Physical Sciences and Mathematics Commons^™

Bloom: Behavioral Learning And Outcome Observation In Microbes, Sean Sarwar Haque, Luke Compton Wharton, Ming Lin, Razvan Voicu

Symposium of Student Scholars

Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected …

02.24.2025 Ored Connect, Liz Williamson

ORED Newsletter

Collaborate Opportunity

SEC Faculty Travel Grants

Participant Support Cost Resources

Stabilization Of Argan Oil Nanoemulsions Using Chitosan Extracted From Pink Shrimp Shells, Yousra Mdarhri, Ikram Bouziane, Ilyace Korodowou, Narjisse Mokhtari, Lama Rissouli, Fakhita Touhami, Mohamed Chabbi, Ahmed Touhami

Physics & Astronomy Faculty Publications

This study investigates the use of chitosan, extracted from pink shrimp shells, as a stabilizer for argan oil nanoemulsions. Chitosan was obtained through demineralization, deproteination, bleaching, deacetylation, and purification, then characterized through potentiometric titration, viscometry, X-ray diffraction (XRD), scanning electron microscopy (SEM), and purity assessments. The resulting chitosan was used to stabilize nanoemulsions formulated with extra-virgin argan oil and a nonionic surfactant using the phase inversion composition (PIC) method. The stability of the nanoemulsion was assessed through light scattering, zeta potential, viscosity, and pH measurements. The extracted chitosan exhibited high purity, a deacetylation degree of 88.34%, a molecular weight of …

Clinician Experiences With Ambient Scribe Technology To Assist With Documentation Burden And Efficiency, Matthew J. Duggan, Julietta Gervase, Anna Schoenbaum, William Hanson, John T. Howell, Michael Sheinberg, Kevin B. Johnson

SKMC Student Presentations and Publications

IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.

OBJECTIVE: To investigate the association of ambient scribing technology with efficiency, quality, and perceived burden of clinical documentation in the outpatient setting.

DESIGN, SETTING, AND PARTICIPANTS: This prospective, single-group pre-post quality improvement study was conducted between April and June 2024 in the outpatient setting of an academic health system in Philadelphia, Pennsylvania. Participants included physicians, nurse practitioners, and physician assistants. Data were analyzed from July to August 2024.

EXPOSURE: Access to an artificial intelligence-driven ambient scribing tool …

Investigating The Relationship Between The Circadian Clock, Age, And Lifestyle, Samuel Park, Amaka Eziakonwa, Amega Nguyen, Madison Payne, Erich Stahmer, Alexis Salinas, Payson Broome, Angel Oliver, Christian Mccormick, Jasmyn Brown, Julie A. Zacharias-Simpson, Jutta Ward, Deborah Bell-Pedersen

Annual Research Symposium

This poster presentation aims to understand how circadian clock strength and phase are affected by age and lifestyle. By analyzing changes in physiological rhythms, the study seeks to develop strategies for promoting healthy aging by optimizing circadian function.

Tracking The Effects Of Time Zone Shift On An Individual’S Circadian Rhythm, Amaka Eziakonwa, Erich Stahmer, Amega Nguyen, Samuel Park, Madison Payne, Alexis Salinas, Deborah Bell-Pedersen, Julie A. Zacharias-Simpson

Annual Research Symposium

This poster presentation aims to understand how a rapid time-zone change can affect an individual’s circadian rhythms. By quantifying changes in physiological rhythms, the study seeks to develop targeted therapeutic interventions to mitigate the negative effects of jetlag.

Can We Eliminate The Negative Consequences Of Circadian Desynchronization Caused By Night Shift Work By Using A Light Therapy Lamp To Simulate A Sunset?, Amega Nguyen, Madison Payne, Amaka Eziakonwa, Erich Stahmer, Alexis Salinas, Samuel Park, Julie A. Zacharias-Simpson, Deborah Bell-Pedersen

Annual Research Symposium

This poster aims to understand the relationship between desynchronosis of circadian rhythms and night-shift work and how light therapy can mitigate some of the issues of desynchronosis. This study seeks to develop therapies for disrupted circadian rhythms from night-ship works.

