Abstract

Gut microbiota-derived metabolites act as vital signaling molecules that link the intestinal ecosystem to host energy balance, inflammation, and metabolic health. Diet-induced dysbiosis, typically triggered by high-fat or high-sugar regimens, alters these metabolite profiles and drives the progression of metabolic disorders, including obesity, type 2 diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). Among these, the tryptophan metabolite indole and its derivatives have emerged as critical players in metabolic disease pathophysiology. While clinical and preclinical models demonstrate that indole levels are severely depleted during obesity and excessive hepatic fat deposition, exogenous supplementation effectively alleviates MASLD phenotypes in mice. Mechanistically, indole stimulates myeloid PFKFB3 activity, which is essential for suppressing macrophage activation and subsequently mitigating hepatic metabolic dysfunction and inflammatory cascades. Furthermore, indole attenuates diet-induced intestinal inflammation, a key mechanism contributing to its protective effects against metabolic dysfunction-associated steatohepatitis (MASH). Consequently, dietary and pharmacological strategies designed to elevate endogenous indole production represent highly viable therapeutic avenues for managing metabolic disease.

Speaker Bio

Dr. Chaodong Wu is a tenured Professor in the Department of Nutrition, Faculty Fellow and Senior Faculty Fellow of Texas A&M AgriLife Research, and Presidential Impact Fellow at Texas A&M University (TAMU). Dr. Wu’s research focuses on elucidating the pathogenesis of obesity and related metabolic diseases. Since 2010, Dr. Wu has secured external funding exceeding $12.49 million, including six NIH R01 grants. Dr. Wu has published 125 peer-reviewed articles, including those in top-ranked journals (Cell Metabolism, Circulation, Hepatology, and Gastroenterology), with an h-index of 50 and an i10-index of 123.

Since 2007, Dr. Wu has mentored and trained 69 graduate students, supervised 6 visiting PhD students and 8 postdoctoral associates. Since 2010, Dr. Wu has served on over 38 NIH study sections as an ad hoc reviewer and played a significant leadership role as a Standing Member and then as Chair (2020–2023) for NIH’s Hepatobiliary Pathophysiology (HCCS) study section. To date, Dr. Wu has delivered 99 invited seminar presentations.

Abstract

The proximal small intestine plays a central role in carbohydrate digestion, where complex dietary polysaccharides are enzymatically hydrolyzed into glucose and intermediate saccharides prior to absorption or further transformation. In this study, a comprehensive numerical model is developed to simulate glucose formation from digestible carbohydrates and its subsequent transformation into non-digestive oligosaccharides within the proximal small intestine. The model, which depicts the proximal small intestinal (PSI) digestion as a series of continuous stirred-tank reactors, integrates enzymatic kinetics, luminal transport, absorption, and convective flow to describe the temporal evolution of carbohydrate species along the intestinal segment. A system of coupled nonlinear ordinary differential equations is formulated to represent starch hydrolysis, brush-border enzyme activity, microbial and exogenous enzyme-mediated transglycosylation, and competitive absorption processes. Michaelis–Menten kinetics are employed to characterize enzyme-catalyzed reactions, while the intraluminal convective–transport equations describe substrate movement along the intestinal lumen. Numerical simulations are performed using finite difference methods under physiologically relevant boundary conditions. Model results demonstrate that glucose concentration profiles are highly sensitive to enzyme activity levels, transit time, and luminal pH. Furthermore, the formation of non-digestive oligosaccharides is shown to depend strongly on substrate availability and microbial enzymatic activity, suggesting a dynamic balance between absorption and conversion pathways. Sensitivity analyses identify key parameters governing glucose bioavailability and oligosaccharide yield, providing insight into dietary modulation strategies. The proposed numerical framework offers a predictive tool for understanding carbohydrate digestion dynamics and supports the design of functional foods aimed at enhancing non-digestive oligosaccharide production in the upper gastrointestinal tract.

