Digital Commons Network^™

Multi-Risk Governance Of Solar Radiation Modification, Jonathan B. Wiener, Tyler Felgenhauer, Mark E. Borsuk

Faculty Scholarship

Solar radiation modification (SRM) presents important challenges to risk regulation and governance, arising from the array of multiple risks that SRM may influence. SRM would not simply reverse climate change, but could pose further ancillary impacts, depending on the method of SRM, such as stratospheric aerosol injection (SAI), marine cloud brightening (MCB), or a space-based planetary sunshade system (PSS). We identify multiple risks that SRM may influence, both biophysical and sociopolitical, to be compared to the multiple risks that may be affected by greenhouse gas (GHG) mitigation and climate adaptation. This multi-risk framework helps analysts and decision makers identify, evaluate, …

Recent Changes In Discretionary Denials Of Drug Patent Challenges, S. Sean Tu, Arti K. Rai, Aaron S. Kesselheim

Faculty Scholarship

Recent policy shifts at the U.S. Patent and Trademark Office (USPTO) have sharply limited the use of two administrative pathways for patent reviews, inter partes review (IPR) and post-grant review (PGR). Congress created these administrative pathways to provide a faster and less costly way to challenge weak patents. Recently, the USPTO has expanded the use of “discretionary denials,” invoking a new “settled expectations” rationale that blocks IPR petitions for patents more than about six years old. From May to September 2025, 60% of 506 requests for discretionary denial were granted, triple historical levels, including one-third involving drug patents. These changes …

Disparities In Maternal Healthcare: Examining The Impact Of Social Determinants Of Health On Provider Care Quality, Rishita Anumukonda

Honors Undergraduate Theses

Poor maternal health outcomes among racially and socioeconomically marginalized populations are a major public health concern in the United States. Effective clinician discussions during prenatal and postpartum care play an essential role in promoting maternal and infant well-being. However, the content of provider-patient discussions in maternal care and how it varies for patients exposed to different social determinants of health has not yet been well explored. This study aims to examine whether healthcare provider discussions about key maternal health topics, including preventative health tips, risk factors, and available resources, differ based on social factors such as income level and race. …

Rhythm Against Poethics, Jayvyn Dacas

Honors Undergraduate Theses

This thesis stages an auto-poetic ethnography that listens to, and moves with, the anterior spaces of Black life. Those murmurs, hums, musical hesitations, and infrastructural vibrations that refuse capture by dominant epistemic regimes. Drawing from Ramon Amaro’s reading of Sylvia Wynter, Katherine McKittrick and Alexander Weheliye’s reflections on sound, Fred Moten’s insights on displacement, and Tendayi Sithole’s attention to the phonographic, the project situates music production as method and unworlding. Through engagements with the 808, sampling, sound systems, and embodied listening across Florida. Spaces like the warehouse, the party, record stores, and community become the work of Black voices, and …

Incremental Innovation, George Horvath

Faculty Scholarship

Transformative innovations—the ones that use new technologies to disrupt the world—command our attention. But most new products are the result of a more mundane process of incremental iterative innovation, evolving through a long series of small modifications of existing technologies. Although both kinds of innovation can result in improved safety and utility, both can also create new dangers. We tend to be more aware of this in transformative innovations (as current worries over artificial intelligence show); by contrast, dangers created by incremental iterative innovation often go unrecognized, because the process itself is easy to overlook. Policymakers and regulators need to …

Aquatic Invasive Species Survey And Treatment On Lake Umatilla And Lake Celilo 2023-2024 Report, Garbiel E. Campbell, Jacob Rose

Center for Lakes and Reservoirs Publications and Presentations

Flowering Rush (Butomus umbellatus) is an invasive aquatic plant in the Pacific Northwest that threatens salmon habitat. The Center for Lakes and Reservoirs staff surveyed for this and other aquatic species from 2023 and 2024 in the Columbia River in Lake Umatilla and Lake Celilo. This document summarizes their survey efforts including their protocols, data, and small-scale removal efforts.

