FYR 2022 Panther RISE Impact Report- Research & Innovation- Prairie View A&M University

Message from the Vice President

Magesh T. Rajan, Ph.D., P.E., M.B.A. Vice President, Research & Innovation Prairie View A&M University

The PRISE grant program is a joint faculty research development initiative between PVAMU and Texas A&M University, first launched in January 2021. The PRISE grant program aims to establish and nurture collaborations across different disciplines between the two universities to address complex societal challenges that require multi-disciplinary approaches. Thus, the PRISE program provides a competitive edge to both PVAMU and Texas A&M for external funding opportunities.

In the inaugural round, proposals were invited from collaborative triads of one PVAMU and two Texas A&M researchers. Ten of the twenty-nine submitted proposals were selected after obtaining three reviews for each one. Each of the selected teams was awarded a grant of $30,000.

The Division of Research & Innovation (R&I) is proud to present the progress reports of the first cohort of the PRISE program. Many PRISE awardees have submitted inter-disciplinary grant proposals to federal funding agencies, utilizing the preliminary data collected.

As PVAMU has recently been designated as a Carnegie R2 institution of high research activity, R&I is excited about continuing such collaborative initiatives with R1 institutions. Go Panthers!

Message from the Vice President

Jack G. Baldauf Vice President for Research Texas A&M University

program. We look forward to the results of their collaborations. We also urge faculty-researchers at both institutions to apply for future rounds as they become available.

Since October 2020, our universities have worked together to encourage the submission of multidisciplinary proposals from inter-institutional teams of faculty-researchers, responding to federal requests for proposals that address our nation’s most complex problems.

The PRISE program is an excellent example of how universities like ours can cooperate to produce world-class, cutting-edge research.

Noushin

Rania

Victoria

Resilience

Oluwagbemiga

Introduction

The COVID-19 pandemic impacted all aspects of education, including the persistence of undergraduate engineering students. Persistence in engineering varies across gender, race/ethnicity, and the intersection of the two variables (Ohland et al., 2011). The “Analyzing Impact of Covid-19 on Student Learning and Persistence: A Multi-Dimensional Study” investigates how the COVID-19 pandemic has affected the performance and persistence of undergraduate engineering students at a large public R1 university and a Historically Black College and University (HBCU), both in the Southwest. This progress report will provide a summary of the work completed so far and next steps.

Methods

Institutional data were requested for undergraduate engineering students at the R1 university and the HBCU. The data for first-year engineering students were sorted by race/ethnicity, financial need, and firstgeneration status. For each institution two pre-COVID cohorts (Fall 2017/Spring 2018 and Fall 2018/2019) and one COVID cohort (Fall 2019/Spring 2020) were identified. The attrition rate from the first year to the second was calculated for each cohort. Survey data was then collected from the COVID cohort through Qualtrics to measure students’ self-efficacy and engineering identity, and the impact of the pandemic on students’ learning. The surveys were emailed to students at both institutions and made available for approximately ten days for students to complete. To increase response rates, survey completion was incentivized and two reminder emails were sent. Focus group data collection is a work in progress.

Results

Preliminary analyses in the form of summary statistics of the institutional data from the R1 university (Callahan et al., 2022) and the HBCU (Burnett et al, 2022) were previously disseminated. The attrition rate for many first-year engineering students at the R1 university in the COVID cohort were lower than in pre-COVID cohorts. And the attrition rate for all first-year engineering students at the HBCU, including African American/Black students, was approximately the same for the first pre-COVID and COVID cohorts with an increase for the second pre-COVID cohort. The analysis of survey data is currently in progress and focus group data collection is a work in progress.

Camille S. Burnett, Ph.D. Assistant Professor Department of Curriculum & Instruction, Whitlowe R. Green College of Education

Analyzing Impact of Covid-19 on Student Learning and Persistence: A Multi-Dimensional Study

Discussion

The results concerning attrition rates among first-year engineering students at both institutions contradict initial hypotheses as it was believed there would be higher attrition rates among first-year engineering students at the onset of the COVID-19 pandemic than in pre-pandemic first-year engineering students. The results highlight the importance for further analyses of the institutional data and analyses of the survey data collected. They also highlight the need for focus groups to better understand the findings from institutional data and survey data.

Significance/Impact

The results of this pilot study will assist faculty and university administrators better understand engineering students’ persistence based on their learning and academic experiences as we emerge from the COVID-19 pandemic. Although the project is still in progress, the results will be used to develop a larger scale study; the proposal will be submitted to the National Science Foundation (NSF). The larger study will focus on developing an instrument for measuring students’ readiness for future uncertainties and evidence-based strategies for supporting students academically and professionally during such times.

References

Burnett, C., Rambo-Hernandez, K., Nepal, B., Lockett, L., Callahan, S., Pedersen, B. (2022, April 8). Trends in firstyear engineering student attrition rates: An examination of institutional data [Poster Presentation]. Prairie View A&M University Faculty Research Day, Prairie View, Texas. Callahan, S., Pedersen, B., Lockett, L., Burnett, C., Nepal, B., & Rambo-Hernandez, K. (2022). Persistence and the pandemic: Retention of Historically Underrepresented First-Year Engineering Students Before and After COVID-19 [Accepted]. ASEE Annual Conference Proceedings. Washington, DC: American Society for Engineering Education Ohland, M. H., Brawner, C. E., Camacho, M. M, Layton, R. A., Russell, R. A., Lord, S. M., & Wasburn, M. H. (2011, April). Race, gender, and measures of success in engineering education. Journal of Engineering Education, 100(2), 225–252. https://doi.org/10.1002/j.2168-9830.2011.tb00012.x.

Karen E. Rambo-Hernandez, Ph.D.

Associate Professor

Teaching, Learning, and Culture: STEM education Educational Psychology: Research, Methods, & Statistics College of Education and Human Development Texas A&M University

Bimal Nepal, Ph.D.