Speaker Bio

Dr. Kazeem Olanrewaju is an Associate Professor in the Department of Chemical Engineering at Prairie View A&M University (PVAMU), where he has served in teaching and research roles since 2014. He earned his Ph.D. in Chemical and Biochemical Engineering from the University of Iowa in 2012. His more than 15 years of experience in the chemical engineering profession across both the public and private sectors includes positions as Chemical/Cement Laboratory Engineer at the State Highway Administration, Maryland Department of Transportation, Associate Consultant at ChemProcess Technology (CPT) LLC., and Process Safety and Risk Analyst Co-op at Akzo Nobel Functional Chemical in Stenungsund, Sweden. He also holds master’s degrees in Chemical and Biochemical Engineering from the University of Iowa and Environmentally Sustainable Process Technology from Chalmers University of Technology, Sweden, and a bachelor’s degree in Chemical Engineering from Obafemi Awolowo University, Nigeria.

Dr. Olanrewaju’s research integrates chemical engineering principles, computational modeling, and machine learning to address challenges in human physiology and sustainable energy. His interests include multiscale modeling of species transport and transformation in biological systems, fluid–structure interactions, biorenewable and supercritical-fluid reactions, and applications of machine learning to physiological and energy systems data. His scholarly work spans biomedical transport, neurodegenerative disease assessment, cellulose conversion, and carbon dioxide capture. He serves as principal investigator on an NIH-funded project examining glucose transglycosylation and optimization in the proximal small intestine, and as co-principal investigator on projects supporting mixed reality in engineering education and the Gulf Scholars Program. His teaching encompasses thermodynamics, transport phenomena, bioreaction in human physiology, process modeling, and biomedical engineering, with an emphasis on student mentorship and computational tools. His honors include the 2021 AIChE Minority Affairs Committee’s 30th Anniversary James Wei Award and Distinguished Service Awards.

Abstract

The rapid advancement of AI has driven growing demand for specialized AI hardware, while the increasing complexity of modern integrated circuits poses substantial challenges to design productivity, functional verification, and power, performance, and area (PPA) optimization. Emerging AI technologies, including large language models (LLMs), reinforcement learning (RL), graph neural networks (GNNs), and autonomous AI agents, present new opportunities to advance electronic design automation (EDA) toward more intelligent and adaptive design methodologies. This seminar will provide an overview of the chip design lifecycle, spanning system architecture, register-transfer level (RTL) design, functional verification, physical implementation, and tapeout, with an emphasis on AI-enabled automation. Three research directions will be highlighted: (1) AI-assisted RTL generation and functional verification, leveraging LLMs and intelligent agents to facilitate hardware description generation, coverage-driven verification, and state-space exploration; (2) AI-driven physical design optimization, employing RL and GNNs to enhance floorplanning and placement; and (3) AI-guided design-space exploration, integrating predictive modeling and intelligent search strategies to optimize PPA trade-offs. Furthermore, the seminar will discuss the potential of agentic AI frameworks to orchestrate EDA tools and enable iterative, feedback-driven chip design workflows.

Speaker Bio

Dr. Lujun Zhai is a Research Assistant Professor in the Department of Electrical and Computer Engineering and the Center for Computational Systems Biology (CCSB) at Prairie View A&M University (PVAMU). Her research focuses on artificial intelligence, computer vision, hardware acceleration, and resource-efficient computing systems. Her work integrates deep learning, hardware-aware optimization, and field-programmable gate array (FPGA) technologies to develop efficient AI solutions for resource-constrained and embedded computing platforms. Her research also explores AI applications to electronic design automation (EDA), including intelligent chip design, functional verification, and design optimization.