Protein Marker-Dependent Drug Discovery Targeting Breast Cancer Stem Cells, Ashley V. Huang, Yali Kong, Kan Wang, Milton L. Brown, David Mu

Department of Biomedical and Translational Sciences Faculty Publications

Breast cancer is one of the most common cancers globally. Unfortunately, many patients with breast cancer develop resistance to chemotherapy and tumor recurrence, which is primarily driven by breast cancer stem cells (BCSCs). BCSCs behave like stem cells and can self-renew and differentiate into mature tumor cells, enabling the cancer to regrow and metastasize. Key markers like CD44 and aldehyde dehydrogenase-1 (ALDH1), along with pathways like Wingless-related integration site (Wnt), Notch, and Hedgehog, are critical to regulating this stem-like behavior of BCSCs and, thus, are being investigated as targets for various new therapies. This review summarizes marker-dependent strategies for targeting …

New Family Law Statutes In 2024: Selected State Legislation, Family Law Quarterly 2024–25 Editors, New York Law School

Redefining Family Law: State Legislative Updates

This article provides summaries and context for 35 changes to family law that were enacted in 2024 by legislatures in 25 states and the District of Columbia. The topics include (1) Equal Protection, (2) Child Custody and Visitation, (3) Nonparent Custody and Visitation, (4) Child Welfare, (5) Domestic Violence, (6) Juvenile Justice, (7) Parentage, (8) Age of Marriage, (9) Companion Animals, (10) Education, (11) Criminal Justice, and (12) Provision of Legal Services. More specifically, the topics of the laws featured in this article include (among others) supervised visitation; increased protections for LGBTQ+ families; pet custody in divorce proceedings; and strengthened …

Business Communication And Editing Students’ Evaluations Of Written Error: An Eye-Tracking Study, Matt Baker, Grant Eck, Ana Barraza, Benjamin Duffield

Faculty Publications

Using eye-tracking and interview methods, this study investigates how business communication students and editing students attend to and evaluate writing. Participants reviewed blog posts embedded with errors and judged publication readiness. While both groups visually fixated longer on errors than non-errors, business communication students were more likely to approve error-containing texts for publication. Qualitative data revealed that business communication students prioritized content while editing students prioritized surface-level issues. These findings suggest that disciplinary background informs evaluative standards, even when error-detection behavior is similar. The results carry implications for instruction in business writing and editing, especially concerning collaborative, cross-disciplinary workplace writing.

Practical Experimental Microbiology: Laboratory Manual, Rivka Levron

Open Touro Created

2025

Microbiology, especially the laboratory component, is a highly practical and useful course for students pursuing many career options. While a number of excellent microbiology laboratory manuals are available, many are more suitable for a full-year course in microbiology or for more advanced studies.

Microbiology Experimental Laboratory Manual is comprehensive in teaching students basic microbiological techniques, i.e., how to grow and stain bacteria, how to identify bacteria and fungi, and means of identifying different species of bacteria microscopically. Further experiments enable students to identify the most effective soap/detergent from those readily available; to discover which spices have the most anti-bacterial …

Performance Of The Dssat Manihot-Cassava Model For Cassava Cultivation In The Recôncavo Baiano, Diego M. De Melo, Paola De F. Bongiovani, Fabio L.S. Costa, Patricia Moreno-Cadena, Alexandre B. Heinemann, Julian Ramirez-Villegas, Mauricio A. Coelho Filho

All Peer-Reviewed Publications

The aim of the present study was to calibrate the DSSAT MANIHOT-Cassava model with information from cassava varieties grown in the Recôncavo Baiano region, Bahia state, Brazil. The database used to calibrate the model was obtained in the dry sub-humid tropical climate in Cruz das Almas city, from 2019 to 2020. The model was calibrated with experimental data obtained under irrigated and rainfed conditions for the BRS Novo Horizonte and Eucalipto varieties. The calibration was carried out by adjusting parameters related to the characteristics of each variety. Model performance was evaluated with statistical indices that indicate the precision and accuracy …

An Assessment Of Vegetable Production Constraints, Trait Preferences And Willingness To Adopt Sustainable Intensification Options In Kenya And Uganda, Rose N. Okoma, Evanson R. Omuse, Daniel M. Mutyambai, Dennis Beesigamukama, Marius F. Murongo, Sevgan Subramanian, Frank Chidawanyika

All Peer-Reviewed Publications

Global food production systems are under pressure due to population increase, limited farmland, biotic and abiotic constrains, and ongoing climate change. Sustainable intensification is needed to increase agricultural productivity with minimal adverse environmental and social impacts. Vegetable-integrated push pull (VIPP) technology coupled with black soldier fly (BSF) frass offer such opportunities to smallholder farmers. However, farmers’ vegetable preferences and willingness to adopt these innovations remain unknown and are variable across various geographic scales. Focus group discussions (FGDs) and in-person interviews with smallholder farmers were conducted to assess vegetable production constraints and select vegetables to be integrated into VIPP coupled with …