Professor and Associate Director of Industrial Distribution Undergraduate Program Department of Engineering Technology & Industrial Distribution Department of Industrial and Systems Engineering (Courtesy Appointment) College of Engineering Texas A&M University

Multimodal Knowledge-Graph Construction for Combating Global Pandemics

Dong, Xishuang

Assistant Professor

Department of Electrical and Computer Engineering, Roy G. Perry College of Engineering

Introduction

Global pandemics have caused immeasurable loss both socially and economically. For COVID-19, although promising progress has been made on its vaccines, the pandemic is still far from over, as infection cases continue to surge globally, and many scientific questions remain unanswered. Furthermore, when future global pandemics of similar or even worse natures strike, science must get ready. Artificial intelligence (AI) has actively contributed to fighting against such pandemics. As a powerful AI tool, knowledge graph enables researchers to combat such pandemics. The objectives of this project include: 1. Efficient knowledge-graph construction for new concepts and relations in free-text medical documents based on weakly-supervised machine learning; 2. Integrating information of medical images and texts effectively for multimodal knowledge graph construction; 3. New memory-adaptive schemes for constructing, storing and utilizing knowledge graphs in practical systems.

Methods, Results, and Discussion

To achieve the objectives, current progresses are below.

• We proposed a novel ensemble deep learning model that integrates bagging deep learning and model calibration to enhance performance of semantic segmentation, as well as reduce prediction uncertainty. It includes three stages: 1) training multiple state-of-the-art DL models on training Chest X-Ray (CXR) datasets; 2) Calculating calibration errors to measure prediction uncertainties of these DL models on validation CXR datasets, where expected calibration error (ECE) and maximum calibration error (MCE) are employed to estimate the prediction uncertainties; 3) Applying weighted voting of predictions on testing CXR datasets generated by these DL models to implement calibrated bagging deep learning, where the weight of each DL model is inversely proportional to the calibration error; Experimental results demonstrate that the proposed method not only enhances the performance of semantic segmentation, but also improves the prediction certainty on CXR data [1].

• We have developed a system on natural language processing (NLP) that can generate queries for databases (both as human-readable questions and as SQL queries), which can be used for finding information in healthcare databases [2]. We have also developed AI tools for image segmentation tasks that are related to biological images [3]. Furthermore, we have been analyzing tools that can be used to further improve the optimization of AI models designed for knowledge graphs and healthcare tasks, and their reliable and privacy-preserving storage in storage systems [4].

• We introduced a new method for NER that builds better entity mention representations and explicitly connects entity mentions based on both global coreference relations and local dependency relations, which incorporated entity mention relations by Graph Neural Networks and showed that our system noticeably improves the NER performance [5]. In addition, we introduced the task of fine-grained location recognition that identifies crossroads, buildings, neighborhoods etc., by creating a dataset annotated with fine-grained locations in tweets and building strong baselines for this task based on curriculum learning [6].

Significant/Impact

The techniques we developed will be able to: 1. improve image segmentation performance, as well as enhance prediction uncertainty for safety-critical locations; 2. enhance the efficiency of querying knowledge graphs and databases for healthcare information, for analyzing big data, and for storing the information reliably in storage systems while preserving privacy; 3. address two gaps of general domain NER research, effectively enhance system performance on named entity recognition targeting a specific domain, and introduce the task of fine-grained location recognition that will enable many high stake downstream applications.

References:

[1] Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim, Xishuang Dong. “Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image”, to be submitted to PLOS ONE. 2022

[2] Xiaojing Yu and Anxiao (Andrew) Jiang, Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates, in Proc. 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 3202-3212, Kyiv, Ukraine, April 2021.

[3] Kailun Zhang, Kiara Pankratz, Hau Duong, Jingwen Guan, Anxiao (Andrew) Jiang, Yiruo Lin, and Lanying Zeng, Interactions Between Viral Regulatory Proteins Ensure a Constant Probability of Host Outcome during Infection by Bacteriophage P1, in American Society for Microbiology (ASM) journal mBio, vol. 12, no. 5, September 2021.

[4] Xiangwu Zuo, Anxiao (Andrew) Jiang, Netanel Raviv, and Paul H. Siegel, Symbolic Regression for Data Storage with Side Information, to be submitted to IEEE Information Theory Workshop (ITW), May 2022.

[5] Pei Chen, Haotian Xu, Cheng Zhang and Ruihong Huang. Crossroads, Buildings and Neighborhoods: a Dataset for Fine-grained Location Recognition. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL 2022), to appear.

[6] Pei Chen, Haibo Ding, Jun Araki and Ruihong Huang. Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition. In Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL 2021) (short paper).

Huang, Ruihong

Assistant Professor, Department of Computer Science and Engineering Texas A&M University

Jiang, Anxiao (Andrew)

Full Professor, Department of Computer Science and Engineering Texas A&M University

Understanding the Response to USDA Food Aid among Minority Residents and Farmers in COVID-19

Noel M. Estwick, Ph.D. Assistant Professor and Research Scientist Department of Agriculture, Nutrition and Human Ecology College of Agriculture and Human Sciences

Project Goals

The goal of the study is to understand the impact of the USDA Farmers to Families Food Box Program in limited-resource communities. Initial focus is on 8 Texas counties. They are Freestone, Jasper, Cass, Walker, Waller, Leon, Washington, Bexar, and Harris. The project has received approval from TAMU IRB #IRB2021-0408D.

Students are being mentored and trained in conducting research. Activities that build on previously reported training include content analysis and conducting focus groups.