02.17.2025 Ored Connect, Liz Williamson

ORED Newsletter

SOP Research Day

Self Supervised Artificial Intelligence Predicts Poor Outcome From Primary Cutaneous Squamous Cell Carcinoma At Diagnosis, Nicolas Coudray, Michelle C. Juarez, Maressa C. Criscito, Adalberto Claudio Quiros, Reason Wilken, Stephanie R. Jackson Cullison, Mary L. Stevenson, Nicole A. Doudican, Ke Yuan, Jamie D. Aquino, Daniel M. Klufas, Jeffrey P. North, Siegrid S. Yu, Fadi Murad, Emily Ruiz, Chrysalyne D. Schmults, Cristian D. Cardona Machado, Javier Cañueto, Anirudh Choudhary, Alysia N. Hughes, Alyssa Stockard, Zachary Leibovit-Reiben, Aaron R. Mangold, Aristotelis Tsirigos, John A. Carucci

Department of Dermatology and Cutaneous Biology Faculty Papers

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of …

Proxy Panels Enable Privacy-Aware Outsourcing Of Genotype Imputation, Degui Zhi, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

One of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and in cases when genomic data are outsourced to perform analysis tasks, for example, genotype imputation services and federated collaborations genomic analysis. Although numerous cryptographic methods have been developed, these methods may not yet be practical for population-scale tasks in terms of computational requirements, rely on high-level expertise in security, and require each algorithm to be implemented from scratch. In this study, we focus on outsourcing of genotype imputation, a fundamental task that utilizes population-level reference panels, and develop protocols that rely on using …

Mutational Scanning And Binding Free Energy Computations Of The Sars-Cov-2 Spike Complexes With Distinct Groups Of Neutralizing Antibodies: Energetic Drivers Of Convergent Evolution Of Binding Affinity And Immune Escape Hotspots, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Nishank Raisinghani, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The rapid evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. In this study, we conducted a comprehensive structural and energetic analysis of SARS-CoV-2 spike receptor-binding domain (RBD) complexes with neutralizing antibodies from four distinct groups (A–D), including group A LY-CoV016, group B AZD8895 and REGN10933, group C LY-CoV555, and group D antibodies AZD1061, REGN10987, and LY-CoV1404. Using coarse-grained simplified simulation models, rapid energy-based mutational scanning, and rigorous MM-GBSA binding free energy calculations, we elucidated the molecular mechanisms of antibody binding and escape mechanisms, identified key …

02.10.2025 Ored Connect, Liz Williamson

ORED Newsletter

Community Engagement Recognition

Quantitative Characterization And Prediction Of The Binding Determinants And Immune Escape Hotspots For Groups Of Broadly Neutralizing Antibodies Against Omicron Variants: Atomistic Modeling Of The Sars-Cov-2 Spike Complexes With Antibodies, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Nishank Raisinghani, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

A growing body of experimental and computational studies suggests that the cross-neutralization antibody activity against Omicron variants may be driven by the balance and tradeoff between multiple energetic factors and interaction contributions of the evolving escape hotspots involved in antigenic drift and convergent evolution. However, the dynamic and energetic details quantifying the balance and contribution of these factors, particularly the balancing nature of specific interactions formed by antibodies with epitope residues, remain largely uncharacterized. In this study, we performed molecular dynamics simulations, an ensemble-based deep mutational scanning of SARS-CoV-2 spike residues, and binding free energy computations for two distinct groups …

Modeling Area Deprivation Index Using Non-Gaussian Fixed Rank Kriging, Edwin Kutsushi Mutimba

SDSU Data Science Symposium

The Area Deprivation Index (ADI) plays a vital role in identifying and addressing socioeconomic challenges, forming the basis for fair public health policies and resource distribution. This study explores the capabilities of both linear and non-linear statistical models, with a focus on Fixed Rank Kriging (FRK), to analyze ADI using data on U.S. Social Determinants of Health (SDOH). The model’s predictive capability is compared with Generalized Additive Models (GAM) and Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The non-gaussian FRK provides a more comprehensive spatial analysis, albeit with higher computational requirements.

