriophage through isolation, purification, and amplification. Fully understanding bacteriophage Casserole will require DNA sequencing. However, significant information has been gained from observing Casserole through plaque assays, gel electrophoresis, and electron microscopy. Moreover, limitations of this study include the chosen host and media. The goal of HHMI is to increase the range of the Actinobacteriophage database. Using Arthrobacter sp. as the host and soil as the media limited the biodiversity of phages sent for sequencing. Soil metadata confirmed that Casserole was isolated from sandy clay. In a study performed by the Delaware Biotechnology Institute, sandy soil proved to be the second most likely soil type to contain phage.5 It is possible that sandy clay increased this study’s ability to find Casserole. The three rounds of purification revealed that Casserole’s plaques are consistently two different sizes, circular in shape, and clear. This indicates the presence of a virulent phage that uses the lytic cycle to reproduce. Finding a virulent phage is quite common because 80% of phages undergo the lytic cycle rather than the lysogenic cycle.6 Allan Campbell, who pioneered the understanding of phage Lambda, wrote that lytic phages have been useful to research because the lytic cycle allows scientists to view molecular processes occurring throughout an entire cell colony.7 As phage Casserole becomes more understood, it could be used to expand knowledge of phage interaction with the cellular world. During Amplification, 44 plaques were picked in an attempt to increase Casserole’s titer from a low titer to a high titer (greater than 109). This unusual procedure was performed because calculations after purification indicated that 1680 µl of high titer lysate would be required to web a plate, which is highly impractical. As an alternative, 44 plaques were picked and added to 100 µl of phage buffer, which successfully produced a high titer lysate. This innovative procedure in which “the web was brought to the plate” to increase phage stock was vital to the study of Casserole throughout all steps of characterization. After this high titer lysate was created, the NanoDrop measured Casserole’s DNA concentration, which was 207 ng/µl. This lower concentration suggested that the DNA sample was pure, and thus, suitable for sequencing. Although the NanoDrop was convenient, it was unable to distinguish between intact and degraded DNA. The NanoDrop verified that a sufficient amount of DNA was present in the sample. It measured the sum of the interested DNA fragment, other DNA, degraded DNA, RNA, degraded RNA, and nucleotides. Gel electrophoresis allowed further analysis of the DNA to determine in which genome cluster Casserole belonged. Another interesting issue also arose during characterization. When Casserole’s PCR product was first examined through gel electrophoresis, DNA bands appeared in areas which indicated two different primers. This suggested the possibility that two different samples of phage DNA had been isolated. For this reason, the individual clusters of primer 2 were separated and examined to determine if contamination was the cause or an issue with the thermocycler had occurred. Ultimately, electron microscopy demonstrated that cluster AV was the strongest cluster present in the sample, although cluster AP was also

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-resis, which showed results for PCR product 2. Then, the four clusters in PCR product 2 were separated (AV, AP, AN, and AU), and were run on a gel. While cluster AV showed a stronger band, results for cluster AP were also visible.

Original Research

visible on the gel. The evidence suggested that phage Casserole belonged to cluster AV because other bacteriophages in cluster AV match Casserole’s Podoviridae morphology. It also seemed unlikely that there could be multiple DNA samples within the lysate after three rounds of purification. More clarity will be obtained after Casserole’s genome is sequenced. There are a few alterations to the protocol which could produce improved results in the future. First, locating phage from the first soil sample collected was an unexpected result. In fact, it is not uncommon to collect 5-7 samples before successfully isolating a phage, or even fail to isolate a phage. A more efficient method is the multi-well plate. This protocol allows 24 soil samples to be purified and tested for phage all at once. However, it is time-consuming because properly filtering contaminants of all 24 samples requires 24 syringe filters. Also, the use of CaCl2 should be a requirement for plaque assays rather than a recommendation. Throughout the semester, no phages were successfully isolated without the use of CaCl2. The use of CaCl2 heightened the likeliness of locating a phage as it increased the likelihood of binding. Finally, increasing the rounds of purification could also improve confidence that a single clonal population has been isolated. During this experiment, gel electrophoresis suggested the possibility that DNA had been extracted from two separate clonal phage populations. If more than three rounds of purification had been performed, this issue would have been avoided.

Conclusion: Although improvements could benefit the protocol, this study successfully isolated a single clonal population of Arthrobacter phage from a soil sample collected on the campus of Baylor University. Phage discovery from soil samples is a simple way to study the biodiversity of bacteriophages. Gel electrophoresis confirmed that Casserole’s DNA is a part of the AV cluster, and TEM revealed that it is a podoviridae phage. After bacteriophage Casserole is analyzed through bioinformatics, even more knowledge about bacteriophages will be added to the Actinobacteriophage database.

Acknowledgements: We would like to thank Dr. Adair for all of her instruction and guidance. She is responsible for exposing us to the wonderful world of microbiology. Also, we would love to thank TAs Sriram Avirneni and Xueyan “Ryan” Wei for helping us carry out various parts of the experiment and offering constant advice.

References: 1. Sharma S, Chatterjee S, Datta S, Prasad R, Dubey D, Prasad RK, Vairale MG. Bacteriophages and Its Applications: An Overview. Folia Microbiol (Praha). 2017 Jan;62(1):17-55. doi: 10.1007/s12223-016-0471-x. Epub 2016 Oct 8. PMID: 27718043. 2. Jassim, Sabah A. A., and Richard G.fd Limoges. “Natural Solution to Antibiotic Resistance: Bacteriophages ‘The Living Drugs.’” World Journal of Microbiology and Biotechnology,

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vol. 30, no. 8, 2014, pp. 2153–2170., doi:10.1007/s11274-0141655-7. 3. Abedon, Stephen T., et al. “Phage Treatment of Human Infections.” Bacteriophage, vol. 1, no. 2, 2011, pp. 66–85., doi:10.4161/bact.1.2.15845. 4. Ali, Ilzat, et al. “SEA PHAGES: Howard Hughes Medical Institute.” SEA PHAGES Resource Guide 2018, Howard Hughes Medical Center, 2018, seaphages.org/institution/HHMI/. 5. Williamson, K. E., Radosevich, M., & Wommack, K. E. (2005). Abundance and Diversity of Viruses in Six Delaware Soils. Applied and Environmental Microbiology, 71(6), 3119–3125. https://doi.org/10.1128/aem71.6.3119-3125.2005. 6. Clokie, M. R., Millard, A. D., Letarov, A. V., & Heaphy, S. (2011). “Phages in nature.” Bacteriophage, 1(1), 31–45. https://doi.org/10.4161/bact.1.1.14942 7. Campbell, A. (2003). “The Future of Bacteriophage Biology.” Nature Reviews Genetics, 4(6), 471–477. https://doi. org/10.1038/nrg1089

Original Research

Abstract Fragile X syndrome (FXS) is the most common form of intellectual disability and is comorbid with other conditions such as Autism spectrum disorder. The purpose of this study was to provide further validation of the adult behavioral phenotypes of the Fmr1 mouse model on a C57BL/6 background strain in male and female mice to allow for further insight into the FXS behavioral phenotypes. We used the Fmr1 knockout to inspect the genotype- and sex-specific differences across multiple measures of repetitive activity, anxiety levels, depressive-like behavior, learning and memory deficits, and sensorimotor gating activity. Our results only displayed significant differences between genotypes in repetitive activity, anxiety levels, and sensorimotor gating activity. These findings demonstrate how Fmr1 mice on the C57BL/6 background strain can be valid in comparing phenotypic behaviors in Fragile X syndrome. Keywords: Fragile X, Fmr1, Autism

Introduction Fragile X syndrome (FXS) is the most common form of inherited intellectual disability and is often comorbid with conditions such as Autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and epilepsy (Hagerman & Harris, 2008; Servadio, Vanderschuren, & Trezza, 2015). Individuals with FXS often have deficits in short term memory, visual-spatial abilities, hyperactivity, intellectual disability, and also display an increase in anxiety, repetitive behaviors, aggression, and impulsivity (Devitt, Gallagher, & Reilly, 2015; Frankland et al., 2004; Spencer, Alekseynko, Serysheva, YuvaPaylor, & Paylor, 2005; Servadio, et al., 2015). FXS is caused by a CGG trinucleotide expansion in the 5’ untranslated region of the X – linked fragile X mental retardation (Fmr1) gene (Ding, Sethna, & Wang, 2014; Ellegood & Crawley, 2015; Servadio, et al., 2015). This expansion leads to hypermethylation of the promoter region which result in a lack of fragile X mental protein expression (FMRP) (Koukoui & Chaudhuir, 2007; Ellegood & Crawley, 2015; Servadio, et al., 2015). FMRP regulates protein expression via its interaction with diverse mRNA transcripts (Koukoui & Chaudhuir, 2007). In addition, FMRP assists in the localization, transport, stabilization, and translation regulation of mRNAs (Koukoui & Chaudhuir, 2007). One of the models used to observe FXS symptoms is the Fmr1 mouse model. Fmr1 knockout (KO) mice and individuals with FXS have behavioral, physical, and neurological similarities that increase the validity of the Fmr1 KO mouse model.

Individuals with FXS and Fmr1 KO mice display similar behavioral symptoms such as repetitive behavior and deficits in visual-spatial abilities (Kazdoba et al., 2014; Ding et al., 2014). FXS human male models display macroorchidism, or enlarged testes, that result from the lack of FMRP and may be due to the proliferative activity of Sartori cells (Kazdoba et al., 2014). This similar physical symptom is displayed in the Fmr1 KO mice, increasing the validity of the Fmr1 KO mice (Kazdoba et al., 2014). Deficits in the density and morphology of the dendritic spines are demonstrated in both humans with FXS and in the Fmr1 KO mice model. Deficits include an increase in the density of the dendritic spines and the tendency of the dendritic spines to be elongated and immature (Ding et al., 2014). The purpose of this paper is to further validate the behavioral symptoms of the Fmr1 mouse model. This present study utilizes Fmr1 KO and wildtype (WT) male mice as well as Fmr1 WT and heterozygous female mice on a C57BL/6 background strain. Due to inheritance patterns, females usually display milder symptoms due to their ability of X chromosome compensation (Kazdoba, Leach, Silverman, & Crawley, 2014). Females display similar symptoms as males, such as hyperactivity and deficits in learning and memory, but the symptoms are usually milder (Bartholomay, Lee, Bruno, Lightbody, & Reiss, 2019). We have included females since most studies have not included females, and we wish to increase the validity of our research to the human condition. In the present study, we examined the adult behavioral

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phenotypes of Fmr1 KO and WT male mice and Fmr1 WT and heterozygous female mice. The behavioral phenotypes were examined with a variety of tests to evaluate anxiety and activity levels, repetitive behavior, learning and memory, and sensorimotor abilities in Fmr1 KO and WT male mice and Fmr1 WT and heterozygous female mice. We aim to provide insight to the FXS phenotype that characterizes individuals who have the inherited intellectual disability and to expand the knowledge of FXS female behavioral phenotypes.

Methods Animals Subject mice consisted of Fmr1 knockout (KO) and wild type (WT) mice on a C57BL/6 background strain. A total of 74 mice were utilized in this study. The mice were bred and housed at Baylor University under standard laboratory conditions with a consistent temperature of 22ºC and a 12:12 hour light:dark diurnal cycle. Breeding pairs consisted of WT (+/+) male mice and Fmr1 heterozygous (+/-) female mice to produce Fmr1 KO (-/-) and WT males and Fmr1 heterozygous and WT females. Some breeding pairs consisted of male Fmr1 KO mice and female WT mice to produce male WT mice and Fmr1 heterozygous female mice. Food and water were provided for the mice ab libitum. Mice had their toes clipped for identification purposes on postnatal (PD) 7 and the toes were sent to Mouse Genotype for genotyping information (Escondido, CA, USA). On PD 21, all mice were weaned, and behavioral testing began on PD 90. To minimize the test order effects, behavioral tests were performed from least invasive to most invasive and are as follows: open field, elevated plus maze, marble bearing, tail suspension, delay fear conditioning, prepulse inhibition (Nolan et. al. 2017). Procedures were conducted in compliance with the Baylor University Institutional Animal Care and Use Committee and the National Institute of Health Guidelines for the Care and Use of Laboratory Animals. The layout of the behavioral testing performed across the five weeks of testing can be seen in Figure 1.