Presentations

March 2022, 36th Annual Minorities in Agriculture Natural Resources and Related Sciences (MANRRS) Conference in Jacksonville, FL. Kyla Peer won 1st place in the Undergraduate Oral Research Competition-D2. (See picture below)

April 2022, Association of Research Directors Meeting in Atlanta, GA.- Kyla Peer and Lauren Page delivered an oral presentation. Progress and Future Plans

The research team is currently analyzing the interview data. Two students were being trained in content analysis using ATLAS.ti software. One student (Kyla Peer, PVAMU) graduated in May 2022 and the other student (Daniela Wong Duarte, TAMU) will continue throughout the summer since funds remain on the TAMU budget. The research team is awaiting final PVAMU approval to conduct a focus group with farmers who are part of the 100 Ranchers Organization of Texas.

On August 26, 2021, the team submitted an REU Site collaborative research proposal (PVAMU Proposal #2110883) for funding to the National Science Foundation. The requested amount is $526,742. The project title is “Building Next Generation Leadership in Research and Translation for Disaster Resilience and Food Security.” The project was not funded but the team intends to make changes based on reviewer comments and resubmit the proposal. The team will prepare articles for publication in peer reviewed journals.

Students and Research Faculty/ Collaborator Presentations

April 2022, the research team submitted a poster for Texas Land Grants Day, held at Prairie View A&M University.

Other Pertinent Information

Dr. Jerrel Moore, Research Assistant Professor from the College of Nursing is assisting the team with statistical analysis.

Michelle Annette Meyer, Ph.D. Associate Professor, Department of Landscape Architecture and Urban Planning Texas A&M University

Other Collaborator(s)

Dr. Jennifer Garza

Extension Program Specialist II-Family and Community Health Prairie View Cooperative Extension Program

Dr. Jerrel Moore

Research Assistant Professor, Prairie View A&M University College of Nursing

Student Researchers

Rebekka Dudensing Ph.D.

Associate Professor, Department of Agricultural Economics Texas A&M University

Daniela Wong Duarte, TAMU Sarah Judkins, TAMU Kyla Peer, PVAMU Noelia Rosas, TAMU Lauren Page, PVAMU

Investigating the Effectiveness of Prediction Markets in Teaching Project Risks

Proposal Summary

Teaching construction and engineering project risks in undergraduate classes is a challenging task. In our experience, students regard mathematical concepts from probability theory as too abstract and unreal. As a result, students become suspicious of the presented material and disengage in any further exploration of the topic. This feedback loop, where a lack of realism affects student engagement and deficiency in engagement prevents further investigation, represents a major hurdle and stalls the education process. Successful recruitment and retention of engineering students rely on engaging classroom activities and, at the same time, can accurately illustrate how engineering systems work and behave. Due to the increasing complexity of designs, human-technology interactions, and project operating requirements, engineering systems and projects are becoming less predictable; in other words, we do not know with absolute certainty how a system or project will perform. Hence, it is essential that, in addition to developing engaging activities to demonstrate deterministic models such as Newton’s Laws, we communicate and teach future engineers probabilistic reasoning, risks, and concepts such as Bayes’ Law. Therefore, this research aims to measure the prediction market’s effectiveness as an engaging tool in teaching project risks.

Vahid Faghihi, Ph.D. Assistant Professor, Construction Science Department, School of Architecture

Ivan Damnjanovic, Ph.D. Professor, Zachry Dept. of Civil and Environmental Engineering, College of Engineering Texas A&M University

Diversity of grass shrimp (Palaemon sp.) and their parasites (Microphallus spp.) in Gulf of Mexico wetlands

Introduction

Coastal wetlands along the Gulf of Mexico (GoM) coast are complex systems of great ecological and economic significance, providing ecosystem services such as storm protection, erosion control, carbon sequestration, support of vertebrate and invertebrate fisheries, and recreation (e. g. Engle 2011). Grass shrimp in the genus Palaemon are ubiquitous inhabitants of coastal saltmarshes and freshwater wetlands. They play important roles in wetland trophic dynamics by controlling detritus and providing a food source to larger organisms (Gonzalez 2016). Palaemon spp. are often infected by a common trematode parasite, Microphallus turgidus, which negatively impacts their reproduction and therefore affects their population dynamics (Pung et al., 2002). Our research aims to 1) use a combination of traditional taxonomy and DNA barcoding to determine species identifications and geographic distributions of Palaemon spp. in freshwater and estuarine habitats along the northern GoM; 2) Determine which species/populations are affected by the parasites; and 3) Predict the physiological impacts of parasite infection by comparing gene expression profiles between infected and uninfected shrimp.

Methods, Results, and Discussion

We are morphologically examining Palaemon spp. specimens from 55 populations along the northern Gulf of Mexico, collected since 1972, ranging from Florida to the Laguna Madre, and confirming the taxonomic species identifications. We are sequencing a subset of those (~100 specimens) for the DNA barcoding marker cytochrome c oxidase subunit I (COI). The sequences are then aligned in combination with publicly available sequences and phylogenetic trees are generated. Preliminary analyses that DNA barcoding largely confirms morphological identification, although a few of the DNA sequences suggested unexpected phylogenetic affiliations. Additionally, we are able to detect genetic variation among populations of the same species.

With regard to the prevalence of parasite infection, we have to rely on freshly collected material because the parasites are only visible in live samples. We are therefore focusing on samples from the area between Galveston and Corpus Christi for this aspect of the project. The differential gene expression analysis has been delayed due to equipment and supply chain issues, but we expect to complete this aspect over the summer of 2022.

Noushin Ghaffari, Ph.D. Assistant Professor, Department of Computer Science, Roy G. Perry College of Engineering

With the aid of our students, we collected and examined grass shrimp (Palaemon spp.) from the vicinity of Corpus Christ, Texas to the border of Texas and Louisiana, and received specimens from Florida. Our genetic analyses indicate that previous species records from Texas may be misidentifications. The shrimp from Louisiana are genetically distinct and may be a different species. We located a “hot spot” for the parasite Microphallus in a ditch along Highway 45 north of Galveston. We observed snowy egrets hunting the shrimp. They are likely to be primary hosts for the parasite.