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Inclusive Space For Everyone Door Sign (Printable 8.5 X 11 Inch), Joan H. Robinson

Open Educational Resources

This is a printable 8.5 x 11 inch door sign for faculty, staff, and administrators to let people know it is an inclusive space for everyone. For improved visibility, print with a color printer and cut along the dotted line.

See also: 11 x 17 inch door sign.

Comparing In-Person, Standard Telehealth, And Remote Musculoskeletal Examination With A Novel Augmented Reality Exercise Game System: Pilot Cross-Sectional Comparison Study, Richard Wu, Keerthana Chakka, Sara Belko, Ninad Khargonkar, Kevin Desai, Balakrishnan Prabhakaran, Thiru Annaswamy

SKMC Student Presentations and Publications

BACKGROUND: Current telemedicine technologies are not fully optimized for conducting physical examinations. The Virtual Remote Tele-Physical Examination (VIRTEPEX) system, a novel proprietary technology platform using a Microsoft Kinect-based augmented reality game system to track motion and estimate force, has the potential to assist with conducting asynchronous, remote musculoskeletal examinations.

OBJECTIVE: This pilot study evaluated the feasibility of the VIRTEPEX system as a supplement to telehealth musculoskeletal strength assessments.

METHODS: In this cross-sectional pilot study, 12 study participants with upper extremity pain and/or weakness underwent strength evaluations for four upper extremity movements using in-person, telehealth, VIRTEPEX, and composite (telehealth plus VIRTEPEX) …

Classification Of Schizophrenia, Bipolar Disorder And Major Depressive Disorder With Comorbid Traits And Deep Learning Algorithms, Xiangning Chen, Yimei Lu, Joan Manuel Cue, Mira V Han, Vishwajit L Nimgaonkar, Daniel R Weinberger, Shizhong Han, Zhongming Zhao, Jingchun Chen

Faculty, Staff and Student Publications

Many psychiatric disorders share genetic liabilities, but whether these shared liabilities can be utilized to classify and differentiate psychiatric disorders remains unclear. In this study, we use polygenic risk scores (PRSs) of 42 traits comorbid with schizophrenia (SCZ), bipolar disorder (BIP), and major depressive disorder (MDD) to evaluate their utilities. We found that combining target specific PRS with PRSs of comorbid traits can improve the classification of the target disorders. Importantly, without inclusion of PRSs from targeted disorders, we can still classify SCZ (accuracy 0.710 ± 0.008, AUC 0.789 ± 0.011), BIP (accuracy 0.782 ± 0.006, AUC 0.852 ± 0.004), …