Lrtm Left-Right Transition Matrices For Molecular Interaction Prediction, Kaitlin Zheng, Guihua Duan, Mengyun Yang, Wei Wu, Yao-Hang Li, Jianxin Wang

Computer Science Faculty Publications

Molecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models’ ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Foundation Models In Bioinformatics, Fei Guo, Renchu Guan, Yaohang Li, Qi Liu, Xiaowo Wang, Can Yang, Jianxin Wang

Computer Science Faculty Publications

With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities. A new era in computational biology has been ushered in by the application of FMs, focusing on both general and specific biological issues. In this review, we introduce recent advancements in bioinformatics FMs …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás

Computer Science Faculty Publications

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu

Computer Science Faculty Publications

Verifying scientific claims is challenging for the general public because most people lack domain knowledge. Manual verification by subject domain experts is accurate, but it is obviously not scalable to meet the rising number of scientific claims on the Web. Whether the emerging large language models and large reasoning models can be used for scientific claim verification, and how their performances compare to humans, are still research questions. To this end, we developed a new benchmark MSVEC2 that consists of 138 claims from credible fact verification websites and science news outlets. Two tasks were given to both human and LLM …

Improving The Performance Of Multi-Stakeholder Partnerships For Sustainable Development In Coastal Areas : Sweden (Hanö Bay) As A Case Study, Jennie Larsson

World Maritime University Ph.D. Dissertations

Coastal areas are vital for both ecosystems and human societies. Comprising diverse terrestrial, freshwater, and marine ecosystems, coastal areas provide us with essential resources and services. However, these areas are under threat from human activities and climate change, necessitating new governance structures to ensure their sustainable management and conservation. This research investigates how to improve the performance of local multi-stakeholder partnerships (M-SPs) in coastal areas, promoted as key mechanisms for achieving sustainable development goals. By drawing on stakeholder theory and using Pattberg & Widerberg’s (2014) analytical framework with nine building blocks for successful M-SPs as a foundation, the study examined …

Penanganan Kasus International Child Abduction Di Indonesia: Studi Kasus Penculikan Ezekiel Gionata Purba & Penculikan Enrico Johannes Susanto Carluen, Dhani Ershiano, Ari Wahyudi Hertanto, Benedetto Setyo Satrio Utomo

Jurnal Hukum & Pembangunan

Child protection, particularly in preventing and addressing cases of international child abduction, is an obligation of all states. This phenomenon frequently occurs in Indonesia when a foreign-national parent, following divorce, unilaterally takes their biological child abroad in violation of court-granted custody, guardianship, and/or access rights of the other parent. Currently, Indonesia lacks adequate legal provisions to prevent and resolve cases of international child abduction. Moreover, Indonesia has not yet acceded to the Hague Convention on the Civil Aspects of International Child Abduction 1980 (“the 1980 Hague Convention”). This convention establishes a legal mechanism to ensure the prompt return …

Conceptual Critical Success Factors Model On Infrastructure Sustainability Rating System For California Construction Projects, Joseph J. Kim, Jose A. Arroyo-Turcios

Mineta Transportation Institute

This report addresses the sensitivity and reliability of the sustainability rating systems by comparing each category’s verified scores with its respective submitted scores to evaluate how points are awarded for infrastructure projects, examine which categories present the most challenges for verification, and identify the best category for verification in the sustainability rating system. The authors conducted three analyses using credit score data obtained from fourteen actual civil infrastructure projects certified under Envision. First, the Natural World category had the highest average score from the submitted and verified data. Pairwise comparisons using t-tests indicate that the mean value of one category …

State Space Model Of Airflow In The Human Vocal Apparatus, Ian Howard

School of Engineering, Computing and Mathematics

To simulate both airflow and air pressure required for speech production, we developed a nonlinear state-space model of airflow in the human speech apparatus. We modeled the lungs as a mechanical, force-driven piston pump venting into a simplified larynx model, represented as a valve with time-varying resistance to airflow. The pump incorporates the effects of elasticity, viscosity, friction, and inertia, as well as differential air pressure. To maintain a constant target airflow through the larynx, a proportional-derivative (PD) controller applies force and regulates the piston. The model was implemented in MATLAB. Simulation results demonstrate that the model can maintain a …