Significance/Impact

This project provides training opportunities for two graduate students, one being supervised by Ghaffari at PVAMU and another one being co-supervised by Schulze (TAMUG) and Wicksten (TAMU). The TAMU/G graduate student has recently submitted a proposal to Texas Seagrant for additional funds to expand the project.

Palaemon have been used as test subjects for toxicology studies due to their high sensitivity to toxic substances, and have the potential to act as a wetland bio indicator due to their ecological relevance (Key et al., 2006). Similarly M. turgidus, which must pass through three species to complete its life cycle, also has the potential to act as a wetland bio indicator (Marcogliese 2005). Learning more about the diversity, distribution, and physiology of these species is integral to an improved understanding of wetland ecology.

References

Engle, V.D. (2011). Estimating the Provision of Ecosystem Services by Gulf of Mexico Coastal Wetlands. Wetlands 31(1): 179-193.

Gonzalez, S.T. (2016). Influence of a trematode parasite (Microphallus turgidus) on grass shrimp (Palaemonetes pugio) response to refuge and predator presence. Journal of Parasitology 102(6): 646-649.

Key, P.B., Wirth, E.F., Fulton, M.H. (2006). A review of grass shrimp, Palaemonetes spp., as a bioindicator of anthropogenic impacts. Environmental Bioindicators, 1(2), 115-128.

Koehler A. V., Poulin, R. (2012) Clone-specific immune reactions in a trematode-crustacean system. Parasitology, 139(1), 128-136.

Marcogliese DJ (2005) Parasites of the superorganism: are they indicators of ecosystem health?. International journal for parasitology, 35(7), 705-716.

Pung, O.J., Burger, A.R., Walker, M.F., Barfield, W.L., Lancaster, M.H. & Jarrous, C.E. (2009): In vitro cultivation of Microphallus turgidus (Trematoda: Microphallidae) from metacercaria to ovigerous adult with continuation of the life cycle in the laboratory. Journal of Parasitology 95(4): 913-919.

Mary Wicksten, Ph.D. Professor, Department of Biology, College of Science Texas A&M University

Anja Schulze, Ph.D. Professor, Department of Marine Biology Texas A&M University at Galveston

Project Introduction

The encouragement of people to use outdoor settings rather than indoor settings has many potential benefits and could be an outcome of urban architecture and landscape architecture [4]. A research agenda to address this topic could increase knowledge of the characteristics of outdoor space that affect thermal comfort and provide a modeling and simulation system that is artificially intelligent to predict the outdoor thermal comfort in different scenario,s therefore, guiding designers to create better spaces. Several objectives were addressed:

1. Increasing knowledge of the impacts of climate factors on thermal comfort in outdoor spaces through empirical studies of energy budgets of individuals in various settings.

2. Modeling sample urban environments as 3D structures, vegetation, and surfaces, enriched with climatological factors, such as maps of temperature, wind, solar radiation, terrestrial radiation, and humidity.

3. Devising a neural network, a machine learning algorithm, to establish associations of locations in the urban environment to predict thermal comfort.

In this seed phase of the research, the proximate objectives were more modest:

1. Cross training the team of faculty members and graduate students in their various areas of expertise.

2. Testing various tools, software, instruments, algorithms, and resources to select those that are best suited for further research.

3. Producing convincing research proposals aligned with national research priorities that will obtain funding.

4. The proposed research study served as the seed for initiating research at the undergraduate level at the Prairie View A&M School of Architecture

Rania Labib, Ph.D. Assistant Professor, Department of Architecture

An Interdisciplinary Team for Investigating a Machine Learning Framework for Predicting Outdoor Thermal Comfort to Reduce Energy Needs of Future Urban Development

Methods

The broad hypothesis guiding this research agenda is that Outdoor urban spaces can be made more attractive by designing them for thermal comfort. An additional hypothesis is that Simulation of the thermal comfort of outdoor urban spaces enables designers to make better decisions and produce more attractive environments. These hypotheses were tested by building a test apparatus (software to support design) and then observed whether the resulting designed urban spaces are improved. The measurement of improvement was done empirically by examining the resultant urban spaces. The researchers developed a prototype software tool for design decision support. Dr. Brown provided authoritative guidance for algorithms and models of thermal comfort in outdoor settings, including testing of data collection tools such as stationary and mobile weather stations, exercise and physiological tracking tools, and mobile apps for tracking and communications. Dr. Clayton guided the construction of models of test environments. Dr. Labib oversaw the design of a test neural network that was trained with synthetic data to predict the thermal comfort of locations in the urban environments.

Results

• Planned a target of a machine learning application for outdoor environment prediction.

• Adopted EnergyPlus simulation engine to generate machine learning training dataset.

• Prepared a test dataset for training based on a sample building model.

• Built a prototype in a BIM software, Autodesk Revit, using Dynamo visual programming tool.

• Tested the prototype on a sample BIM model to observe whether the trained neural network model is working for predicting landscape surface temperature prediction.

Discussion

Our team has made a prototype model in building information modeling (BIM) environment to investigate the concept for neural networks (NN)-based outdoor thermal comfort evaluation. The target of the trained NN model is predicting landscape objects’ surface temperatures for longwave radiation flux calculation. We could observe that 3D geometry in a BIM model provides sufficient information to predict key factors contributing to outdoor thermal comfort, such as sky view factor and material information. Further measurement and simulations are needed for the validation of our approach.

The successful completion of the prototype BIM model led the team to pursue a more accurate CAD prototype which allows for the integration of OpenFoam wind simulations for accurate calculations of the surface temperature of objects within the urban context. The wind simulations will be paired with EnergyPlus surface temperature calculations and the results of both processes will be used to train a CADintegrated NN algorithm to predict the outdoor thermal comfort of future urban environments. However, creating this CAD model can be time-consuming, considering that thousands of wind simulations are needed to train the NN model. Therefore, the team anticipates validating the BIM model for this PRISE project but will also work on the CAD model beyond the PRISE due date for future grant opportunities.