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Figure 1: Outline for timeline of behavioral tests performed. Open Field Open field was performed to evaluate activity levels as well as repetitive behavior and anxiety-like behavior. The testing environment consisted of an acrylic chamber (40 cm x 40 cm x 30 cm) in an isolated room that was controlled for background noise, light levels, and temperature. The mice were individually placed in the chamber and movement was measured with

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automatic optical animal detection software (Fusion by AccuScan Instruments, Inc., USA). The factors measured during the 30-minute testing period were total activity levels and clockwise revolutions, which is a measure of repetitive behavior. Anxiety was also quantified by analyzing the time and distance spent in the center of the chamber versus the outside of the chamber (where the inner was 50% of the area, 20 cm by 20 cm). After the testing period ended, mice were placed in separate cages until all the mice in the home cage were tested. Then all mice were returned back to their home cage with their littermates. The chamber was cleaned with 30% isopropanol alcohol between testing sessions. Elevated Plus Maze Elevated plus maze was performed to evaluate anxiety-like behavior and activity levels. The testing apparatus consisted of a center platform (5 cm x 5 cm) positioned 40 cm above the floor, with 4 arms (each 30 cm x 5 cm) and 2 of those arms were enclosed with acrylic walls that are each 15 cm tall. The testing apparatus was in an isolated room controlled for background noise, light levels, and temperature. Each testing session lasted 10 minutes and started after the experimenter left the room. The factors recorded were frequency of entries and duration of time spent in each of the arms. These factors were quantified by Ethovision XT video tracking software (Noldus, Netherlands). After the testing session ended, mice were placed in separate cages until all the mice in the home cage were tested. All the mice were returned back to their home cage with their littermates at the end of the testing session. The plus maze was cleaned with 30% isopropyl alcohol between testing sessions. Marble Burying The marble burying task was performed to evaluate repetitive behavior. The testing environment consisted of an Allentown mouse cage (27 cm x 16.5 cm x 12.5 cm) filled with approximately 2 inches of sanichip bedding. Twenty black 15 mm glass marbles were placed equidistant in a 4 x 5 array throughout the cage. The experimenter was not present in the room during the testing session. The testing session lasted 30 minutes in which each mouse was placed in a cage and allowed to freely bury the marbles. After 30 minutes of testing, mice were returned to their home cage with their littermates. The experimenter quantified how many marbles were buried at least to the 50%, 75%, 100% level, and those that were totally buried. One hundred percent buried was defined as the marble being buried but a small part of the marble remained seen at the surface of the bedding. Totally buried was defined as the marble being deep within the bedding and could not be seen by the experimenter. Tail Suspension The tail suspension test was performed to examine depressive-like behavior. The mice were suspended in an isolated area by fastening the distal end of their tails with a padded close pin to the edge of an elevated shelf in a room controlled for background noise, light levels, and temperature. An experimenter, who was blinded to the genotype, scored the total time that the mouse was mobile during a 6-minute time period. Immobility

Delay Fear Conditioning Delay fear conditioning was performed to examine learning and memory. The testing environment consisted of an operant conditioning chamber (26 cm x 22 cm x 18 cm) that included two acrylic sides and two metallic sides with a metal grid flooring to deliver a mild shock. The operant conditioning chamber was inside a sound-attenuating chamber. Throughout all trials, freezing behavior was measured with FreezeFrame 3 automated detection software (Coulbourn; Ohio). Freezing behavior is defined as an absence of any movement with the exceptions of breathing movements (Valentinuzzi et al., 1998). The two-day testing session consisted of 3 separate trials. On day 1, the 1st trial was conducted, in which the mice received two pairings of a 30 second 80 decibel (dB) white noise [conditioned stimulus (CS)]. Immediately after the CS, there was a 2 second 0.7 mA shock [unconditioned stimulus (US)]. Each tone-shock pairing was separated by 120 seconds, resulting in the 1st trial lasting 334 seconds. On day 2, the 2nd and 3rd trials were conducted. In the 2nd trial the testing environment remained the same and freezing was measured while the mice explored for 300 seconds. The testing environment was novel for the 3rd trial by changing the shape and flooring of the chamber, cleaning the chamber with 70% ethanol instead of 30% isopropanol in between testing sessions, placing vanilla-scented odor (Adam’s Extract) under the flooring, and changing the transfer cage bedding to shredded paper towels. Mice were placed in the altered testing environment for 360 seconds and the environment remained the same until the last 180 seconds in which the CS was presented continuously. During this time their freezing behavior was measured. After the testing session, the mice were placed in separate cages until all the mice in the home cage were tested in which then all the mice were returned to their home cage with their littermates. Prepulse Inhibition Prepulse inhibition (PPI) was performed to evaluate sensorimotor gating abilities. The testing environment consisted of an acrylic hollow constraint tube mounted on a sensor platform with the background noise level maintained at 68 dB. The sensor platform was used to measure and convert startle response amplitude via the SR – Lab System (San Diego Instruments, San Diego, CS, USA). The testing session consisted of 3 days: on day 1, mice habituated in the testing environment for 5 minutes and were then presented with 80 startle stimuli every 15 seconds. The startle stimulus consisted of a 120 dB noise that was 40 ms long that included a rise/fall time each less than 1 ms. On testing day 2, mice habituated in the testing environment for 5 minutes and were then presented with a 90-trial prepulse phase. These trials were randomized, and trials had 15 second spaced inter-trial intervals. The 1st three trial types were 20 ms weak prepulse stimuli with a rise/fall time less than 1 ms that were at intensities of 70, 75, and 80 dB. The 2nd three trial types were the same three prepulse stimuli paired with the original startle stimulus 100 ms after the prepulse stimuli. The startle threshold

of the mice was examined after a week of the PPI testing. Mice habituated in the testing environment for 5 minutes and were then presented with 99 trials consisting of 11 trial types in a pseudorandom order with each type presented once: no stimulus and 10 startle stimuli ranging from 75 – 120 dB at 5 dB intervals. The startle stimuli were 40 ms long with a rise/fall time less than 1 ms. Statistical Analysis Data were analyzed using GraphPad Prism 7 Software (San Diego, CA, USA) and SSPS 24.0 (IBM, USA). An independent t-test was utilized to evaluate results in open field, EPM, marble burying, tail suspension, the first part of day 2 in delay fear conditioning, and on day 2 of PPI. A repeated measures ANOVA was utilized to evaluate results of open field, day 1 and the second part of day 2 in delay fear conditioning, and PPI. Males and females were evaluated separately for each test. The level of significance for all comparisons was set at p < 0.05. Data are expressed as the mean ± standard error of the mean (SEM).

Results Open Field Open field was performed to evaluate anxiety and activity levels, as well as repetitive behavior. An independent t-test was used to analyze the results for repetitive behavior. Genotype-dependent differences in repetitive activity levels were observed in Fmr1 male KO and WT mice. There was a main effect of genotype on vertical episode count with Fmr1 male KO mice displaying increased rearing compared to the male WT mice, t(32) = 2.10, p < .05 (Fig. 2A). There was a main effect of genotype on clockwise revolutions with Fmr1 male KO mice displaying increased clockwise revolutions compared to male WT mice, t(32) = 2.11, p < .05 (Fig. 2B). Genotype-dependent differences in repetitive activity levels were not observed in Fmr1 female WT and heterozygous mice. There was not a main effect of genotype on vertical episode count in Fmr1 female WT and heterozygous mice, t(37) = 1.84, p = .073 (Fig. 2A). There was not a main effect of genotype on clockwise revolutions in Fmr1 female WT and heterozygous mice, t(37) = .28, p = .785 (Fig. 2B). A repeated measures ANOVA was used to analyze the results for anxiety levels, with a within-subjects factor of “location” for time and distance (containing two levels: center area and surrounding area of the apparatus). Genotype-dependent differences in the anxiety levels were observed in Fmr1 male KO and WT mice. There was a significant interaction between location and genotype for Fmr1 male mice, F(1, 32) = 9.68, p < .01 (Fig. 2C). We performed follow-up independent samples t-tests and found significant differences in time spent in the center (t(1,32) = 3.0, p < .0) and surround (t(1,32) = 3.2, p < 0.01) between male WT and KO mice. There was no main effect of distance on location in Fmr1 male mice (F(1, 32) = 2.1, p = .157) nor interaction between genotype in each location, F(1,32) = 0.27, p = .6 (Fig. 2D). Genotype-dependent differences in the anxiety levels were also observed in Fmr1 female WT and heterozygous mice. There was a significant interaction between genotype and locations for

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was then calculated by subtracting seconds mobile from total duration (360 seconds). Immobility was defined as the absence of hind limb movement, according to the protocol outlined in Can et al. (2012).

Marble Burying Marble burying was conducted to evaluate repetitive behavior, by measuring the number of marbles buried at the 50%, 75%, 100% levels, and those that were completely buried. An independent t-test was used to analyze the results of repetitive behavior. Genotype-dependent differences in repetitive behavior were not observed in male Fmr1 KO and WT mice. There was not a main effect of genotype for Fmr1 male KO and WT mice (t(33) = .10, p = .92) for marbles buried at the 50% level (Fig. 3A), the 75% level (t(33) = .34, p = .739) (Fig. 3B), the

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B. Males Females

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Elevated Plus Maze Elevated plus maze (EPM) evaluated anxiety levels through time spent in the arms of the maze. An independent t-test was used to evaluate the results of anxiety levels. Genotype-dependent differences in anxiety levels were observed in male Fmr1 KO and WT mice. There was a main effect of genotype for open arm duration with Fmr1 male KO mice displaying increased time in the open arms when compared to male WT mice, t(32) = 3.33, p < .01 (Fig. 2E). We also measured the number of head dips the mice performed on each arm. There was a main effect of genotype for the frequency of head dips with Fmr1 male KO mice displaying increased frequency of head dips, t(32) = 3.81, p < .001 (Fig. 2F). Genotype-dependent differences in anxiety levels were not observed in female Fmr1 WT and heterozygous mice. There was not a main effect of genotype for open arm duration with Fmr1 female WT and heterozygous mice, t(37) = .46 p = .649 (Fig. 2E). Furthermore, there was not a main effect of genotype for frequency of head dips with Fmr1 female WT and heterozygous mice, t(37) = .46, p = .650 (Fig. 2F).

Figure 2: Open field and EPM. Data are expressed as mean ± standard error of the mean (SEM), * p < .05, ** p < .01

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Tail Suspension The tail suspension test was performed to examine depressive-like behavior by examining the amount of time the mice were immobile. An independent t-test was used to analyze the results for depressive-like behavior. Genotype-dependent differences in depressive-like behavior were not observed in Fmr1 male KO and WT mice. There was not a main effect of genotypes for Fmr1 male KO and WT mice (t(32) = 1.75, p = .090) in total time immobility . The mean ± SEM values for the group were: WT male: 240.3 s ± 26.19; KO male: 229 s ± 31.17. Genotype-dependent differences in depressive-like behavior were not observed in Fmr1 female WT and heterozygous mice. There was not a main effect of genotype for Fmr1 female WT and heterozygous mice (t(29) = .90, p = .376) in the total time immobility. The mean ± SEM values for the group were (WT female: 242.8 s ± 28.42; Heterozygous female: 234.6 s ± 22.22).

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100% level (t(33) = .25, p = .805) (Fig. 3C), or at the level when marbles are completely buried (t(33) = .63, p = .534) (Fig. 3D). Genotype-dependent differences in repetitive behavior were not observed in Fmr1 female WT and heterozygous mice. There was not a main effect of genotype for Fmr1 female WT and heterozygous mice (t(37) = 1.01, p = .319) for marbles buried at the 50% level (Fig. 3A), at the 75% level (t(37) = .87, p = .391) (Fig. 3B), at the 100% level (t(37) = .38, p = .706) (Fig. 3C), or when the marbles were completely buried (t(37) = .55, p = .585) (Fig. 3D).

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Fmr1 female mice, F(1, 37) = 4.48, p < .05 (Fig. 2C). We performed follow-up independent samples t-tests and found significant differences in time spent in the center (t(1,37) = 2.05, p < 0.05) and surround (t(1,37) = 2.17, p < 0.05) between female WT and Heterozygous mice. There was no main effect of distance on location in Fmr1 female mice (F(1, 37) = 1.2, p = .27) nor interaction between genotype in each location F(1,37) = 0.54, p = .82 (Fig. 2D).

Figure 3: Marble bearing. Fmr1 mice of both sexes did not display significant results between genotypes in the amount the marble was buried; fifty percent (A), seventy-five percent (B), one hundred percent (C), and totally buried (D). Data are expressed as mean ± standard error of the mean (SEM). Delay Fear Conditioning Delay fear conditioning was performed to examine learning and memory abilities in mice. On day 1, the training phase, mice were presented with two pairings of a conditioned stimulus (CS) and unconditioned stimulus (US). A repeated measures ANOVA was used, with a within-subject factor of “time” (containing two levels: baseline and tone). Genotype-dependent differences in

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Prepulse Inhibition Prepulse inhibition (PPI) was performed to evaluate sensorimotor gating abilities in Fmr1 mice. On day 1 of testing, habituation to startle stimuli was tested. A repeated measures ANOVA, with a within-subjects factor of “time” (80 startle stimuli condensed into 8 time bins with 10 trials per bin) was used to analyze the results. Genotype-dependent differences were observed in habituation between Fmr1 male KO and WT mice. There was no significant interactions between time and genotype for Fmr1 male mice, F(7, 224) = 0.48, p = .852. However, there was a main between-subjects effects of genotype for habituation in Fmr1 KO males when compared to male WT mice, F(7, 224) = 5.71, p < .05, with Fmr1 male KO mice displaying decreased habituation (Fig. 5A). Genotypedependent differences were not observed in Fmr1 female WT and heterozygous mice. There was no significant interaction between time and genotype for Fmr1 female mice, F(7, 259) = 0.51, p = .827. There was not a main between-subjects effect of genotype for habituation between Fmr1 female WT and heterozygous mice, F(7, 259) = 0.16, p = .691 (Fig. 5B). On day 2 of testing, changes in total prepulse inhibition were observed. An independent t-test was used to analyze the results of inhibition. Genotype-dependent differences in inhibition were observed in Fmr1 male KO and WT mice. There was a main effect of genotype for levels of inhibition between Fmr1 male KO and WT mice (t(33) = 3.01, p < .01) with Fmr1 male KO mice displaying increased levels of inhibition (Fig. 5C). Genotype-dependent differences in inhibition were observed in Fmr1 female WT and heterozygous mice. There was a main effect of genotype for levels of inhibition between Fmr1 female WT and heterozygous mice (t(37) = 2.25, p < .05) with Fmr1 female heterozygous mice displaying increased levels of inhibition (Fig. 5C). One week following PPI testing, startle threshold was observed. A repeated measures ANOVA, with a within-subjects factor of “dB” levels (11 dB levels) was used to analyze the results. Genotype-dependent differences in startle responses were observed in Fmr1 male KO and WT mice. There was a significant interaction between decibel and genotype for Fmr1 male mice, F(10, 330) = 4.98, p < .001. There was a main between-subjects effect of genotype for startle responses with Fmr1 male KO displaying increased startle responses at lower stimulus levels compared to WT mice (dB levels: 75, 80, 85, 90, 95) (p < .001) (Fig. 5D). Genotype-dependent differences in startle responses were not observed in Fmr1 female WT and heterozygous mice. There was no significant interaction between decibel and genotype for Fmr1 female mice, F(10, 370) = 1.49, p = .143. There was not a main between-subjects effect of genotype for Fmr1 female mice across the various dB levels, p = .143 (Fig. 5E).