A Statistical Framework For Multi-Trait Rare Variant Analysis In Large-Scale Whole-Genome Sequencing Studies, Xihao Li, Han Chen, Margaret Sunitha Selvaraj, Eric Van Buren, Hufeng Zhou, Yuxuan Wang, Ryan Sun, Zachary R Mccaw, Zhi Yu, Min-Zhi Jiang, Daniel Dicorpo, Sheila M Gaynor, Rounak Dey, Donna K Arnett, Emelia J Benjamin, Joshua C Bis, John Blangero, Eric Boerwinkle, Donald W Bowden, Jennifer A Brody, Brian E Cade, April P Carson, Jenna C Carlson, Nathalie Chami, Yii-Der Ida Chen, Joanne E Curran, Paul S De Vries, Myriam Fornage, Nora Franceschini, Barry I Freedman, Charles Gu, Nancy L Heard-Costa, Jiang He, Lifang Hou, Yi-Jen Hung, Marguerite R Irvin, Robert C Kaplan, Sharon L R Kardia, Tanika N Kelly, Iain Konigsberg, Charles Kooperberg, Brian G Kral, Changwei Li, Yun Li, Honghuang Lin, Ching-Ti Liu, Ruth J F Loos, Michael C Mahaney, Lisa W Martin, Rasika A Mathias, Braxton D Mitchell, May E Montasser, Alanna C Morrison, Take Naseri, Kari E North, Nicholette D Palmer, Patricia A Peyser, Bruce M Psaty, Susan Redline, Alexander P Reiner, Stephen S Rich, Colleen M Sitlani, Jennifer A Smith, Kent D Taylor, Hemant K Tiwari, Ramachandran S Vasan, Satupa'itea Viali, Zhe Wang, Jennifer Wessel, Lisa R Yanek, Bing Yu, Nhlbi Trans-Omics For Precision Medicine (Topmed) Consortium, Josée Dupuis, James B Meigs, Paul L Auer, Laura M Raffield, Alisa K Manning, Kenneth M Rice, Jerome I Rotter, Gina M Peloso, Pradeep Natarajan, Zilin Li, Zhonghua Liu, Xihong Lin

Faculty, Staff and Student Publications

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple …

Effectiveness Of Postoperative Cephalosporins In Reducing Urinary Tract Infections And Other Parameters Following Transurethral Resection Of The Prostate: A Systematic Review And Meta-Analysis, Wael Hafez, Feras Al-Obeidat, Asrar Rashid, Arun Kumar Venkatachalapathi, Amr Massaod, Ziad Albaha, Samy Kishk, Tesfalidet Emoshe, Samuel Tesfaye Tefera, Ismail A. Ibrahim, Mohammad Alkammar, Gowhar Rashid, Ahmed Fayed, Karim Soliman, Abdulqadir J. Nashwan, Alaaldeen Mohamed, Daniel Simancas-Racines, Ivan Cherrez-Ojeda

All Works

No abstract provided.

Childhood Sexual Trauma, Substance Use, And Antiretroviral Therapy Adherence Among Older Adults Living With Hiv: A Mediational Analysis, Monique J. Brown, Josie Zhang, Prince Nii Ossah Addo, Amandeep Kaur, Daniel Amoatika, Elizabeth L. Crouch Ph.D., Steven A. Cohen

Faculty Publications

Childhood sexual abuse (CSA) is linked to substance use and lower antiretroviral therapy (ART) adherence. However, studies examining the mediational role of substance use between CSA and ART adherence are lacking. Therefore, the aim of this study was to determine the potential mediational role of substance use between CSA and ART adherence among older adults living with HIV (OALH) (n = 91). Mediation analyses assessed the direct and indirect relationships between CSA, substance use, and ART adherence. Statistically significant differences existed in substance use by gender (male vs. female: 12.3 vs. 9.97) and ART adherence by race (Black vs. …

Vaxbot-Hpv: A Gpt-Based Chatbot For Answering Hpv Vaccine-Related Questions, Yiming Li, Jianfu Li, Manqi Li, Evan Yu, Danniel Rhee, Muhammad Amith, Lu Tang, Lara S Savas, Licong Cui, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

METHODS: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and …

Ai-Integrated Cross-Disciplinary Liberal Arts Colloquium, Diana S. Perdue, Niloofar Gholamrezaei, Jennifer Krusinger, Shannon Hogan

Teaching Repository of AI-Infused Learning

What began organically as an AI-focused collaboration between faculty in different disciplines along with researchers in the college’s Center for Instructional Innovation, has evolved into college-wide pedagogical workshops and the development of a studio model that affects courses, programs, and paradigms for faculty scholarship.