External Funding

The team used the initial results of the PRISE research to submit a multi-disciplinary proposal to the Department of Energy (DOE). The submitted proposal is lead by Dr. Labib and included nine Co-PIs from PVAMU and TAMU, including the TAMU faculty who contributed to the PRISE research. The proposal is titled “Urban Field Lab for Comprehensive Sensing to produce Calibrated AI Models for Optimizing and Guiding Planning and Design Decisions” and the budget is $10.5M. Although the proposal was rejected the team plans to pursue other funding opportunities.

Broader impact

Although this research agenda has not been widely or deeply pursued, it potentially can have significant impacts on several major social problems. Global Climate Change and Urban heat Island Intensification are combining to make cities more dangerous. Twice as many people die from heat than either floods or storms. With more than 80% of Americans now living in cities [2],it is critical that urban environments be

designed to ameliorate rather than exacerbate the urban heat [3]. Weight reduction and health benefits of outdoor activity are accepted as having the potential to greatly reduce cost of healthcare. Outdoor activity is also shown to improve social cohesion and reduce stress. Reduction of energy use to achieve thermal comfort is considered a major objective for achieving reduction of global warming. By providing thermally comfortable outdoor spaces especially in hot climates, inhabitants are likely to spend less time in indoor settings that consume excessive amounts of energy for heating and cooling. Furthermore, thermal comfort could increase the commercial value of outdoor spaces used for recreational activities that are provided by service sectors such as restaurants, cafes, and theme parks. The proposed modeling and simulation framework is expected to not only predict the thermal comfort in outdoor spaces but also allow architects and planners to reach a decision in the early design process almost instantly instead of working for weeks or even months to complete complex simulation tasks, thus empowering architects, and planners with advanced knowledge to optimize the built environment’s comfort and energy performance which ultimately leads to a huge reduction in energy cost.

References

[1] M. Nikolopoulou, N. Baker, K. Steemers, Thermal comfort in outdoor urban spaces: understanding the human parameter, Sol. Energy. 70 (2001) 227–235.

[2] U. Nations, World Population Prospects: The 2015 Revision, U. N. Econ. Soc. Aff. XXXIII (2015) 1–66. https://doi.org/10.1007/s13398-014-0173-7.2.

[3] A. Qaid, H. Bin Lamit, D.R. Ossen, R.N. Raja Shahminan, Urban heat island and thermal comfort conditions at micro-climate scale in a tropical planned city, Energy Build. 133 (2016) 577–595. https://doi.org/10.1016/j. enbuild.2016.10.006.

Mark J. Clayton, Ph.D. Professor, Department of Architecture Texas A&M University

Robert Brown, Ph.D. Professor, Department of Landscape Architecture and Urban Planning Texas A&M University

Nanoengineered materials to modulate mitochondrial function

Xiangfang (Lindsey) Li Ph.D. Associate professor at ECE Department

Introduction:

This multidisciplinary project leverage the team’s complementary expertise in Bioengineering, Veterinary Physiology, and Electrical and Computer Engineering, it bridges multiple research areas.

Methods, Results, and Discussion:

The goal of the PVAMU team focuses on modeling cell functions and cell type identification is a must. Thus, deep neural networks have been employed in this work to identify cell types from scRNAseq data with high performance. However, current methods using neural networks rely on the availability of a large amount of individual cells with accurate and unbiased annotated types to train the identification models. Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as it involves manual inspection of marker genes. To overcome this challenge, we propose a semi-supervised learning model “SemiRNet” to use unlabeled scRNAseq cells and a limited amount of labeled scRNAseq cells to implement cell identification.

There are two paths in the proposed model: supervised learning path for obtaining supervised cross entropy loss and unsupervised learning path for unsupervised mean squared error loss, respectively. Then training is performed by jointly optimizing these two losses, and this allows the proposed scheme to take advantage of both information from the labeled cells and information from the unlabeled cells. Furthermore, we introduce a preprocessing procedure to overcome the problem of data sparsity. Experimental results indicate that the proposed model could identify cell type effectively using very limited labeled cells and a large amount of unlabeled cells.

In summary, a novel framework of deep semi-supervised learning is proposed for cell type identification on scRNAseq data, specifically, we propose a deep semi-supervised learning model based on recurrent convolutional neural networks (RCNN) that can utilize unlabeled cells to enhance identification performance. As an emerging research area, implementing cell type identification automatically is extremely important for the downstream analysis on the scRNAseq data. In our future work, we plan to extend the proposed model for other tasks such as pathway network construction.

In this project, a PhD. Student, Shanta Chowdhury is supported. The progress of the student is encouraging, a conference paper and a journal paper are published based on the results.

Significance/Impact:

The project is very innovative and it bridges multiple research areas. This multidisciplinary project will leverage the team’s complementary expertise in Bioengineering, Veterinary Physiology, and Electrical and Computer Engineering. The efforts will boost collaboration and start new research initiatives leveraging the existing research capabilities in both PVAMU and TAMU.

References: X. Dong, S. Chowdhury, U. Victor, X. Li, L. Qian. (2022) “Semi-supervised Deep Learning for Cell Type Identification from Single-Cell Transcriptomic Data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics. S. Chowdhury, X. Dong, O. A. Solis, L. Qian and X. Li, “Cell Type Identification from Single-Cell Transcriptomic Data via Gene Embedding,” 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 258-263, doi: 10.1109/ICMLA51294.2020.00050. Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim, Xishuang Dong, “Semi-supervised Learning for COVID-19 Image Classification via ResNet”, EAI Endorsed Transactions on Bioengineering and Bioinformatics, Volume 1, Issue 3, e5, 03 2021 - 08 2021.

Annie Newell-Fugate, Ph.D.

Assistant Professor of Veterinary Physiology and Pharmacology Texas A&M University

Akhilesh Gaharwar, Ph.D.