Tone

Figure 4: Delay fear conditioning. Fmr1 mice of both sexes did not display significant results between genotypes in the amount freezing for day 1 (A &B), part 1 of day 2 (C & D), and part 2 of day 2 (E). Data are expressed as mean ± standard error of the mean (SEM).

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freeing time were not observed in Fmr1 male WT and KO mice. There was no significant interaction between time and genotype for Fmr1 male mice, F(1, 33) = 2.04, p = .163. There was not a main between-subjects effect of genotypes for Fmr1 male mice for the amount of freezing time, F(1, 33) = 1.01, p = .321 (Fig. 4A). Genotype-dependent differences in freezing time were not observed in Fmr1 female WT and heterozygous mice. There was also no significant interaction between time and genotype for Fmr1 female mice, F(1, 37) = 1.11, p = .298. There was also no main between-subject effect of genotype for Fmr1 female WT and heterozygous mice, F(1, 37) = 1.51, p = .228 (Fig. 4B). In the first part of day 2, the contextual fear conditioning phase, mice were placed back into original context and freezing behavior was observed for 5 minutes. Genotype-differences were not observed in freezing behavior in Fmr1 male KO and WT mice. There was not a main effect of genotype for total freezing time between Fmr1 male KO and WT mice (t(33) = .04, p = .967) (Fig. 4C). Genotype-differences were not observed in freezing behavior in Fmr1 female WT and heterozygous mice. There was no difference between Fmr1 female WT and heterozygous mice for total time freezing (t(37) = .90, p = .375) (Fig. 4D). In the second part of day 2, cued recall phase, mice were placed in novel context and freezing behavior was observed. A repeated measures ANOVA with a within-subjects factor of “time” (containing two levels: baseline and tone) was used to analyze the results. Genotype-dependent differences were not observed in Fmr1 male KO and WT mice. There was no significant interaction between time and genotype for Fmr1 male mice, F(1, 33) = 0.04, p = .836. There was not a main betweensubjects effect of genotype for the freezing time between Fmr1 male WT and KO mice, F(1, 33) = 0.99, p = .327 (Fig. 4E). Genotype-dependent differences were not observed in Fmr1 female WT and heterozygous mice. There was not a significant interaction between time and genotype for Fmr1 female mice F(1, 37) = 0.00, p = .996. There was not a main between-subjects effect of genotype for freezing time between Fmr1 female WT and heterozygous mice, F(1, 37) = 0.08, p = .777 (Fig. 4E).

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Figure 5: Prepulse inhibition. Fmr1 male knockout (KO) mice displayed significantly reduced prepulse habituation compared to male wildtype (WT) mice (A). Female heterozygous and WT mice did not display any significant differences in enhanced prepulse habituation (B). Male KO displayed significantly more enhanced prepulse inhibition compared to male WT mice (C). Female heterozygous mice displayed significantly more enhanced prepulse inhibition compared to female WT mice (C). Male KO mice displayed higher startle responses at certain decibel (dB) levels (75, 80, 85, 90, 95) compared to male WT (D). Female heterozygous mice displayed a higher startle response at the dB level of 80 compared to WT mice (E). Data are expressed as mean ± standard error of the mean (SEM), * p < .05, ** p < .01

Discussion

Sensorimotor gating ability Individuals with FXS often display diminished sensorimotor gating abilities (Ding et al., 2014). Fmr1 male KO mice displayed an increase in sensorimotor getting abilities in the lower dB levels when compared to the male WT mice, as shown in the PPI test. Our findings of increased sensorimotor gating abilities in Fmr1 male KO mice were consistent with other studies using the same paradigm (Frankland et. al., 2004; Ding et al., 2014; Pietropaolo, Guilleminot, Martin, D’Amato, & Crusio, 2011). There were no significant differences between the Fmr1 female heterozygous mice and the female WT mice in sensorimotor gating abilities. One other study that used the same paradigm and background strain demonstrated that Fmr1 female heterozygous mice display an increase in sensorimotor gating abilities (Ding et. al. 2014). These differences in sensorimotor gating abilities between Fmr1 mice and the FXS behavioral phenotype could be due to the fact that sensorimotor gating differences may depend on genetic backgrounds (Ding et al., 2014; Pietropaolo et al., 2011).

Fragile X syndrome is the most common form of intellectual disability that is comorbid with other conditions such as Autism spectrum disorder and ADHD. The purpose of this study was to provide further validation of the adult behavioral phenotypes of Fmr1 mouse model on a C57BL/6 background strain to allow for further insight into the FXS behavioral phenotypes in individuals. The behavioral phenotypes included in this study are anxiety levels, sensorimotor gating abilities, depressive-like behavior, learning and memory deficits, and repetitive activity. Our results displayed lower anxiety levels in Fmr1 male KO and Fmr1 female heterozygous, higher sensorimotor gating abilities in Fmr1 male KO, and higher repetitive activity for Fmr1 male KO.

Depressive behavior Individuals with FXS often display an increase in depressive behavior (Bartholomay et al., 2019). There were no significant differences between the Fmr1 male KO mice and male WT mice in depressive behavior, as shown in the tail suspension test. Although other studies using the same paradigm and background strain demonstrated an increase in depressive activity for Fmr1 male KO mice (Spencer et. al., 2011). There were also no significant differences in depressive-like behavior between Fmr1 female heterozygous and female WT mice. Unfortunately, there is little research on Fmr1 female mice in depressive behavior. This shows that more research of female heterozygous is needed to provide consistent insight into the FXS behavioral phenotype in females.

Anxiety Levels One of the most common phenotypes observed in individuals with FXS is an alteration in anxiety levels (Bartholomay et al., 2019; Kazdoba et al., 2014). Fmr1 male KO mice displayed a significant decrease in anxiety levels when compared to the male WT mice, as shown in the open field test and EPM. Our findings of decreased anxiety levels in Fmr1 male KO mice for the open field was consistent with other studies using the same paradigm and background strain for Fmr1 (Spencer et al., 2005; Kazdoba et al., 2014). Fmr1 female heterozygous mice displayed a significant decrease in anxiety levels when compared to female

Learning and memory There were no significant differences between the Fmr1 male KO mice and male WT mice in deficits of learning and memory, as shown in the delay fear conditioning test. Other studies using the same paradigm demonstrated an increase in learning and memory deficits in Fmr1 KO mice, however a different background strain was used (Nolan et al., 2017). There were no significant differences between the Fmr1 female heterozygous mice and female WT mice in deficits of learning and memory. Although other studies using the same paradigm and background demonstrated an increase in learning and memory

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deficits in Fmr1 KO mice (Kazdoba et. al., 2014).

References Bartholomay, K. L., Lee, C. H., Bruno, J. L., Lightbody, A. A., & Reiss, A. L. (2019). Closing the gender gap in fragile X syndrome: review of females with fragile X syndrome and preliminary research findings. Brain Sciences, 9(1), 11. Ding, Q., Sethna, F., & Wang, H. (2014). Behavioral analysis of male and female Fmr1 knockout mice on C57BL/6 background. Brain Research Reviews, 271, 72 – 78. Devitt, N. M., Gallagher, L., & Reilly, R. B. (2015). Autism spectrum disorder (ASD) and fragile X syndrome (FXS): two overlapping disorders reviewed through electroencephalography-what can be interpreted from the available information? Brain Sciences, 5, 92-117. Ellegood, J., & Crawley, J. N. (2015). Behavioral and neuroana-

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Original Research

Repetitive Activity Individuals with FXS often display an increase in repetitive activity (Hagerman & Harris, 2008). Fmr1 male KO mice displayed an increase in repetitive activity when compared to male WT mice, as shown in open field and EPM. In the open field test, the males displayed an increase in rearing behavior and clockwise rotations, while in the EPM they displayed an increase in the frequency of head dips. Other studies using the same paradigm and background strain also demonstrated higher repetitive activity in Fmr1 KO mice (Kazdoba et. al., 2014; Servadio, et al., 2015). There were no significant differences between Fmr1 female heterozygous mice and female WT mice in repetitive activity. One of the novel aspects of our research is the inclusion of Fmr1 heterozygous females. The work in our study did find some subtle differences in Fmr1 heterozygous females compared to wild-type females. Even though the effects were small/ moderate in the females, they could provide insights into humans who do not have the full Fmr1 mutation. Another finding in our work is that some of the behavioral findings seem to be dependent on the genetic background of the Fmr1 mice or the specific type of behavior being observed (Kazdoba et al., 2014; Spencer et al., 2005). Like anxiety behavior, FXS phenotype displays a specific type of anxiety involving social and novel environments (Spencer et al., 2005). This could explain some of the insignificant findings of the Fmr1 male and female mice and their inconsistencies with the FXS phenotype. The purpose of this study was to provide further validation of the adult behavioral phenotypes of the Fmr1 mouse model on a C57BL/6 background strain to allow for further insight to the FXS behavioral phenotypes in individuals. This further validates the Fmr1 mouse model on the C57BL/6 background strain. There were some differences between the Fmr1 mouse model and the FXS phenotype. A better understanding of which mouse model most represents FXS behavioral phenotypes will hopefully lead us to have more knowledge about fragile X syndrome and its causes. Knowing about the causes will lead us to provide better services and treatment to those suffering from fragile X syndrome.

-tomical phenotypes in mouse models of austism. Neurotherapeutics 12, 521-533. Frankland, P.W., Wang, Y., Rosner, B., Shimizu, T., Balleine, B. W., Dykens, E. M., … Silva, A. J. (2004). Sensorimotor gating abnormalities in young males with fragile X syndrome and Fmr1- knockout mice. Molecular Psychiatry, 9, 417 – 425. Hagerman, R. J., & Harris S. W. (2008). Autism profiles of males with fragile X syndrome. American Journal of Mental Retardation, 113(6), 427- 438. Kazdoba, T. M., Leach, P. T., Silverman, J. L., & Crawley, J. N. (2014). Modeling fragile X syndrome in the Fmr1 knockout mice. Intractable & Rare Diseases Research, 3(4), 118 – 133. Koukoui, S. D., & Chaudhuri, A. (2007). Neuroanatomical, molecular genetics, and behavioral correlates of fragile X syndrome. Brain Research Reviews, 53, 27 – 38. Nolan, S. O., Reynolds, C. D., Smith, G. D., Holley, A. J., Escobar, B., Chandler, M. A., … Lugo, J. N. (2017). Deletion of Fmr1 results in sex-specific changes in behavior. Brain and Behavior, 1 – 13. Pietropaolo, S., Guilleminot, A., Martin, B., D’Amato, F. R., Crusio, W. E. (2011). Genetic background modulation of core and variable autistic-like symptoms in Fmr1 knock-out mice. PLoS ONE, 6(2). Servadio, M., Vanderschuren, L. J. M. J., & Trezza, V. (2015). Modeling autism-relevant behavioral phenotypes in rats and mice: do ‘autistic’ rodents exist? Behavioral Pharmacology 25, 522-540. Spencer, C.M., Alekseyenko O., Hamilton, S. M., Thomas, A. M., Serysheva, E., Yuva-Paylor L. A., & Paylor, R. (2011). Modifying behavioral phenotypes in Fmr1 KO mice: genetic background differences reveal autistic-like responses. Autism Res. 4(1), 40-56. Spencer, C. M., Alekseyenko, O., Serysheva, E., Yuva-Paylor, L. A., & Paylor, R. (2005). Alter anxiety-related and social behaviors in the Fmr1 knockout mouse model of fragile X syndrome. Genes, Brain, and Behavior, 4, 420 – 430. Valentinuzzi, V. S., Kolker, D. E., Vitaterna, M. H., Shimomura, K., Whiteley, A., Low-Zeddies, S., … Takahashi, J. S. (1998). Automated measurement of mouse freezing behavior and its use for quantitative trait locus analysis of contextual fear conditioning in (BALB/cJ x C57BL/6J) F2 mice. Learning and Memory, 5(4), 391-403.

Original Research

An Analysis of Physician Nutrition Through the Use of Laparoscopic Students Sowmya Duddu, Mahita, Maddukuri, Abhinav Mehta, Arvind Muruganantham, Meredith Ehlmann, Marty Harvill, Ph.D. Department of Biology, Baylor University, Waco, TX

Abstract Physician burnout and fatigue contribute to thousands of patient deaths a year and billions of dollars in damages for the healthcare industry. Thus, determining the optimal macronutrient for on-duty snacking is essential to alleviating the burnout burden and preventing medical errors during surgery. In this study, we determine the ideal macronutrient that should be consumed when subject to an irregular eating schedule by examining undergraduate premedical students (n=21) using laparoscopic surgery simulators. Subjects were asked to fast for 14 hours prior to the experiment and were randomly assigned to either a protein or carbohydrate treatment group. Students completed the laparoscopic pegboard exercises immediately after consumption. Pre- and post-experiment surveys were completed by subjects to assess whether certain lifestyle practices were correlated with experiment performance. Students performed significantly faster when subject to 14 hours of fasting followed by a macronutrient supplement shortly before the exercise, regardless of which macronutrient was consumed. Routine caffeine consumers had a larger increase in performance on the experiment day compared to non-caffeine consumers. Subjects that rarely consume protein bars performed significantly better on experiment day compared to those that regularly consume protein bars. Our study correlates several lifestyle and dietary choices with improved performance on a laparoscopic surgery simulator and clarifies the need for further research on optimal on-duty physician nutrition.