Lessons from these collaborations inform understandings of how key features of liberal arts education can be supported through intentional AI-integration within a Cross-Disciplinary Liberal Arts Colloquium. More specifically, this colloquium offers recommendations on adapting a collaborative, interdisciplinary, and reflective studio model to in-person, online, and hybrid educational environments facilitated through a Technological Pedagogical Content Knowledge (TPACK) …

Uncovering Acoustic Biomarkers To Classify Parkinson Disease Through Machine Learning, Felix Yeboah

Data Science and Data Mining

The early detection of diseases profoundly influences treatment efficacy, and accurate classification methodologies are essential for effective disease identification. In this project, we examined fve different classifers—Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), and Support Vector Machines—and evaluated their performance in detecting Parkinson’s disease (PD) based on voice features. The study aims to identify the best classifier for detecting PD. XGBoost performed the best, with an accuracy of 91% on the full dataset. After variable selection, KNN had the best performance with an accuracy of 91%. These findings suggest that Machine learning algorithms(classifiers) can …

Deep Learning-Based Auto-Segmentation For Liver Yttrium-90 Selective Internal Radiation Therapy, Jun Li, Wookjin Choi, Rani Anne

Department of Radiation Oncology Faculty Papers

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, …

Challenges And Opportunities In The Dissemination Of Scientific Content: Insights From Media Professionals In Jordan, Ruba Mustafa Mohammad, Mahmoud Ahmad Al-Rajabi, Hala Nabil Amr

This study explored the challenges and opportunities in disseminating scientific content from the perspective of media experts in Jordan. Using a descriptive methodology and a questionnaire, the research aimed to understand the landscape better. The sample included 343 media experts holding master's degrees or higher in various media fields, along with several journalists actively working in the sector. The study revealed several key findings. Chief among them are the numerous obstacles hindering the dissemination of scientific content in Jordanian media. These include a lack of training for journalists on accessing new scientific information, a general absence of critical thinking skills …

The Degree Of Dependence Of The Jordanian Public On Social Media In Health Awareness Of Obesity Diseases (Survey Study), Jumana Irshiedat, Kamel Khurshid Murad

The article aimed to reveal the dependency of social media platforms in educating the Jordanian public about obesity diseases and explore the cognitive, emotional, and behavioral effects on the Jordanian public. The survey method was adopted; an approach used in descriptive studies, and the questionnaire tool was used to a sample (357) of the Jordanian public in Amman. The study-produced results including the platform most used by the Jordanian public to receive knowledge about health awareness is WhatsApp in the first place, followed by Instagram and then Facebook. The results also showed that the health topics of most interest to …

The Energy Transition In The Jordanian Media, Hanan Al-Shiekh

The study aimed to clarify the role of scientific journalism towards stimulating investment in environmentally friendly renewable energy sources, which is one of the necessary challenges facing the Kingdom of Jordan for economic growth and social development, due to its heavy dependence on energy imports, which reflects negatively on the Jordanian economy. This study is one of the descriptive studies that focuses on describing a specific topic, as it is in the current reality, in terms of its general and detailed characteristics, including variables, elements, relationships, and influences, which are among the most prominent features of scientific journalism in terms …

The Role Of Television In Increasing Knowledge About Health Issues Among Jordanian Citizens, Izzat Mohammad Hijab, Ahmad Oreqat, Hani Al-Badri

This study explores the role of TV health programs in increasing the knowledge of Jordanian citizens about health issues. The study was based on the social survey methodology through a questionnaire distributed to a sample of 200 people from the city of Amman watching Jordan Television (JTV) health programs. The researcher concluded the following; a high watching for health programs, and that the Jordanians watch health programs with their family at home. The study also confirmed that the Jordanian benefits from health programs provided by JTV, and these programs are important and essential in educating citizens in health issues. Finally, …

01.27.2025 Ored Connect, Liz Williamson

ORED Newsletter

Data Science for Addiction Research

UM research and related activities as a new presidential administration transition occurs