Associate Professor at the Department of Biomedical Engineering Texas A&M University

Victoria Mgbemena, Ph.D. Prairie View A & M University

Co-Investigator: Anna Joy Prairie View A & M University

Project: Goals:

Breast cancer (BC) is the second most common cause of brain metastasis (BCBM) behind only lung cancer. Here we propose to do develop HER2+ inhibitors that overcome drug resistance and optimize CNS penetration of the promising lead compounds for efficacy against HER2+ Breast Cancer brain metastasis (BCBM) and determine the epigenetic and functional properties of the structurally optimized compounds.

Progress:

We have had one of our TAMU post-doctoral scholars, Maryam M. El Gawain, present the following presentation in April 2022:

Preclinical Development and Emerging Strategies for Design of Selective Kinase Inhibitors Targeting Brain Metastasis of Resistant HER2+ Breast Cancer

Future immediate experiments:

Complete characterization of BCBM model, perform inhibitor studies followed by molecular biology experiments. Timecourse experiments will be performed using 2D monolayer of cells, outlined below:

1. We will expand the cells in T-25/T-25 flasks, before moving them to the following formats: 6 well plate, 12 well plate, 24 well plate, 96 well plate. Media will be replaced daily. We will make sure the cells do not achieve 100% confluence, and will collect at 60% confluence.

2. We will trypsinize the cells using 0.25% trypsin after washing 3X with Phosphate buffered saline (PBS We will centrifuge the cells, take off the old media and replace with fresh media.

3. We will use the following formula to calculate the doubling time: Doubling Time = [ T × ( ln2 ) ] / [ ln (Xe / Xb) ] where T = Time in hours. The number of cells starting at the beginning is Xb. The number of cells at the end is referred to as Xe. From there, we will proceed with the following: 1. MTT cytotoxicity/viability assays in the presence of the HER2 inhibitors. This will be followed by 2. An Annexin V assay for apoptosis at IC50 will also be carried out in monolayered cells 3. We will assay for EMX1 and PAX6 via Western blot followed by investigating the expression of Wnt5a with Wnt2b1, and Tgfb22 (to assess cytoarchitecture and confer function in organoids).

The timeline for initiation of this part of the project: Start June 17.

Mahua Choudhury, Ph.D. Texas A&M University

Hamed Ismail, Ph.D. Texas A&M University

Oluwagbemiga Ojumu, Ph.D. Assistant Professor of Economics, College of Business

Introduction:

American Psychology dictionary defined resilience as the process and outcome of successfully adapting to difficult or life-challenging experiences through mental, emotional, and behavioral flexibility and coming out successfully. Several researchers (Kim and Hargrove 2013, Yeager and Dweck 2012; Artuch-Garde et al (2017), also see resilience as the ability to withstand adversity or recover from stressful and negative experiences or the ability to move forward and grow in response to difficulties and challenges, or to become stronger through adversity. The Coronavirus 2019 (COVID-19) pandemic brought new and unforeseen pressures on everyone, and the policies designed to alleviate its spread led to sudden, sweeping changes in individual and social behavior to increase their chances to survive the COVID-19 impacts. Resilience is multifaceted and covers a broad area of study that unfolds in many different aspects based on the resilience paradigm. Therefore, there is a need for a holistic approach to resilience to understand the scope in terms of types of population, and adjustment domains such as the COVID-19 restrictions. That is, approaching resilience as the ability and capacity of a system, which includes a person, the family, and the community to adapt successfully to challenges that threatened the functioning and survivability of individuals. The American Psychological Association identified three predominant factors that contribute to how well people adapt to adversities, (a) the ways in which individuals view and engage with the world, (b) the availability and quality of social resources, and (c) specific coping strategies. COVID-19 has had a strong impact on Black communities in the United States, and this differential impact extends to Black college students. Due to the sudden outbreak of the COVID-19 pandemic, new and unforeseen pressures lead to comprehensive social distancing measures widely adopted, which encompassed other restrictions, and students had to shelter in place at home and other places. Several other stressful situations arose as spin-offs from COVID-19. Surviving the stress caused by these various restrictions and the impacts of the pandemic, some students showed resilience, such as adopting active countermeasures as well as a more stable psychological state to deal with the crisis, while some students did not. We believe that by reducing risk factors and enhancing protective factors through the adjustments of norms, the abilities of the people can improve to develop resilience. This study examined the resilience of students at Prairie View A&M and Texas A&M Universities, focusing on the impact of COVID-19 on student progress to degree completion and graduation rates.

Resilience in Black College Students: Consequences of the COVID-19 Pandemic on Student Success

Motivation

Knowing that resilience is a phenomenon present in different domains with multifaceted nature, broad measures of resilience phenomenon unfolds in many different aspects of people’s lives with different responses and impacts. Therefore, understanding the resilience phenomenon in relation to academic success needs is important in order to have a better understanding of the students and their responses or perceived responses to different adverse COVID-19 impacts and control measures. This study focuses on students’ progress and graduation rates, which qualifies this research as academic resilience study. Academically resilient students are those who achieve success in school despite experiencing stressful events that place them at risk of performing poorly (Wang, Haertal, & Walberg, 1994). Artuch-Garde et al (2017) found resilience to be associated with a positive predictor of self-regulation, learning approaches and coping strategies. In two separate studies, De la Fonte et al and (2017) and Prickett et al (2020), also established a relationship between resilience and effective learning.