Introduction In the United States, medical errors rooted in physician fatigue--the inability for physicians to continue performing effectively (Patel et al., 2018)--contribute to the death of at least 44,000 patients a year (Kohn et al., 2000). Burnout is a continual stress reaction that can lead to emotional exhaustion, personal and job dissatisfaction, and depersonalization (patients are viewed as objects rather than human beings; AHRQ, 2017). As a result, patients of physicians with high exhaustion and depersonalization had significantly lower satisfaction scores than patients seeing physicians with low exhaustion and depersonalization (Anagnostopoulos et al., 2012). Additionally, the US Department of Health and Human Services predicts that there will be a shortage of 45,000 to 90,000 physicians by the year 2025, partly caused by physician burnout (Anagnostopoulos et al., 2012). The economic impacts of physician fatigue cost the US healthcare system at least $4.6 billion dollars annually. (Han et al., 2019). It is imperative to alleviate this by reducing physician fatigue. Poor workplace nutrition for physicians, often caused by lack of sufficient time to eat and lack of access to nutritious food, has been associated with fatigue symptoms such as reduced cognitive performance, physical ailments, and emotional symptoms (Lemaire et al., 2011). It can be argued

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that all performance in health care is cognitive, from decision making to consultations to performing surgery (Holden et al., 2010). Previous studies have suggested that snacking at regular intervals may enhance cognitive performance, even if regular meals are missed (Marriott et al., 1994). Minor modifications in the substance and frequency of physicians’ diets may have profound positive impacts on their performance and ability to function within occupational settings. Proteins, carbohydrates, and lipids are the major macromolecules used in energy metabolism and maintenance of bodily functions. Approximately 60 grams of carbohydrates can be ingested by the body in one hour when the source of carbohydrates contains a single sugar (Jeukendrup et al., 2014), making it faster than protein and lipid digestion. Carbohydrates are broken down in the small intestine and then absorbed into the bloodstream. These molecules are the main energy source for cells and necessary for the brain to produce energy and function. Similarly, proteins are broken down in the small intestine into amino acids, where their carbon backbones can act as an energy source. They improve memory, boost resistance to stress, and prevent hunger for longer periods of time, all of which could improve surgeon performance (Dossantos, 2016). These macromolecules were chosen to be compared in this

Materials and Methods Laparoscopic Surgery Simulator Laparoscopy boxes fitted with two disposable Maryland dissectors (grasping forceps used for Laparoscopic surgery), a webcam (Logitech HD C615), a standard Fundamentals of Laparoscopic Surgery (FLS) pegboard (FLS Products No. 50331), and subjects’ camera displays were used to gauge subject laparoscopic exercise efficacy after fasting and the administration of their respective macronutrients. Pegboards were set up such that all six pegs rested on one side of the board. Study participants’ laptops were connected to the provided webcams to provide an image inside the box. (Ritter et al., 2007).

ensuring further data reliability. Participants were informed that failure to inform the lab group would negatively impact their grade in the Laparoscopy class. Nutritional Sources The study participants were randomly divided into two groups of 11 and 10 laparoscopic students. Five minutes before the laparoscopic task, one group was given 60 grams of sugar and one cup of water, while the other group was given 15 grams of protein powder dissolved in one cup of water. Crystallized table sugar was chosen as the source of carbohydrates because it consists only of sucrose and therefore is a source of pure carbohydrates, containing no other macromolecules (Groves, 2018). The sugar was heated and cooled into a solid for easier consumption. Isopure Protein Powder was chosen as an ideal source of protein since this protein powder contains 25 grams of protein and 0 grams of carbohydrates and fat. It is estimated that the amount of protein that the body can absorb at one time in the form of whey protein concentrate is between 10 and 20 grams, and so participants were given 15 grams of the protein powder (Y. Boirie et al., 1997). The protein powder was dissolved in one cup of water before drinking, and the sugar group was also given one cup of water to consume in addition to the sugar to ensure that water consumption was not a confounding variable. The participants were required to consume the entire sample, as verified by researchers and assisting experimenters.

Figure 1: Schematic of the laparoscopic surgery simulator.

Test Subjects Test subjects consisted of 21 freshman year college students at Baylor University who resided in Hallie Earle Hall and were chosen as members of the selective Laparoscopy course after a rigorous application process. All participants had been practicing the pegboard exercise for months leading up to the experiment. Before the study, participants were administered a survey asking them to list any dietary restrictions (including allergies and diet types). As recommended by a nutritionist, all test subjects were asked to refrain from eating any food or liquids, besides water, for 14 hours before the starting time of the study (Weems, 2019). The test subjects were given five minutes to consume their protein or carbohydrates before they began the laparoscopic experiment to ensure that the expected increase in energy levels caused by the protein and carbohydrates would be within the time frame of the 30-35 minutes of data collection.

Period of Abstinence from Food All participants were asked to stop eating at 12:30 AM on the day of the study and continued to fast until 2:30 PM. Participants were provided with a list of rules to follow during the fast. They were told that they could not consume any food except celery and water, and this included caffeine, energy drinks, chewing gum, or mints. Test subjects were instructed not to take any naps during the 14-hour fasting period (Weems, 2019). A glucometer was not used in this study, but participants were told that their blood glucose levels would be tested to ensure that they would adhere to the rules of the fast, and they were given the opportunity to inform the lab group without penalty if they had been unable to follow the rules of the fast,

Pegboard Exercise The pegboard exercise used to gauge surgical efficacy was administered according to standard FLS guidelines. Subject exercise timing began as soon as the subjects touched a triangle on the pegboard and ended as soon as the last triangle was placed onto a peg. In the trial sequence of the exercise, subjects had to move six pegs one by one from their dominant hand side of the pegboard to their non-dominant hand side, and then back again. Each triangle had to be picked up with a Maryland with one hand, transferred in mid-air to a Maryland in the other hand, and then dropped on a peg on the opposite side of the board. Test subjects were familiar with this exercise and had practiced it regularly for months leading to the experiment.

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Original Research

study because both proteins and carbohydrates digest faster than lipids and can aid a physician’s performance. Understanding which snacks optimize workplace performance for physicians could be critical to reducing patient mortality and morbidity due to physician error. Our study compares the effects of protein versus carbohydrate composition of pre-operative snacks on cognitive and physical abilities.

Scoring Errors were defined as follows: if the test subject dropped a peg outside of the field of view and/or was not able to retrieve it, this was deemed an error and 10 seconds were added to their time according to the FLS guidelines. The total number of errors were both assessed individually and combined as part of the study participant’s overall score for that trial. The participant’s raw score for each trial was the amount of time it took them to complete the exercise, while the modified score consisted of the exercise completion time with 10 seconds added for each error that occurred during the trial.

between students fed carbohydrates and students fed protein before the experiment began, with a significance level of 0.05. The Student’s t-test was used because the average times of the two groups were being compared with each other. The students’ improvement times were used to see whether the carbohydrate or protein group performed better in order to account for the differences in skill and ability. Afterward, the survey data was used to see if any variable showed a correlation to the improvement of time using a regression model. Paired t-tests with a significance of 0.1 were then used to show significance between the survey variables and the improvement times. The software used for statistical analysis was R, with ggplot package utilized for data visualization.

Results The average difference between the control day times and the experimental times was shown to be statistically significant (P=0.01) and a 99% confidence interval of 1.23 to 8.14 showed that students performed better on experiment day. The difference between the average protein group improvement times with the average carbohydrate group improvement times, however, was not found to be statistically significant (P=0.7529). The average difference between the control day and experimental day times for each participant is shown in Figure 2.

Mean Difference Between Control and Experimental Times

Exercise time + errors (10) = modified score Survey Questions Participants were asked to complete a set of survey questions after both control and experimental trials were completed. These questions asked subjects about several lifestyle and dietary choices they regularly made. Statistical Analysis The average time for the nine trials on experiment day was calculated for each student. Following that, the average time for the first nine trials on control day was calculated for each student. The tenth trial from the control day was omitted because only nine trials were completed on experiment day. A Paired t-test was used to determine if there was a significant difference between the average control day and experimental day times with a significance level of 0.05. A Paired t-test was used because each student’s control day average time was being compared with their own experimental time instead of with other students’ times. The average experimental day time was subtracted from the average control day time for each student to get the improvement time. If the improvement time was negative, then that meant the student performed better on the control day. If the improvement time was positive, then the student performed better on experiment day. A Student’s t-test was used to determine if the improvement times were significantly different

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Difference in Times

Original Research

Trials Participants were asked to complete this pegboard exercise over the course of nine trial periods, with each trial period lasting four minutes and consisting of one performed exercise. Four minutes were given as the maximum completion time because the average control daytime was close to 2 minutes, giving subjects sufficient time to complete the exercise. The time between the completion of the exercise and the beginning of the next four minutes was recorded as the break time. Further, fixed time periods guaranteed that participants were at similar points in their digestion process at equal time points, allowing for easier comparison across groups. After each trial, participants were asked to rate their energy level on a scale of one to ten. The performance of each participant is assessed by time and errors during the exercise as a percentage of their previously recorded control times and errors to generate an exhaustion curve for each group.

Group Carbohydrate Protein

-10

Student

Figure 2: Bar graph depicting the difference between mean control time and mean experimental time for each student. Blue bars show students in the carbohydrate group. Red bars show

bars increased their performance on experiment day more than those who do. The improvement times for those who regularly consume protein bars and those who do not are shown in Figure 4.

Protein Bars vs. No Protein Bars Difference in Time

Caffeine vs. Non-Caffeine Difference in Times

Caffeine No

Yes

Improvement in Time (s)

Protein Bars No Yes

-10

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Yes

Figure 3: Violin plot depicting mean improvement in times from control to experiment day between participants who reported to regularly consume caffeine compared to those who reported to seldom consume caffeine. The difference in improvement times between both groups was statistically significant. Two-tailed Student’s t-test, P=0.07839. 90% confident that the interval 0.493 to 12.765 seconds captured the true mean difference, showing those who regularly consume caffeine performed better on experiment day than those who do not. The difference between the improvement times of caffeine drinkers and non-caffeine drinkers was statistically significant (P=0.0784). The interval 0.49296 to 12.7648 seconds captured the true mean difference with 90% confidence, showing that caffeine drinkers increased their performance on experiment day more than non-caffeine drinkers increased their performance. The improvement times for caffeine drinkers and non-caffeine drinkers are shown in Figure 3. The difference between the improvement times of those who consume a protein bar every day and those who don’t was statistically significant (P=0.0581). The interval 1.159 to 13.668 seconds captured the true mean difference with 90% confidence, showing those who don’t regularly consume protein

Caffeine

Yes

Figure 4: Violin plot depicting mean improvement in times from control to experiment day between participants who reported regularly consuming protein bars compared to those who reported to seldom consume protein bars. The difference in improvement times between both groups was statistically significant. Two-Tailed Student’s t-test, P=0.0581. 90% confident that the interval 1.159 to 13.668 seconds captured the true mean difference, showing those who rarely consume protein bars performed better on experiment day than those who regularly consume protein bars.

Discussion The primary purpose of this study was to study the effects of caloric deprivation and consumption of proteins or carbohydrates on surgical performance. We found no difference in average performance between the protein consumption and carbohydrate consumption groups. However, fifteen of twenty-one students performed significantly faster when subject to caloric deprivation and subsequent consumption of either protein or carbohydrate on the experiment day, suggesting that the prior consumption of protein or carbohydrates may have enhanced their laparoscopic performance. Our study also indicates that surgeons who regularly consume caffeine may be able to perform better in the operating room, even if they do not eat for several hours before. However,

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students in the protein group. The time difference in seconds shown on the y-axis and the number of students is shown on the x-axis-test. A paired t-test showed that the difference between the control and experimental times were statistically significant (P=0.0102) and a 95% confidence interval of 1.23 to 8.15 seconds confirmed that experimental times were lower.