Abstract

Orthorexia nervosa (ON) is a pattern of disordered eating marked by an obsessive preoccupation with healthy eating, which can lead to rigid dietary restriction and significant impairment. Difficulties in executive functioning, the higher-order cognitive processes that guide goal-directed behavior, have been implicated in eating disorders, and ON in particular has been linked to deficits in cognitive flexibility, a core component of executive function. However, evidence for this relationship has been inconsistent, and the role of distress tolerance has not been previously examined. This presentation will highlight a series of three cross-sectional studies investigating how cognitive flexibility and distress tolerance relate to ON symptoms. The presentation will describe findings from both self-report measures (the Cognitive Flexibility Inventory) and performance-based measures of executive function (the Wisconsin Card Sorting Test), showing that specific dimensions of flexibility, rather than flexibility as a whole, are differentially associated with ON symptoms. Particular emphasis will be placed on distress tolerance, which emerged as a potential risk and maintenance factor: individuals with lower distress tolerance reported higher levels of ON symptoms. Implications for theory, research, and clinical practice will be discussed, along with directions for future research, including combining self-report and performance-based assessments of executive function and examining these relationships in clinical populations with ON.

Speaker Bio

Dr. Niki Hayatbini is a postdoctoral fellow in the T32 AI/ML and Precision Nutrition program (AIPrN) at Prairie View A&M University. Her interdisciplinary research bridges cognitive psychology, machine learning, and nutrition science, integrating behavioral science with AI/ML methods to understand how cognitive, behavioral, and nutritional factors shape physical and mental health. She is also interested in the human-centered evaluation of AI systems in health contexts.

Dr. Hayatbini earned her Ph.D. in Cognitive Psychology, where her work focused on executive functions, particularly cognitive flexibility and its role in mental health and disordered eating. She also holds a Master’s degree in Clinical Psychology. Prior to joining Prairie View A&M University, she was a postdoctoral researcher in the School of Nursing and Health Studies at the University of Washington, where she studied K–12 behavioral and mental health.

Abstract

Plants are essential components of our ecosystem and play a vital role in sustaining life on earth. Recent climate changes pose significant challenges to agricultural productivity by exposing crops to increasing biotic and abiotic stresses. Developing resilient crops requires an integrated understanding of plant development, genetics, and physiology, coupled with innovative technologies for crop improvement. This talk will highlight my research on plant genome engineering, peptide-mediated immune responses, root development, and stress tolerance. It will also introduce my current research investigating the genetic and developmental mechanisms regulating tillering in drought-tolerant, stay-green sorghum. By integrating molecular biology, functional genomics, and plant physiology, my research aims to identify strategies for improving crop traits and advancing sustainable agriculture under changing climatic conditions.

Speaker Bio

Dr. Sowmya Poosapati is a Research Specialist in Dr. Tesfamichael Kebrom’s laboratory at the Cooperative Agricultural Research Center (CARC), Prairie View A&M University (PVAMU), where she studies the genetic and developmental mechanisms underlying shoot branching in crops. Originally from Andhra Pradesh, India, she earned her Ph.D. in Biotechnology from Jawaharlal Nehru Technological University, India. Her doctoral research identified thermotolerant Trichoderma strains with enhanced biocontrol potential against plant pathogens, which were subsequently commercialized for agricultural use in India.

Dr. Poosapati spent over five years at DuPont India Ltd., receiving multiple awards for her contributions to genome editing and cloning projects. She was later selected as a Tata Institute of Genetics and Society (TIGS) Visiting Scholar, working with Professor Martin Yanofsky at the University of California, San Diego (UCSD), to develop genome-editing tools for rice.

She subsequently conducted postdoctoral research in Professor Alisa Huffaker’s laboratory at UCSD, investigating plant innate immune responses in Arabidopsis and legumes. At the Salk Institute for Biological Studies, she worked with Professor Joanne Chory to develop strategies for improving root architecture, particularly deeper and denser root systems, to enhance plant performance and contribute to climate change mitigation. Her research spans plant molecular biology, genome engineering, plant immunity, root development, and crop resilience.