Methods, Results, and Discussion Procedure

The survey is designed to ascertain the conditions that prevail in a group of cases chosen for study in different modules. The modules explore the strong impact of COVID-19 in the United States, the respondents’ norms and responses to COVID-19 restrictions that could affect their norms, the differential impact that extends college students, trust in themselves and others, and their coping strategies. Starting with the “behavioral” premise that precautionary behavior is both a personal decision and a social interaction, we use a norms framework to explain the precautionary behavior we observe. As such, the behavior is governed by a set of social interactions - i.e, by social norms (Bicchieri 2005). Goldberg et al. 2020 report preliminary results estimating the causal effect of perceived social norms on COVID-19 preventive behaviors using data from a nationally representative survey. Social norms are proxied by responses to modules of survey questions regarding how often family and friends perform preventive behaviors, and whether they think it is important for the respondent to do so. The paper suggests an important link between perceived social norms and own preventative behavior. We surveyed two waves of subjects in 2017 and 2019 at the two schools (about 500 subjects at each). When COVID-19 hit in 2020, we obtained additional funding to re-contact our subjects to find out about the impact of the virus, students’ compliance with recommended precautionary behavior, as well as trust in institutions (government, media, educational – Eckel and Wilson 2004), and perceptions of social norms (Krupka and Weber 2013). This rich data set provides the ideal background to study the impact of COVID-19 on minority students’ resilience in the face of COVID-19. We analyze the possible relationship between resilience and observed precautionary behavior and positive coping strategies. The aim is to establish an associated model with predictive relations, identify causes, and make prepositions for intervention in different student profiles that will strengthen resilience.

Instruments and Data Analysis

Resilience is a complex construct that is somewhat difficult to assess. Ryan et al (2019) posit that resilience theory has its roots in studies of individual mental dysfunction, but it has evolved to focus beyond the individual to recognize the impact of social and environmental influences. In an assertion to the difficulty of resilience measure, King et al. (2016) described four waves of development in resilience theory. The first wave focused on the factors and characteristics that enable individuals to overcome adversity through self-esteem, self-efficacy, and optimism. This second wave examined how certain factors contribute to resilience, with a third wave focusing on the development of interventions to build resilience. The fourth wave focused on genetic, neurological, and developmental factors related to resilience capability. For this research study, resilience is defined as the ability to thrive in spite of risk of Covid-19 and will build on the first two waves highlighted by King et al (2016) and focus on exploring the various factors that contribute towards student resilience in the face of COVID-19. Our goal is to assess the impact of

COVID-19 and the Black Lives Matter events and protests on Black student resilience at two universities in Texas: Texas A&M University, a Primarily White Institution (PWI) and Prairie View A&M University, a Historically Black University (HBU). This study specifically explores the resilience of minority students in the face of COVID-19 pandemic and elicit factors that could promote recovery with respect to student progress toward their undergraduate degrees. Other findings show nine different protective factors identified in resilient students as being important factors for academic success:(1)individual aspirations;(2) personal factors;(3) academic behaviors;(4)family support;(5) academic environmental factors;(6) other support factors;(7) positive social behaviors;(8) negative social behaviors; and(9) spirituality.

Our hypothesis is that COVID-19 will affect students differently, depending on their financial resilience, psychological resilience, and support network from the university community and their respective families. In the face of the sudden outbreak of coronavirus 2019 (COVID-19), some students showed resilience in coping with difficulties while some did not. While different types of students showed different levels of resilience, are there significant characteristics among students with similar levels of resilience?

Data

The survey data comes from a large multi-wave survey panel of a diverse sample of university students. The project builds on samples of students from Rice University, Prairie View A&M University (PVAMU) and Texas A&M University (TAMU) that were recruited to participate in two prior studies which began in 2016. These panels constitute the subject pool for this paper. The same battery of questions were asked over five waves. The first wave began in early April 2020, the second wave began in late July, and the fifth wave with resilience modules began mid-June 2021. The objective of the waves used for this paper is to investigate factors underlying precautionary behavior for the spread of the coronavirus in the face of changing instructions about best practices for prevention and as the pandemic progresses, culminating into the final waves seeking to understand the respondents’ resilience. Therefore, embedded in different modules of the present study are incentivized and survey-based measures of preferences, norms, attitudes about COVID-19 prevention, and resilience. Altogether 299 respondents from the PVAMU and TAMU, and 719 RICE university students participated in the fifth wave of the study. In the preferred resilience specification, we rely on the sub-sample of subjects who completed all previous waves of the survey and completed the resilience modules to avoid issues of attrition. Specifically, the modules that helped in addressing resilience are feelings, trust, engagement extraversion, self-confidence, coping strategies, risk tolerance, and resilience scale.

Constructing the Variables Wang et al. (1994) concluded that resilient children have a clear sense of purpose about their future goals, generally perceive experiences constructively and believe in controlling their own fates. Windle et al. (2011) reviewed nineteen resilience measures and reported that all the measures had some missing information regarding the psychometric properties like reliability, validity, and internal consistency. They found no ‘gold standard’, but concluded that the Connor-Davidson Resilience Scale (CD-RSC), the Resilience Scale for Adults (RSA), and the Brief Resilience Scale (BRS) received the best psychometric ratings. Given this, resilience will be measured using the Brief Resilience Scale (BRC) that received the best psychometric ratings. The scales uses the 1) feelings of the respondents, 2) trust: who does the respondent trust - family and friends; local authorities; 3) self-confidence: how confident the respondents are in their own abilities (Q10), 4) coping- how the respondents are coping under the current COVID-19 situation, and 5) institutional support – how much support is received from family and university. We then attempt to quantify resilience by defining the different aspects that make up a resilient person, as verified by empirical data

The Brief Resilience Scale (BRS) is used to assess the perceived resilience, which is the ability to bounce back or recover from stress. The scale was developed with six questions to assess a unitary construct of

resilience, including both positively and negatively worded items. The possible score range on the BRS is from 1 (low resilience) to 5 (high resilience). For questions 1, 3, and 5: 1. Strongly Disagree, 2. Disagree, 3. Neutral, 4. Agree, 5. Strongly Agree. For questions 2, 4, and 6: 5. Strongly Disagree, 4. Disagree, 30 Neutral, 2. Agree, 1. Strongly Agree. BRS scores are interpreted as follows: Low resilience with scores between 1.00-2.99; Normal resilience with scores between 3.00 - 4.30, and High resilience with scores between 4.31 - 5.00. The responses varying from 1-5 are then summed for all six items giving a range from 6-30. To allow for equal weights, each respondent’s total sum is divided by the total number of questions answered.