Original Research

the results of our study only indicate that there is a correlation between regular caffeine consumption and improved performance on a surgical task. To determine causation, further study in the form of a controlled experiment is required. A study conducted by Penetar and McCann indicated that caffeine is effective in temporarily reversing the differences in focus and alertness that are caused by sleep deprivation (Penetar et al, 1994). A similar study could be designed in the future that explores the effects of caffeine on changes in performance due to altered food intake, and whether the trend that is displayed in sleep-deprived test subjects is also seen in nutritionally deprived participants. Similarly, the results of our study indicate that there is a positive correlation between regular consumption of protein products and improved performance during the surgical task even after a period of nutritional deprivation during which no protein or food of any kind had been consumed. Like the correlation between caffeine consumption and surgical performance, this data also suggests an area of possible future study. These findings were unexpected as nutritional deprivationinduced fatigue was anticipated to reduce the mental acuity and physical agility of subjects (Cutsem et al., 2017). A potential explanation for these findings is that the participants performed better on experiment day because they were able to consume either protein or carbohydrates five minutes before the trial, while on the control day, they did not receive any food right before exercise. This suggests that the transient increase in energy experienced subsequent to protein and carbohydrate consumption is necessary and sufficient to counteract the effects of previous caloric deprivation. Alternatively, since the data was collected four weeks after the control day, subjects practicing the pegboard exercise during that time may have contributed to a lower average time on experiment day. In order to validate our findings, a longitudinal study over several hours monitoring the surgical performance of subjects after nutritional deprivation and subsequent protein and carbohydrate consumption should be done. In this situation, it is possible that those who eat normally until the time of data collection will perform better on average over a longer span of time. If this experiment was performed, there could potentially be a significant difference in performance between the proteinconsumption group and the carbohydrate-consumption group, since metabolically, carbohydrates are catabolized before proteins (Elia et al., 1999). Additional measures such as a larger sample size, multiple trials of varying snack to task times, further monitoring of caloric intake during the control group, and separating groups by BMI are possible additions to future experiments which could increase accuracy of results. Additionally, a future experiment could include a third treatment group that receives both protein and carbohydrates before the task, and as a result, may perform significantly better than either of the other two groups that receive only one of the macromolecules. Previous work has shown that when test subjects were tested continuously both before and up to three hours after protein, carbohydrates, or fat was consumed, results showed that carbohydrate consumption improved short term memory and accurate performance in tasks requiring low metabolic activation, while protein consumption improved focus and efficiency in tasks involving high metabolic activation. (Fischer et al., 2001). The results of this study suggest that a

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combination of carbohydrates and protein is optimal for improved performance in the laparoscopic exercise, which requires both focus and accuracy in a low metabolic task. This could explain why the effects of protein or carbohydrates individually did not significantly differ from each other; both macromolecules may provide different benefits that would improve one’s performance in this exercise. The results of this study can be applied to the performance of surgeons who often endure long workdays and forego regular meals. Results from future larger-scale experiments that expand upon our study could be used to suggest nutrition policies for hospitals to implement to provide surgeons with nutrition before they go into the operating room.

References Anagnostopoulos, F., Liolios, E., Persefonis, G., Slater, J., Kafetsios, K., &amp; Niakas, D. (2012). Physician Burnout and Patient Satisfaction with Consultation in Primary Health Care Settings: Evidence of Relationships from a onewith-many Design. Journal of Clinical Psychology in Medical Settings, 19(4), 401-410. doi:10.1007/s10880-011-9278-8. Dossantos, N. (2016, November 10). 6 Health Benefits of Eating More Protein. Dye, L., Lluch, A., & Blundell, J. (2000, October 16). Macronutrients and mental performance. Elia, M., Stubbs, R., & Henry, C. (2012, September 06). Differences in Fat, Carbohydrate, and Protein Metabolism between Lean and Obese Subjects Undergoing Total Starvation. Fischer, K., Kolombani, P., Langhans, W., & Wenk, C. (2001, March). Cognitive performance and its relationship with postprandial metabolic changes after ingestion of different macronutrients in the morning. Groves, M. (2018, June 08). Sucrose vs Glucose vs Fructose: What’s the Difference? Han, S., Shanafelt, T. D., Sinsky, C. A., Awad, K. M., Dyrbye, L. N., Fiscus, L. C., . . . Goh, J. (2019, June 4). Estimating the Attributable Cost of Physician Burnout in the United States. Holden, R. J. (2010). Cognitive performance-altering effects of electronic medical records: An application of the human factors paradigm for patient safety. Cognition, Technology &amp; Work, 13(1), 11-29. doi:10.1007/s10111-010-0141-8 Institute of Medicine (US) Committee on Military Nutrition Research. (1994, January 01). Carbohydrates, Protein, and Performance. Jeukendrup, A. (2014). A Step Towards Personalized Sports Nutrition: Carbohydrate Intake During Exercise. Sports Medicine, 44(S1), 25-33. doi:10.1007/s40279-014- 0148-z Kanarek, R. B., & Swinney, D. (1990). Effects of food snacks on cognitive performance in male college students. Appetite, 14(1), 15-27. doi:10.1016/0195-6663(90)90051-9 Kohn, L., Corrigan, J., & Donaldson, M. (2000). To err is human: Building a safer health system. Washington: National Academy Press. Lemaire, J. B., Wallace, J. E., Dinsmore, K., & Roberts, D. (2011). Food for thought: An exploratory study of how physicians experience poor workplace nutrition. Nutrition Journal,

Original Research

10(1). doi:10.1186/1475-2891-10-18 Marriott, B. M. (1994). 17 Carbohydrates, Protein,, and Performance. In Food Components to Enhance Performance: An Evaluation of Potential Performance- Enhancing Food Components for Operational Rations. Place of publication not identified: National Academies Press. O’Callaghan, F., Muurlink, O., & Reid, N. (2018, December 7). Effects of caffeine on sleep quality and daytime functioning. Oben, J., Kothari, S. C., & Anderson, M. L. (2008). An open label study to determine the effects of an oral proteolytic enzyme system on whey protein concentrate metabolism in healthy males. Journal of the International Society of Sports Nutrition, 5(1), 10. doi:10.1186/1550-2783-5-10 Patel, R., Bachu, R., Adikey, A., Malik, M., & Shah, M. (2018, October 25). Factors Related to Physician Burnout and Its Consequences: A Review. Penetar, D. M., McCann, U., Thorne, D., & Institute of Medicine (US) Committee on Military Nutrition Research. (1994, January 01). Effects of Caffeine on Cognitive Performance, Mood, and Alertness in Sleep-Deprived Humans. Physician Burnout. (2017, July). Retrieved December 21, 2020, from https://www.ahrq.gov/prevention/clinician/ahrqworks/burnout/index.html Ritter, E. M., & Scott, D. J. (2007, June 14). Design of a proficiencybased skills training curriculum for the fundamentals of laparoscopic surgery. Retrieved from https://pubmed.ncbi. nlm.nih.gov/17558016/ Weems, S. (2019, November). [Personal interview]. Y. Boirie, M., M. Dangin, Y., Layman, D., TL. Halton, F., JA. Rufian-Henares, C., S. Sindayikengera, X., . . . W. Kriengsinyos, M. (1997, January 01). An open label study to determine the effects of an oral proteolytic enzyme system on whey protein concentrate metabolism in healthy males. (US) Committee on Military Nutrition Research. (1994, January 01). Effects of Caffeine on Cognitive Performance, Mood, and Alertness in Sleep-Deprived Humans. Physician Burnout. (2017, July). Retrieved December 21, 2020, from https://www.ahrq.gov/prevention/clinician/ahrqworks/burnout/index.html Ritter, E. M., & Scott, D. J. (2007, June 14). Design of a proficiencybased skills training curriculum for the fundamentals of laparoscopic surgery. Retrieved from https://pubmed.ncbi. nlm.nih.gov/17558016/ Weems, S. (2019, November). [Personal interview]. Y. Boirie, M., M. Dangin, Y., Layman, D., TL. Halton, F., JA. Rufian-Henares, C., S. Sindayikengera, X., . . . W. Kriengsinyos, M. (1997, January 01). An open label study to determine the effects of an oral proteolytic enzyme system on whey protein concentrate metabolism in healthy males.

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Incompleteness and Not Just Right Experiences: A New Perspective on OCD Nicole Wire

Review Article

Department of Psychology and Neuroscience, Baylor University, Waco, TX

Abstract Obsessive Compulsive Disorder (OCD) is a debilitating mental health disorder that afflicts an estimated 2.3% of the world’s population. Characterized by the presence of obsessions and compulsions, it is an extremely heterogeneous disorder that can be elicited by multiple factors. Traditionally, OCD has been thought to be driven by harm avoidance (HA) and compulsions aim to alleviate the subsequent anxiety. However, a review of the research in this area shows growing evidence for the role of Not Just Right Experiences (NJREs) and Incompleteness (INC) in giving rise to certain OCD symptom dimensions such as ordering and checking. As such, this review seeks to bring to light the newfound role of NJREs and INC in specific OCD symptom dimensions, their possible use as an explanation for the high comorbidity between OCD and Obsessive-Compulsive Personality Disorder (OCPD), and the efficacy of focusing on INC and NJREs during treatment. The review also highlights considerations for future research concerning the use of representative ethnic and clinical samples to determine the roles of NJREs and INC in OCD cross-culturally.

Introduction Obsessive Compulsive Disorder (OCD) has an intriguing history dating back to Shakespearean times when obsessive washing was associated with guilt and in the 19th century when OCD was related to feelings of melancholy or depression (Fornaro et al., 2009). Today, OCD is characterized by the presence of obsessions (recurrent and unwanted thoughts that cause anxiety or distress) and compulsions (repetitive behaviors or mental acts aimed to reduce feelings associated with obsessions) (APA, 2013). According to Abramowitz et al. (2010), OCD can be characterized by four widely accepted and supported symptom dimensions: (a) contamination/washing, (b) harm obsessions/checking compulsions, (c) symmetry/ ordering, and (d) unacceptable thoughts/mental compulsive rituals or neutralizing strategies. OCD has a lifetime prevalence of 2.3% (Ruscio et al., 2010), however, this number may be underestimated due to the secrecy and misdiagnosis associated with the disorder. One reason the stigma towards mental health disorders has been perpetuated throughout society is due to the strain it puts on the economy. According to the human capital approach, economic strain from health-related issues is measured by 2 factors, direct and indirect costs. Direct costs are those associated with the diagnosis and treatment of the disease or mental health disorder, and indirect costs result from factors such as mortality, disability, care seeking, work absence and early retirement. Mental health disorders differ from other diseases such as cancer in that the indirect costs incurred are often greater than the direct costs. In 2010, the global indirect cost of mental health disorders totaled

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US$1.7 trillion, whereas direct costs only amounted to US$0.8 trillion (Trautmann et al., 2010). The preservation of this stigma is largely due to media outlets commonly adopting misconceptions about the various mental health disorders. Rüsch et al. (2005) demonstrated that the media often portrayed people with mental illnesses as rebellious, irresponsible, dependent on others, and dangerous due to their manic behaviors. As a result, people suffering from OCD are less likely to seek or continue treatment if they perceived negative reactions or labeling from people around them (Leaf et al., 1986) and experience employment discrimination (Hazer & Bedell, 2000). Concerning misdiagnosis, Glazier et al. (2015) found that 50.5% of physicians misidentified OCD vignettes and subsequently prescribed antipsychotic medication instead of the recommended cognitive-behavioral therapy (CBT) or selective serotonin reuptake inhibitors (SSRIs). This may be confounded by the fact that OCD shares similarities with other mental disorders such as anxiety, mood, and tic disorders. Therefore, this warrants exploring factors that can distinguish these disorders to allow for accurate diagnoses and appropriate treatment recommendations. Traditionally, OCD is considered to be motivated by harm avoidance (HA). Feared consequences (obsessions) drive harm avoidance either through physically avoiding feared situations or performing rituals (compulsions) to alleviate anxiety associated with them (Rachman, 1997). Recently, however, it has been noticed that a significant number of OCD patients do not identify with a feared consequence, and thus were less

High INC

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highest success. This review seeks to consolidate the significant volume of research on the role of INC in various OCD symptom dimensions, explore the connections between OCD and OCPD, discuss the efficacy of focusing on INC and NJREs during treatment, and address certain oversights in current research. Also, it seeks to bring to light considerations for future research for more representative and conclusive results to be determined. With increasing acceptance towards talking about mental health struggles in the media, it is important to understand the complexity of OCD so that effective treatments for the various symptom dimensions can be put in place to treat the diverse population living with the disorder. The growing support for the associations that INC and NJREs have with various dimensions and mental disorders opens up new avenues for clinicians, the media, and individuals to understand OCD.

Comparing the Roles of INC and HA in OCD Symptom Dimensions Traditionally, anxiety has been thought to play a leading role in motivating obsessive-compulsive symptoms by promoting HA, hence its classification under anxiety disorders in the DSMIII (APA, 1987) and DSM-IV (APA, 2000). Years of research on HA have produced similar results concerning its link with obsession or obsessional thought (Ecker & Gönner, 2008; Pietrefesa & Coles 2008, 2009), a personal sense of responsibility or overestimation of threat (RH), and importance of thoughts and need to control them (IT) (Bragdon & Coles, 2017). HA has also been associated with various OC symptoms such as washing and checking (Bragdon & Coles, 2017). However, recent research has discovered that HA does not fully explain all symptom dimensions, and levels of HA among individuals with OCD and various anxiety disorders do not differ significantly (Belloch et al., 2016; Coles & Ravid, 2016). Therefore, it is important to discover a more discriminatory factor to draw a

High HA

HighMotivation Motivation High Group Group

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INC INCGroup Group

Low Low Low INC INC INC

HA Group

Low Low HA HA

Low Low INC INC

Low Motivation Group

Figure 1: Motivation Model of OCD. (a) High motivation group. (b) HA group. (c) INC group. (d) Low motivation group.

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responsive to traditional CBT (Foa et al., 1999). In 2013, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) removed OCD from the anxiety disorder category to reflect this new insight, signaling a shift in the consensus about the motivator of OCD. As such, this warrants investigation into alternative factors that can contribute to OCD. Many theories have been suggested, and one such proposal is that not just right experiences (NJREs) and the related feelings of incompleteness (INC) contribute to the development of OCD. INC was mentioned as early as 1903 by Pierre Janet and reiterated in 1992 by Rasmussen and Eisen and refers to “an inner sense of imperfection, connected with the perception that actions or intentions have been incompletely achieved” (Summerfeldt, 2004, p. 1156). Similarly, NJREs are situations where people feel tense or distressed from something not being how it should be (Ravid et al., 2020). It is important to note that INC and NJREs are experienced by the general public (Ghisi et al., 2010), however, OCD patients experience NJREs that are more disturbing, difficult to suppress, and involve greater urges to perform a compulsion (Fornés-Romero & Belloch, 2017). Also, INC has produced unique associations with various symptoms of OCD including ordering and symmetry (Bragdon & Coles, 2017; Ecker & Gönner 2008; Pietrefesa & Coles, 2008; Sibrava et al., 2016) and even washing (Fornés-Romero & Belloch, 2017) which was traditionally an HA-driven symptom. These new associations between INC and specific obsessions and compulsions have pointed to possible relationships with Obsessive Compulsive Personality Disorder (OCPD) and tic disorders (Peterson et al., 2001). As a result of this complex relationship, recent studies underscore the usefulness of dimensions to dissect this heterogeneous disorder into more manageable parts (Summerfeldt et al., 2014). However, it is important to note that various obsessions and compulsions can co-occur (Abramowitz et al., 2010) and can be motivated by multiple factors. Therefore, more research needs to be conducted to identify more symptom dimensions to better understand them and provide targeted treatments to ensure the

Review Article

separation between OCD and anxiety disorders. One such factor could be INC as Ecker et al. (2014a) found that OCD participants had higher INC scores than anxiety, depressive, and non-clinical controls. The surge of research on the role of INC in OCD in just the last few decades has already shown promising results. Studies have found a stronger correlation between INC and symmetry/ ordering symptom dimension in OCD than that with HA (Bragdon & Coles, 2017; Ecker & Gönner, 2008; Fornés-Romero & Belloch, 2017; Pietrefesa & Coles, 2008). Bragdon & Coles (2017) found elevated levels of frequency and distress related to ordering compulsions in the context of INC compared to HA. A reason for this, as proposed by Summerfeldt et al. (2015), may be the result of emotional dysregulation where ordering compulsions are an attempt to reduce discomfort associated with a difficulty in addressing complex external stimuli. Also, an interesting relationship between levels of INC and OCD severity was discovered by Ecker & Gönner (2008) where symptom severity on 2 scales of OCD symptoms (i.e., Y-BOCS and VOCI) emerged as unique predictors of INC; this has been supported by various other studies (Belloch et al., 2016; Pietrefesa & Coles, 2008; Taylor et al., 2014). As such, this can point to an important application of INC in diagnosing OCD. On top of this, research has even found evidence of INC eliciting traditionally HA driven dimensions such as checking (Coles et al., 2003; Pietrefesa & Coles, 2008) and ordering even after controlling for worry, anxiety, depression, and HA (Ecker et al., 2014b). As a result, Bragdon & Coles 2017 proposed the Motivation Model (Figure 1) which is based on the relative levels of INC and HA rather than the content of the symptoms. They identified 4 subgroups: (a) High motivation group (high HA and high INC), (b) HA group (high HA and low INC), (c) INC group (low HA and high INC), and (d) Low motivation group (low HA and low INC). Results from between-group comparisons showed unique characteristics of each group to various obsessive beliefs and widely used symptom dimensions of OCD. For example, the INC group endorsed more ordering symptoms than the HA group and the Low motivation group scored lower on all OCI subscales (i.e., checking, doubting, ordering, obsessing, hoarding, and neutralizing) except washing. As such the Motivation Model may be more useful in categorizing OCD symptoms than the commonly used 4 dimensions in the DOCS which only takes into account the content of symptoms. As such, this shows the importance of including INC in the various models of OCD and underscores the heterogeneity of the disorder by proposing that multiple motivators can contribute to a single symptom dimension. Future models should build on the Motivational Model by including other motivators such as disgust, which has been implicated in contamination related OCD symptoms. This can lead to a more comprehensive understanding of the disorder and the development of more targeted treatments for the various subtypes of OCD instead of focusing solely on feared consequences.

Link between INC, OCPD, and Perfectionism 60 | Scientia 2021

Obsessive-Compulsive Personality Disorder (OCPD) is defined as a "chronic maladaptive pattern of excessive perfectionism, preoccupation with orderliness and detail, and need for control over one's environment that leads to significant distress or impairment, particularly in areas of interpersonal functioning." (Pinto et al., 2017, p. 102–103). Eight personality traits have been associated with OCPD: perfectionism, overattention to detail, excessive devotion to work, inability to discard worn or useless items, hypermorality, inability to delegate tasks, rigidity, and miserliness (APA, 2000). The media has often confused the perfectionistic traits of the lesser-known psychiatric disorder, OCPD, with the more widely recognized OCD. However, differences between the disorders have been found, mainly that OCD and not OCPD was associated with obsessions and people with OCPD were better able to delay rewards (Pinto et al., 2014). As a result of the lack of mental health literacy, it has led to a misunderstanding and subsequent trivialization of OCD with people posting on social media about their OCD tendencies when referring to neatly organized spaces. This stereotype has gone so far that members of the public value it and even see it as something to strive for (Stewart et al., 2019), disregarding the major impairments this trait has on daily life. Stewart et al. (2019) demonstrated this misunderstanding when asking participants to define 'obsessivecompulsive'. The most common answer described "repetitive, uncontrolled behaviors, compulsions" (18.7%) and the second most common answer described "perfectionism; demanding that everything be just so; must be in control" (12.1%) (Stewart et al., 2019). Also, they found that perfectionism was the third most common description of OCD. Despite this, the public's confusion may not be unfounded. Pinto et al. (2011) found that 34.7% of OCD patients in a sample met the diagnostic criteria for OCPD according to the DSM-IV and OCPD diagnosis, more severe OCPD, and specifically perfectionism predicted worse outcomes for exposure and ritual prevention (ERP) therapies. Therefore, it is important to study this association to find out possible motivators behind it. Recent studies have found that INC and NJREs may be a plausible factor for the high comorbidity rates of OCPD in OCD patients. Perfectionism is a trait of OCPD of particular interest concerning OCD. Perfectionism is "a personality disposition characterized by exceedingly high standards for performance accompanied by tendencies for overly critical self-evaluations of one's behavior" (Stoeber & Janssen, 2011, p. 477). Tolin et al. (2006) found that OCD patients were more likely to endorse beliefs about perfectionism compared to anxiety controls. The motivator behind this link was later demonstrated by Pietrefesa & Coles (2008) who showed that INC rather than HA had a significant association to perfectionism and specific domains such as concern over mistakes, doubt about actions, personal standards, and organization. Ecker et al. (2014b) built on this relationship by demonstrating a significant positive association between Obsessive Compulsive Personality Traits (OCPTs), which include perfectionism, and INC-driven OCD dimensions (i.e., ordering and checking) even after controlling for worry, depression, anxiety, and HA. Sibrava et al. (2016) also found that OCD patients with higher levels of INC were more likely to meet the diagnosing criteria for OCPD, have an obsession

Impact of targeting NJREs/INC during treatment Lagging mental health literacy coupled with the stigma towards mental disorders has led to the destructive delay in

(a) Exposure and Ritual Prevention (ERP) therapy model

seeking treatment in OCD patients, with as many as 11 years between diagnosable symptom onset and receiving treatment (Pinto et al., 2006). This is extremely important because a delay in treatment for more than 2 years leads to less than desirable outcomes (Dell'Osso et al., 2010). Receiving treatment promptly is not the only concern but receiving the appropriate treatment is also paramount. Because OCD has traditionally been characterized by HA and anxiety related to feared consequences, many treatments have been designed to specifically target this aspect of the disorder. Two types of CBT, ERP and cognitive therapy (Figure 2) have shown promising results in reducing the effects of OCD. ERP exposes patients to their feared outcome and prevents them from performing compulsions to reduce the anxiety associated with the feared outcome. Cognitive therapies focus on reevaluating and changing interpretations of intrusive thoughts such as an abnormally high likelihood of a harmful outcome occurring and personal responsibility for preventing such outcomes. By altering these maladaptive thoughts, patients can reduce the anxiety associated with their feared outcome and thus reduce the urge to perform compulsions (Coles & Ravid, 2016). While results have shown that ERP and cognitive therapies can reduce OCD symptoms, Foa et al. (1999) found that patients who did not identify with a feared consequence experienced poorer outcomes than those who did. This may be due to the roles of other motivators such as INC and NJREs in OCD as they do not involve any feared consequences. As a result of the growing knowledge about the role of INC and NJREs in OCD, multiple studies have suggested the incorporation of this knowledge into current treatments to

(b) Cognitive Therapy (CT) model

Without CT

Exposure to feared outcome (intrusive thoughts)

Reappraisal of intrusive thoughts

Intrusive Thoughts

Anxiety

Reduced Anxiety

Anxiety

Reduced urge for compulsion

Compulsion

ERP

Prevent Compulsion

Decreased anxiety to feared outcome and extinction of conditioned response

Without ERP

Compulsion

Relief from anxiety and reinforcement of compulsion

Decreased anxiety to feared outcome and extinction of conditioned response

Relief from anxiety and reinforcement of compulsion

Figure 2: Traditional treatment strategies for OCD. (a) Exposure and Ritual Prevention (ERP) therapy model in context to typical OCD conditioning. (b) Cognitive Therapy (CT) model in context to typical OCD conditioning.

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with details, and experience symmetry/exactness obsessions and ordering/arranging compulsions. Bragdon & Coles (2017) also found that in their Motivation Model, the INC group had stronger associations with perfectionism and intolerance to uncertainty (PC) and importance and over-control of thoughts (IT) compared to a personal sense of responsibility or overestimation of threat (RH). As a result of the growing evidence supporting the relationship between INC, OCD symptom dimensions, perfectionism, and OCPD, Ecker et al. (2014b) proposed that NJREs, which are characterized by INC, may be a common affective motivational factor linking OCD and OCPD. As such, the link between perfectionism, INC, and OCD may explain poorer treatment outcomes. However, empirical studies need to be conducted to confirm this relationship. Sibrava et al. (2016) also highlighted that while INC showed promising associations, it still did not fully explain a cause-and-effect relationship between the presence of INC and diagnosis of OCPD, thus, pointing to additional factors in the relationship. With more research investigating the role of INC and other possible factors associated with the link between OCD and OCPD, more comprehensive diagnoses and targeted treatments can be developed for these disorders.

Review Article

make them more targeted to these specific motivators. Bragdon & Coles (2017) suggested that instead of targeting anxiety from feared outcomes, habituation could focus on the sensations and emotional states (i.e., discomfort) experienced during NJREs. Summerfeldt (2004) also proposed the role of sensory-affective dysfunction and suggested that targeting the appraisal of the emotions evoked by NJREs may be more useful than changing interpretations of intrusive thoughts. Coles & Ravid (2016) tested the efficacy of targeting INC and NJREs in treatment by explicitly addressing the role of NJREs in patients' OCD during ERP. They found that after implementing this adapted treatment, OCD patients did not identify as many NJREs and remaining NJREs were less severe. Also, changes in NJREs correlated to overall changes in OC symptoms and the obsession domain. While this study showed promising results, future studies should focus on a few key aspects. Because changes in the number and severity of NJREs did not significantly correlate to changes in ordering/ arranging symptoms during this study, future research should investigate this peculiar lack of association as previous studies have supported the link between INC/NJREs and the symptom dimension of ordering. Future research should also aim to compare the effects of traditional CBT and NJRE-targeted OCD to determine if there are any significant differences in treatment outcomes among people with INC-driven OCD. The role of perfectionism in OCD treatment outcomes is another important consideration. Pinto et al. (2011) found that perfectionism seemed to be a major predictor of worse outcomes with regards to psychotherapy and pharmaceutical treatments (Cavendini et al., 1997). However, there have been mixed results about the efficacy of reducing OCD symptoms via targeting perfectionistic beliefs during ERP and cognitive therapy, therefore, future research should investigate if specific rather than general perfectionistic beliefs are interfering with treatment. In light of the promising associations between INC/NJREs and OCD and their role in treatment, it is important not to forget to target HA in treating the disorder as well. Bragdon & Coles (2017) found that over half of the participants in their sample experienced relatively high levels of both HA and INC, which may be associated with increased severity of the disorder, however, further research is needed to confirm this. As such, all the possible motivators behind symptoms in a specific individual should be taken into account during treatment to provide a tailored and effective treatment plan for them. Leaving out or misdiagnosing a motivator could potentially be detrimental as time and money are lost on therapy and more importantly, symptoms go untreated. If future research continues to support the efficacy of targeting INC/NJREs and perfectionism during treatment in patients who are unable to identify a feared outcome, this modified therapy should be translated into a clinical setting to benefit the entire OCD community.