Significance/Impact

It is important that universities recognize the importance of resilience and begin to invest in research and services aimed at building student resilience.

Challenges

The main challenge of the research so far is the size of data form PVAMU and TAMU. The collected sample is currently small and we will need more data to be collected from more respondents in both colleges. (The team is seeking more funds that will be used to pay more respondents and this can be done over Summer-2022 semester)

Key References

De la Fuente J, Fernández-Cabezas M, Cambil M, Vera MM, González-TorresMC, Artuch-Garde R. Linear relationship between resilience, learning approaches, and coping strategies to predict achievement in undergraduate students. Front Psychol. (2017) 8:1039. doi:10.3389/fpsyg.2017.01039

Windle1 G., K. M. Bennett, J. Noyes, (2011) A methodological review of resilience measurement scales, Health and Quality of Life Outcomes 9, 8 (2011). https://doi.org/10.1186/1477-7525-9-8

Hou, J., Yu, Q., and Lan, X. (2020). COVID-19 infection risk and depressive symptoms among young adults during quarantine: the moderating role of grit and social support. Front. Psychol. 11:577942. doi: 10.3389/ fpsyg.2020.577942

Kim, E., & Hargrove, D. T. (2013). Deficient or resilient: A critical review of Black male academic success and persistence in higher education. Journal of Negro Education, 82(3), 300–311. Labrague, L. J., De los Santos, J. A. A., and Falguera, C. (2020). Social and emotional loneliness among college students during the COVID-19 pandemic: the predictive role of coping behaviors, social support, and personal resilience. Perspective Psychiatry Care. doi: 10.21203/rs.3.rs-93878/v1

Hernandez AL, González-Escobar S, González NI, López-Fuentes A, Barcelata BE. (2019). Stress, self-efficacy, academic achievement and resilience in emerging adults. Electron J Res Educ Psychol. (2019) 17:129–48. doi: 10.25115/ejrep.v17i47.2226

Yeager D. S. and C. S. Dweck (2012) Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed, Educational Psychologist, 47:4, 302-314, DOI: 10.1080/00461520.2012.722805

Catherine Eckel Ph.D. Texas A&M University

Kalena Cortez, Ph.D. Texas A&M University

Student engagement

Gloria M. Rose

Assistant Professor, Graduate Program Director, and Family Nurse Practitioner Coordinator, College of Nursing

Introduction:

Severe morbidity and mortality in the postpartum period is largely due to hemorrhage, hypertension, and depression. Postpartum women are advised to visit their obstetrical care provider within the first six weeks after delivery to assess for potential complications and provide health guidance. The researchers believe provider-initiated health encounters before six weeks postpartum are vital to the early identification and mitigation of potential complications that increase the risk of maternal mortality.

The proposed study will engage Prairie View A&M University and Texas A&M University students enrolled in the Family Nurse Practitioner (FNP) program to conduct virtual home visits with high-risk women during the first and third postpartum weeks. During virtual visits, FNP students will obtain a health history, administer a psychosocial assessment, and ask questions about physiological recovery and adaptation. If FNP students identify abnormal conditions in the patients, they will refer patients to their obstetrical provider for follow-up and treatment. Patient participation and satisfaction with the program and FNP student satisfaction will be assessed as feasibility outcomes. Patient outcomes will include postpartum complications identified, referrals, patient follow-through on referrals, and resolution of complications.

Methods, Results, and Discussion:

Researchers will utilize telehealth technology and care coordination with Obstetrical providers to test the study hypothesis that virtual home visitation of postpartum women by FNP students within the first six weeks after delivery will facilitate early identification and treatment for postpartum hemorrhage, hypertension, depression, and minimize the incidence of severe maternal morbidity that could lead to mortality. Inclusion criteria for potential participants will consist of the following: 1) women within 1 and 3 weeks postpartum, 2) vaginal or cesarean delivery, 3) singleton or multiple births, 4) primiparous or multiparous. In order to determine the feasibility of the intervention the proposed study will not establish any exclusion criterion. Based on the current rate of high-risk patients in the targeted clinics, we anticipate a sample size of 56 patients. Students will approach potential participants during their 36 plus weeks prenatal visit in the clinic and provide a flyer and detailed description of the proposed intervention. Participants will be contacted via text by an FNP student from PVAMU approximately one week after delivery. In the initial text message, FNP

Nursing in the Puerperium: A Virtual Home Visiting Intervention for women at risk for severe maternal morbidity and mortality

students will identify themselves and use an infographic to provide a summary of what the visit will entail. The initial text message will explain the benefits of early assessment, inform the potential participant that any data gathered will be communicated with their obstetrical provider. Participants will be given an option to have a virtual or phone meeting with the FNP student.

Upon completion of the virtual visit, participants will be notified that the FNP student will reach out to them again in two weeks via text message. Participants will be advised to contact their obstetrical provider if any issues arise between encounters. The results of the encounter will be documented and reported to the participant’s identified obstetrical provider. The intervention will be completed within a 4-to-6-week period.

PVAMU Progress:

The 4 students that are involved in the project have completed a total of 31 visits of the 56 required. Based on the questionnaire, the results and data analysis are pending. Students are continuing to recruit more participants for the study. We expect to complete data collection by August 2022.

TAMU Progress:

The 3 students that are involved in the project have completed a total of 10 televisits of the 56 required. 5 of those participants have completed the questionnaire and data analysis is pending completion of enrollment. We have hired a new Student Research Assistant who will begin recruiting new participants at the start of the Summer semester (May 31). We have also added 2 new FNP students to the project since the prior students graduated in May. We expect to complete data collection by November/December 2022.

Stacy Sam, Ph.D., RN, MSN, MCH Prairie View A&M University

Robin L. Page

Texas A&M University

G. Weston

A&M University