Lack of Representation in OCD Research While previous research has provided promising results for the role of NJREs and INC in OCD, one of the major issues in OCD research has been obtaining a representative sample for both non-clinical and clinical participants. With NJREs and

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INC only gaining popularity in OCD research in recent years, it will be essential for future research to include representative samples to determine whether factors such as ethnicity, religious affiliations, and comorbid disorders affect how people interact with these motivators. First, concerning the lack of representation in non-clinical samples, a glaring number of studies fail to accurately represent the ethnic minorities in their samples. In reviewing 21 clinical studies on OCD, Williams et al. (2010) found that the studies included 91.5% European Americans, 1.6% Asian American, 1.3% African American, 1.0% Hispanic American, 1.5% Other, and 3.1% Unknown. Studies have found that the prevalence rates of OCD among ethnic groups did not differ largely (Breslau et al., 2006; Subramaniam et al., 2012) however, including an accurate representation of ethnic populations is particularly important in this kind of research as different life circumstances can affect the interaction a person has with their OCD. One study found that African Americans and Asian Americans reported more contamination related symptoms (Wheaton et al., 2013). Also, Stewart et al. (2019) found that those who were less educated and who belonged to minority groups (i.e., African American, Hispanic or Latino, Asian, Pacific Islander, Native American or Alaskan Native) were less likely to know what obsessivecompulsive meant and less likely to have heard about OCD. This, together with other possible reasons like distrust of larger mental health systems, cultural beliefs (Williams et al., 2010), and fear of stigmatization towards seeking treatment (Williams et al., 2012) could be possible factors in explaining the varying impacts of OCD on minority groups. Multiple studies that have found that these reasons have contributed to ethnic minorities being less likely to seek treatment, let alone receive appropriate treatments for OCD. Himle et al. (2008) found that only 20% of African Americans and Caribbean Americans with OCD in the study received specialty mental health services and only 20% of the sample used serotonin reuptake inhibitors (SRIs) in the past year. As a result of the lack of representation in research, Williams & Turkheimer (2007) proposed that differences in attitudes between racial groups may lead to under- or overdiagnosis in minorities as they showed that when differences in attitudes towards cleanliness were controlled for, previously identified racial differences failed to remain significant. Future research should aim to include more representative samples to determine if there are differences in symptomology, severity, and functional impairment between non-minority and minority groups in the country. As such, with the growing support for NJREs and INC, it is important to include minorities in future research to determine if these motivators demonstrate similar effects cross-culturally. Subsequently, communication of these findings to both minorities will be paramount in getting rid of stereotypes of mental health and allowing them to be more aware of symptoms to seek treatment. This can also lead to better diagnoses and treatments to be recommended to minorities with OCD by clinicians. Concerning clinical samples, a handful of studies tend to include only a smaller number of OCD patients compared to non-clinical samples. With OCD being a heterogeneous

disorder, it is important to capture a larger sample of OCD patients to fully represent all symptom dimensions and comorbid disorders to help understand the interactions of these factors with symptom severity and response to treatment. This has been demonstrated with perfectionism as it has been thought to hinder the effectiveness of ERP treatments (Blatt et al., 1998). Therefore, future research should explore how different dimensions and comorbid disorders could affect traditional treatment outcomes. With this, future work should aim to tailor treatments to accommodate individual circumstances rather than just the primary disorder.

Conclusion

Acknowledgements I would like to thank Dr. Rizalia Klausmeyer for her advice and comments when drafting this manuscript.

Abramowitz, J. S., Deacon, B. J., Olatunji, B. O., Wheaton, M. G., Berman, N. C., Losardo, D., Timpano, K. R., McGrath, P. B., Riemann, B. C., Adams, T., Björgvinsson, T., Storch, E. A., & Hale, L. R. (2010). Assessment of obsessive-compulsive symptom dimensions: Development and evaluation of the Dimensional Obsessive-Compulsive Scale. Psychological Assessment, 22(1), 180–198. https://doi.org/10.1037/ a0018260 American Psychiatric Association. (1987). DSM 3. American Psychiatric Association. American Psychiatric Association. (2000). DSM 4. American Psychiatric Association. American Psychiatric Association. (2013). DSM 5. American Psychiatric Association. Belloch, A., Fornés, G., Carrasco, A., López-Solá, C., Alonso, P., & Menchón, J. M. (2016). Incompleteness and not just right experiences in the explanation of Obsessive–Compulsive Disorder. Psychiatry Research, 236(2016), 1–8. https://doi. org/10.1016/j.psychres.2016.01.012 Blatt, S. J., Zuroff, D. C., Bondi, C. M., Sanislow, C. A. I., & Pilkonis, P. A. (1998). When and how perfectionism impedes the brief treatment of depression: Further analyses of the National Institute of Mental Health Treatment of Depression Collaborative Research Program. Journal of Consulting and Clinical Psychology, 66(2), 423–428. https:// doi.org/10.1037/0022-006X.66.2.423 Bragdon, L. B., & Coles, M. E. (2017). Examining heterogeneity of Obsessive-Compulsive Disorder: Evidence for subgroups based on motivations. Journal of Anxiety Disorders, 45(2017), 64–71. https://doi.org/10.1016/j.janxdis.2016.12.002 Breslau, J., Aguilar-Gaxiola, S., Kendler, K. S., Su, M., Williams, D., & Kessler, R. C. (2006). Specifying race-ethnic difference in risk for psychiatric disorder in a USA national sample. Psychological Medicine, 36(1), 57-68. https://doi.org/10.1017/ S0033291705006161 Cavedini, P., Erzegovesi, S., Ronchi, P., & Bellodi, L. (1997). Predictive value of Obsessive–Compulsive Personality Disorder in antiobsessional pharmacological treatment. European Neuropsychopharmacology, 7(1), 45–49. https:// doi.org/10.1016/S0924-977X(96)00382-3 Coles, M. E., Frost, R. O., Heimberg, R. G., & Rhéaume, J. (2003). “Not just right experiences”: Perfectionism, obsessive–compulsive features and general psychopathology. Behaviour Research and Therapy, 41(6), 681–700. https://doi. org/10.1016/S0005-7967(02)00044-X Coles, M. E., & Ravid, A. (2016). Clinical presentation of notjust right experiences (NJREs) in individuals with OCD: Characteristics and response to treatment. Behaviour Research and Therapy, 87(2016), 182–187. https://doi. org/10.1016/j.brat.2016.09.013 Dell’Osso, B., Buoli, M., Hollander, E., & Altamura, A. C. (2010). Duration of untreated illness as a predictor of treatment response and remission in obsessive-compulsive disorder. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry,

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There is increasing support for the validity of INC as a motivator in OCD in addition to the widely supported motivator of HA. This was supported by the links discovered between INC and specific symptom dimensions (i.e., symmetry/ ordering, checking, washing), higher comorbidity rates with OCPD, perfectionism, OCD severity, and increased efficacy of treatment. With this new information, the heterogeneity of the disease can begin to appear somewhat less daunting as now, more informed decisions can be made when it comes to diagnosing patients and recommending treatment plans to them. Despite the promising evidence supporting INC, several concerns surrounding the associations that INC has with other variables should be addressed. Future research should continue to investigate if the links between INC and traditionally HAdriven symptoms (i.e., checking and washing) are supported. If they are, then this may call for a greater emphasis on the Motivation Model over current content-based dimensions. Even though INC has increased the understanding of the various symptom dimensions and links to OCPD, it still does not fully account for all the associations. To fill these gaps, links to other motivators such as disgust should be explored to determine if they can explain the lack of or weak associations with INC and HA. Also, the efficacy of targeting NJREs during treatment should be further investigated as there is yet to be a study directly comparing the difference between traditional therapy and modified therapies. Future research should also strive to include more representative samples to either support or disregard any differences that have been observed between minority and non-minority groups. This will help to determine if cultural differences contribute to differences among ethnic groups or if these differences are a product of racial bias. This is not only important to better understand how OCD affects these groups, but it can also lead to better outcomes for those living with the disorder. It will help to increase mental health literacy, quell stereotypes associated with mental disorders, reduce the fear of stigmatization, and increase access to treatment for this often underrepresented group in society.

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Academy of Child & Adolescent Psychiatry, 40(6), 685–695. https://doi.org/10.1097/00004583-200106000-00014 Pietrefesa, A. S., & Coles, M. E. (2008). Moving beyond an exclusive focus on harm avoidance in Obsessive Compulsive Disorder: Considering the role of incompleteness. Behavior Therapy, 39(3), 224–231. https://doi.org/10.1016/j. beth.2007.08.004 Pietrefesa, A. S., & Coles, M. E. (2009). Moving beyond an exclusive focus on harm avoidance in Obsessive-Compulsive Disorder: Behavioral validation for the separability of harm avoidance and incompleteness. Behavior Therapy, 40(3), 251–259. https://doi.org/10.1016/j.beth.2008.06.003 Pinto, A., Dargani, N., Wheaton, M. G., Cervoni, C., Rees, C. S., & Egan, S. J. (2017). Perfectionism in Obsessive-Compulsive Disorder and related disorders: What should treating clinicians know? Journal of Obsessive-Compulsive and Related Disorders, 12(2017), 102–108. https://doi.org/10.1016/j. jocrd.2017.01.001 Pinto, A., Liebowitz, M. R., Foa, E. B., & Simpson, H. B. (2011). Obsessive Compulsive Personality Disorder as a predictor of exposure and ritual prevention outcome for Obsessive Compulsive Disorder. Behaviour Research and Therapy, 49(8), 453–458. https://doi.org/10.1016/j.brat.2011.04.004 Pinto, A., Mancebo, M. C., Eisen, J. L., Pagano, M. E., & Rasmussen, S. A. (2006). The Brown Longitudinal Obsessive Compulsive Study: Clinical features and symptoms of the sample at intake. The Journal of Clinical Psychiatry, 67(5), 703–711. Pinto, A., Steinglass, J. E., Greene, A. L., Weber, E. U., & Simpson, H. B. (2014). Capacity to delay reward differentiates Obsessive-Compulsive Disorder and Obsessive-Compulsive Personality Disorder. Biological Psychiatry, 75(8), 653–659. https://doi.org/10.1016/j.biopsych.2013.09.007 Rachman, S. (1997). A cognitive theory of obsessions. Behaviour Research and Therapy, 35(9), 793–802. https://doi. org/10.1016/S0005-7967(97)00040-5 Ravid, A., Collins, L., & Coles, M. E. (2020). “Not just right experiences” in children and adolescents: Phenomenology and relation to OCD symptoms. Journal of ObsessiveCompulsive and Related Disorders, 24(2020). 1–6. https://doi. org/10.1016/j.jocrd.2019.100501 Rüsch, N., Angermeyer, M. C., & Corrigan, P. W. (2005). Mental illness stigma: Concepts, consequences, and initiatives to reduce stigma. European Psychiatry, 20(8), 529–539. https:// doi.org/10.1016/j.eurpsy.2005.04.004 Ruscio, A. M., Stein, D. J., Chiu, W. T., & Kessler, R. C. (2010). The Epidemiology of Obsessive-Compulsive Disorder in the National Comorbidity Survey Replication. Molecular Psychiatry, 15(1), 53–63. https://doi.org/10.1038/mp.2008.94 Sibrava, N. J., Boisseau, C. L., Eisen, J. L., Mancebo, M. C., & Rasmussen, S. A. (2016). An empirical investigation of incompleteness in a large clinical sample of Obsessive Compulsive Disorder. Journal of Anxiety Disorders, 42(2016), 45–51. https://doi.org/10.1016/j.janxdis.2016.05.005 Stewart, E., Grunthal, B., Collins, L., & Coles, M. (2019). Public recognition and perceptions of Obsessive Compulsive Disorder. Community Mental Health Journal; New York, 55(1), 74–82. http://dx.doi.org/10.1007/s10597-018-0323-z

Review Article

Stoeber, J., & Janssen, DirkP. (2011). Perfectionism and coping with daily failures: Positive reframing helps achieve satisfaction at the end of the day. Anxiety, Stress & Coping, 24(5), 477–497. https://doi.org/10.1080/10615806.2011.56297 Subramaniam, M., Abdin, E., Vaingankar, J., & Chong, S. (2012). Obsessive-Compulsive Disorder: Prevalence, correlates, help-seeking and quality of life in a multiracial Asian population. Social Psychiatry & Psychiatric Epidemiology, 47(12), 2035–2043. https://doi.org/10.1007/s00127-0120507-8 Summerfeldt, L. J. (2004). Understanding and treating incompleteness in obsessive-compulsive disorder. Journal of Clinical Psychology, 60(11), 1155–1168. https://doi. org/10.1002/jclp.20080 Summerfeldt, L. J., Gilbert, S. J., & Reynolds, M. (2015). Incompleteness, aesthetic sensitivity, and the obsessivecompulsive need for symmetry. Journal of Behavior Therapy and Experimental Psychiatry, 49(2015), 141–149. https://doi. org/10.1016/j.jbtep.2015.03.006 Summerfeldt, L. J., Kloosterman, P. H., Antony, M. M., & Swinson, R. P. (2014). Examining an obsessive-compulsive core dimensions model: Structural validity of harm avoidance and incompleteness. Journal of Obsessive-Compulsive and Related Disorders, 3(2), 83–94. https://doi.org/10.1016/j. jocrd.2014.01.003 Taylor, S., McKay, D., Crowe, K. B., Abramowitz, J. S., Conelea, C. A., Calamari, J. E., & Sica, C. (2014). The sense of incompleteness as a motivator of obsessive-compulsive symptoms: An empirical analysis of concepts and correlates. Behavior Therapy, 45(2), 254–262. https://doi.org/10.1016/j. beth.2013.11.004 Tolin, D. F., Worhunsky, P., & Maltby, N. (2006). Are “obsessive” beliefs specific to OCD?: A comparison across anxiety disorders. Behaviour Research and Therapy, 44(4), 469–480. https://doi.org/10.1016/j.brat.2005.03.007 Trautmann, S., Rehm, J., & Wittchen, H. (2016). The economic costs of mental disorders. EMBO Reports, 17(9), 1245–1249. https://doi.org/10.15252/embr.201642951 Wheaton, MichaelG., Berman, NoahC., Fabricant, LauraE., & Abramowitz, JonathanS. (2013). Differences in obsessive– compulsive symptoms and obsessive beliefs: A comparison between African Americans, Asian Americans, Latino Americans, and European Americans. Cognitive Behaviour Therapy, 42(1), 9–20. https://doi.org/10.1080/16506073.201 2.701663 Williams, M., Powers, M., Yun, Y.-G., & Foa, E. (2010). Minority participation in randomized controlled trials for obsessive– compulsive disorder. Journal of Anxiety Disorders, 24(2), 171–177. https://doi.org/10.1016/j.janxdis.2009.11.004 Williams, Monnica T., & Turkheimer, E. (2007). Identification and Explanation of Racial Differences on Contamination Measures. Behaviour Research and Therapy, 45(12), 3041– 3050. https://doi.org/10.1016/j.brat.2007.08.013 Williams, M.T., Domanico, J., Marques, L., Leblanc, N. J., & Turkheimer, E. (2012). Barriers to treatment among African Americans with Obsessive-Compulsive Disorder. Journal of Anxiety Disorders, 26(4), 555–563. https://doi.org/10.1016/j. janxdis.2012.02.009

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The Effects of Photosynthesis and Respiration on Carbon Processing in Small Ponds Lacey Barnes, Robert Doyle, Ph.D. Department of Biology, Baylor University, Waco, TX

Original Abstracts

Small ponds (typically < 10 ha in surface area) are abundant and important in carbon dynamics in the landscape. These bodies of water have been historically neglected by limnologists, yet there is an increasing amount of evidence that shows the importance of small ponds in the carbon cycle. We measured the relative magnitude of plankton photosynthesis to respiration which allowed us to determine if a pond is a net source or sink of CO2 to the atmosphere. The objective of this study was to measure and utilize the photosynthesis to respiration ratio (P:R) to determine if three different small farm ponds were carbon sinks or carbon sources. The sampling occurred twice at the USDA-ARS Riesel Watershed field station in Waco, Texas between the months of September (summer) and February (winter). We measured plankton photosynthesis and respiration by laboratory incubations under varying light levels to determine key photosynthetic parameters (Pmax, alpha, respiration). These parameters were used to estimate in situ rates of P and R for each pond over three days in each of the two sampling periods. The plankton community of one pond (Old Lake) was a CO2 sink in both summer and winter, while a second pond (New Lake) was a CO2 source in both summer and winter. The plankton of the third pond (Bennie) was a CO2 source in the summer and a CO2 sink in the winter. This displays the notion that small ponds play a factor in carbon dynamics. Future research will be conducted to better understand the role of farm ponds in the global carbon cycle.

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Biology

Isolation and Bioinformatic Analysis of Arthrobacter Phage Pippa Jonathan Aparicio, Bronson Balzac, Sinchana Basoor, Ritu Channagiri, Luke Day, Sowmya Duddu, Anthony Giovi, Haider Khan, Jonathan Lai, Arvind Muruganantham, Angel Otto, Jorge Ramirez, Catherine Ravikumar, Christian Schultz, Saisha Singh, Avery Voight, Nicole Wire, Emily Young, Sriram Avirneni, Grip Gilbert, Leo Rule, Aadil Sheikh, Tamarah L. Adair, Ph.D. Bacteriophages, despite their abundance in the biosphere, are vastly uncharacterized and potentially hold information critical to solving modern biological problems. Arthrobacter phages are viruses capable of infecting Arthrobacter, a commonly found soil Actinobacteria. The purpose of this study was to characterize the morphology and genome of Arthrobacter phage Pippa, isolated from a soil sample at Baylor University. Following isolation using a soil lysate enriched with Arthrobacter sp. ATCC 21022, a series of plaque assays were employed to obtain a high titer lysate. Using transmission electron microscopy, Pippa was classified as a Myoviridae phage. The phage cluster was determined to be AO by tape measure polymerase chain reaction. Isolated viral DNA was submitted to the Pittsburgh Bacteriophage Institute for Illumina Next-Gen Sequencing. The genomic sequence was auto-annotated using DNA Master and determined to have 50,783 base pairs, 78 open reading frames (ORFs), and a GC content of 61.1%. Further manual annotation was performed to check each auto-annotated ORF and resulted in the deletion of a single ORF. Auto-annotated start codons were each evaluated using a suite of bioinformatic software. Additional in silico analysis utilizing BLAST, HHPred, and Phamerator, was performed to predict functions. Out of 77 ORFs, 34 had supporting evidence for a known function. Further bioinformatic research into Pippa’s genome will provide more insight into the structure and function of its proteins, phage gene regulation, Myoviridae infection, and the co-evolutionary relationship between phages and their hosts.

Bloodmeal analysis of wild-caught Anopheles stephensi in Ethiopia Joseph Spear, Sae Hee Choi, Solomon Yared, Meshesha Balkew, Peter Mumba, Dereje Dengela, Gedeon Yohannes, Dejene Getachew, Sheleme Chibsa,Matthew Murphy, Kristen George, Cecilia Flately, Karen Lopez, Daniel Janies, Seth R. Irish, Tamar E. Carter, Ph.D.

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URSA Award Winners

Malaria is an ongoing health crisis in many developing countries throughout the world including Ethiopia. Determining the vector composition and characterizing their behaviors is crucial towards limiting the spread of malaria parasites. Anopheles stephensi, a vector normally found in the Middle East and South Asia, was first detected in Ethiopia in 2016 and is now confirmed to be broadly distributed in the country. Here we analyzed the bloodmeals of wild-caught An. stephensi in Ethiopia using a molecular approach to provide preliminary insight to potential An. stephensi feeding sources. DNA extractions were performed on mosquitoes collected in CDC light traps and pyrethrum spray catches (PSC) in 10 sites in eastern Ethiopia. To determine the species of the bloodmeal source, PCR and sequencing were employed targeting the cytochrome b mitochondrial gene (cytB) known to differentiate between vertebrate species. Of the 85 wild-caught An. stephensi, 39 were determined to be bloodfed based on universal primer amplification. Of those, 16 were selected for preliminary cytB sequencing. BLAST analysis of generated sequences successfully identified bloodmeal source species in all 16; 11 fed on caprine, three fed on bovine, one fed on human, and one fed on canine hosts. These results provide preliminary evidence that An. stephensi can feed on multiple vertebrate species in Ethiopia, including humans and common livestock in Ethiopia. With the evidence of multiple bloodmeal hosts species detected, to determine An. stephensi relative feeding preferences, a sample size of at least 500 wild-caught blood fed mosquitos should be targeted in future surveys.

Chemistry & Biochemistry

An Application of DFT for Characterizing the Energetics of HDX for Solvated Glucose Meg E. McCutcheon, Emvia I. Calixte, Emily D. Ziperman, Jamie H. Kim , Elyssia S. Gallagher, Ph.D. Glycans are significant in disease due to their relevancy in cell-cell communication. However, branch complexity and lack of functional group diversity complicates structural elucidation. Hydrogen/Deuterium exchange (HDX) labels hydroxyls on carbohydrates and we hypothesize that in-electrospray (ESI) HDX can be utilized to differentiate isomers. We introduce D2O to the solvated sugar as ESI droplets evaporate and metal adducts form. Here, we employ Density Functional Theory (DFT) with the B3LYP method and 6-311G++ basis set to explore the energetics of this process. Systems were constructed using β-glucose surrounded by one solvation shell with varying percentages of D2O (10%-100%) and water and one sodium ion. Preliminary work has shown that when exchange occurs in systems containing D2O in the first solvation shell around glucose, HDX is an exothermic process. Additionally, we found that greater percentages of D2O had lower system energies. We then compared the ground state energies before and after exchange of both primary and secondary hydroxyls (those located on C6 and C4, respectively). We found that a larger release of energy is associated with exchange of a secondary hydroxyl (p-value < 0.0001). We then added a sodium adduct to the secondary hydroxyl. Comparing the adducted exchange site to the adjacent site (on a primary carbon) the data showed that the presence of the sodium adduct stabilized the exchange site by approximately 2.08±0.03 kcal/mol (p-value <0.001). These data demonstrate that exchanging hydroxyls on secondary carbons is more stable and the presence of an adduct makes exchange at that site energetically favorable.

Populating a Vacuum Ultraviolet Spectroscopy Library using Tandem GC/VUV-MS and Chemometric Deconvolution of Real-World Sample Data

URSA Award Winners

Shubhneet Warar, Ian Anthony, Christina Gaw, Touradj Solouki, Ph.D. Gas chromatography (GC) coupled to vacuum ultraviolet (VUV) spectroscopy and mass spectrometry (MS) can provide highly confident chemical identities; however, one limitation is the small size (<2,000 compounds) of available VUV search libraries. Previously, the VUV library has been expanded via analysis of purchased standards, but acquiring a large number of standards can be costly and inefficient. Here, we introduce a novel approach for rapidly populating a VUV library using complex “real-world” samples that contain unknown chemicals and are analyzed using tandem GC/VUV-MS and VUV Automated Library-Integrated Deconvolution (VALID). Perfume and essential oil samples were injected into a GC/VUV-MS instrument (GC-14B; 60 m column) (VGA-100) (TQMS 1200L) and data was collected. Library matching was performed with MS Search (NIST) and a python script against the NIST 2014 EI-MS and GC RI. To extract convoluted VUV spectra using VALID, a SIMPLISMA-ALS-based workflow for the deconvolution and R-squared for the library-comparison metric was employed. VALID successfully identified analytes from convoluted regions of chromatographic data that could not be detected by manual inspection (i.e. “hidden” GC peaks such as β-elemene). With GC-MS library-searching, extracted spectra were assigned and stored in the VUV library. As proof of concept, pure ethyl salicylate and oxybenzone samples were analyzed to confirm VUV spectral assignments. Combined use of VALID and the GC-MS library yielded identifications of three unknowns (viz., sandalrome, amyl salicylate, helional). In total, 16 analytes were identified from two samples (costing < $20 total) and expanded the VUV library, avoiding a $1,400 cost if purchased individually.

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Family & Consumer Sciences

Understanding the Effect of Shopping on Young Adults with Body Shame and Weight Preoccupation Simar Goyal, Jay Yoo, Ph.D. The importance of retail therapy as a distinct type of shopping to relieve stress has been recognized because nearly one in three American engages in retail therapy. Four distinctive therapeutic aspects of shopping have been identified: Therapeutic shopping motivation, positive mood reinforcement, negative mood reduction, and shopping outcome. Therapeutic shopping can, therefore, be utilized to enhance selfimage and positively impact emotional well-being and perceived quality of life. However, past studies have neglected to understand the relationship between therapeutic shopping motivations and self-consciousness associated with body image. Body shame triggers selfcriticism, leading to overcompensated personal features. In addition, weight preoccupation plays a central role in overall body satisfaction or body image, especially among women. Based on the individuals’ level of body shame and weight preoccupation, retail therapy showed a positive impact on those who have higher body shame. In addition, negative mood reduction has a positive effect, especially on those who are highly preoccupied with weight. Given a large number of patients with body-related disorders, mental health professionals should look to retail therapy to treat body image issues such as depression, sadness, poor body image, and body dissatisfaction. This result also coincides with the previous findings that changing one’s appearance by using clothing, cosmetics, hair, weight management, plastic surgery, skincare, accessories, and nails can be utilized to alleviate body image distress. Retailers must incorporate shopping strategies that will reduce a negative mood. Retailers should provide positive shopping experiences so that these purchases make customers feel better about their bodies and mind. Therefore, retail therapy, as body image enhancement, should be applied for a wide application in retail, as well as medical contexts for the future.

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Scientia's Mission Scientia shall provide a professional platform upon which undergraduates of Baylor University are able to publish personally conducted and outstanding research in the areas of biological sciences, physical sciences, mathematics, and technology.

Accepted Formats Research Articles (maximum 4500 words including captions and references) presenting major findings performed by current undergraduate level students enrolled at Baylor University. Research articles must include an abstract, introduction, materials and methods, up to six figures or tables, results, and discussion. Review Articles (maximum 6000 words including captions and references) synthesizing developments of interdisciplinary significance written by current undergraduate level students enrolled at Baylor University. Review articles must include an abstract and an introduction outlining the topic of discussion. Abstracts (maximum 500 words) proposing research topics currently being investigated by current undergraduate level students enrolled at Baylor University.

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Want to become involved in research at Baylor University and beyond? Learn about Baylor Undergraduate Research in Science and Technology (BURST)! BURST is the student organization for Baylor undergraduate students interested in scientific research. Mission To increase awareness of undergraduate research within the Baylor campus, we aim to provide opportunities for undergraduates to optimize their research experiences, and educate them in the proper habits and techniques of research in scientific fields. Journal Clubs Members participate in peer-led Journal Clubs of a variety of fields. Each Journal Club reads through and discusses a selection of research articles. Lab Tours A tour of the lab in the Baylor Sciences Building (BSB) allows members to see various research environments across campus. Members have the opportunity to ask questions, visualize the research techniques they have learned about, and occasionally gain hands-on experience with lab equipment. Scientia Scientia is the Baylor Undergraduate Research Journal of Science and Technology. First published in the Spring of 2014, Scientia is a yearly publication produced by BURST members and supported by the Baylor College of Arts and Sciences, and funded by Baylor Student Government. Conferences (HoT) Members who are currently doing research are encouraged to attend a variety of conferences, where they can present their findings to the scientific community in a professional environment. BURST works closely with URSA during URSA Scholars' Week, and recently launched the inaugural Heart of Texas (HoT) Undergraduate Research Conference. Service in the STEM Fields BURST organizes opportunities for members to actively engage in spreading interest for the sciences and technology in Waco. STEM Lectures Each semester, BURST organizes lectures featuring research experts from Baylor and beyond. Academic Workshops BURST organizes a workshop once a semester to teach its members fundamental laboratory skills. We also have workshops dedicated to advanced lab techniques, Resume and CV, Internship Applications, Cover Letters, How to Create a Research Presentation, and much more! Social Events Throughout the semester, BURST offers several opportunities to just hang out with other members! Research Internship Day BURST hosts an annual BURST Research Internship Day to increase awareness of the many research internship opportunities for undergraduate students at Baylor. For Prospective Members Are you interested in joining BURST? Please contact us at burst@baylor.edu and find out more at https://www.baylor.edu/burst/

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