Abstract

Pulmonary hypertension (PH) is a progressive and near fatal disease affecting the arteries in the lungs and the right ventricular size and function. Early diagnosis of PH can be challenging, as its symptoms overlap with those of many other disease conditions. Although PH is conventionally characterized as a disease restricted to the pulmonary vasculature, extra-pulmonary complications of PH, including anxiety, depression, and cognitive impairment, have been often reported. Understanding the links between PH and brain abnormalities may open up new avenues for more effective disease diagnosis and treatment. However, research in this area is currently scarce and often constrained by small sample sizes. In this presentation, I will discuss our recent cross-cohort study, which utilizes electronic health records (EHRs) and imaging data from PH patients to explore how PH may influence brain changes. Our findings indicate that these brain alterations are consistent across different cohorts and are specific to PH, underscoring their significance in understanding PH pathophysiology and their potential as new biomarkers.

Speaker Bio

Dr. Satoshi Okawa is an Assistant Professor of the Vascular Medicine Institute at the University of Pittsburgh School of Medicine, with affiliations to the Department of Computational & Systems Biology and McGowan Institute of Regenerative Medicine. He received his PhD in mass spectormetry-based proteomics from the European Molecular Biology Laboratory Heidelberg, Germany. He then performed his postdoctoral training in computational systems biology at the Luxembourg Centre for Systems Biomedicine, where he and his colleagues developed and applied computational systems biology tools for tackling biological problems. Since joining the University of Pittsburgh School of Medicine, his research has been focusing on large-scale population data analyses to elucidate disease mechanisms, particularly in cardiac diseases such as pulmonary hypertension, in collaboration with physician scientists at the institute.

Abstract

Like most crops, sorghum [Sorghum bicolor (L.) Moench] is susceptible to lodging due to severe mechanical forces generated by weather-related phenomena like wind and rain. However, in response to less severe mechanical stimulation, plants may exhibit alterations in growth and development known as thigmomorphogenesis that enhance the plant’s ability to withstand stronger forces. Understanding the mechanisms regulating thigmomorphogenesis may facilitate developing new varieties with greater lodging resistance. The current study investigated the effect of mechanical stimulation on the morphology, anatomy, biomechanical properties, transcriptome expression and endogenous hormones of sweet sorghum stems and if this effect was dependent on the developmental stage of the internodes or the duration of treatment. Mechanical stimulation led to reduced length and increased diameter of internodes, and younger internodes experienced a more pronounced reduction in length. Mechanically-stimulated internodes exhibited lower elastic modulus and flexural stiffness but higher strength (more flexible, stronger) compared to control internodes. Mechanical stimulation also appeared to increase internode vascular bundle size and density as well as the lignification level and a more noticeable increase in lignin content was observed in younger internodes. Transcriptome profiling of internode rind revealed that mechanical stimulation altered the expression of over 900 genes, including a large number of transcription factors and genes related to hormone signaling. The abundances of IAA, GA1 and ABA generally declined following mechanical stimulation, while JA was elevated. Weighted Gene Co-expression Network Analysis (WGCNA) identified three modules highly correlated with mechanical stimulation and morphological and biomechanical traits, which were enriched in pathways associated with cell wall biology, hormone signaling and general stress responses. Additionally, mechanical stimulation-triggered responses were found to be dependent on the developmental stage of the internode and the duration of stimulation. This study provides insights into the underlying mechanisms of plant hormone-regulated thigmomorphogenesis in sorghum stems. The findings from this study may offer opportunities to improve lodging resistance in sorghum and other crops.

Speaker Bio

Dr. Qing Li is a postdoctoral researcher in Dr. Tesfamichael Kebrom’s research group at the Cooperative Agricultural Research Center (CARC). She received her bachelor’s degree in Agricultural Resources and Environmental Science in 2017 at Jilin University, China, and completed her doctoral studies in Molecular & Environmental Plant Sciences at Texas A&M University in August 2023. For her doctoral research, she investigated the physiological and molecular mechanisms that regulate the response of sorghum stems to mechanical stimulation under the supervision of Dr. Scott Finlayson. Qing’s research passion lies in exploring the roles of environmental factors in regulating plant growth and development.

Abstract

Cell is the smallest living unit and makes up living organisms. In multicelluar organisms, cells communicate with each other to coordinate complex cellular functions. Hence, deciphering cell signaling is vital to understanding complex biological processes underlying multicellular organisms. Emergence of transcriptional profiling at single cell level (scRNAseq) has enabled the investigation of cellular processes at unprecedented level. It has also inspired the development of computational methods to utilize scRNAseq data to discover novel cell types, investigate gene regulation, and decipher cell-cell communication. More recent development of spatial transcriptomic profiling (ST) enabled the transcriptional profiling at single cell or close to single cell level together with spatial information. This talk will first briefly introduce single cell transcriptomics and spatial transcriptomics. A recent study of pulmonary hypertension (PAH) to identify differential pathways between PAH patients naive to treatment and with treatment using scRNAseq will be discussed. Secondly, a new computational approach utilizing ST to discover cell signaling with spatial coordination will be described.

Speaker Bio

Dr. Seungchan Kim is a Chief Scientist and Executive Professor at the Department of Electrical and Computer Engineering and the Director of the CRI Center for Computational Systems Biology at the Prairie View A&M University (PVAMU). Prior to this appointment, he was the Head of Biocomputing Unit and an Associate Professor at Integrated Cancer Genomics Division of Translational Genomics Research Institute (TGen). He was one of the founding faculty members of TGen, founded in 2002, by Dr. Trent, then-Scientific Director of the National Human Genome Research Institute at the National Institutes of Health, leading computational systems biology research at the institute. He was also an Assistant Professor in the School of Computing, Informatics, Decision Systems Engineering (CIDSE) at the Arizona State University from 2004 till 2011. Dr. Kim received B.S. and M.S. degrees in Agriculture Engineering from the Seoul National University, and Ph.D. in Electrical Engineering from the Texas A&M University. He also got his post-doctoral training at the Cancer Genetics Branch of National Human Genome Research Institute.

Dr. Kim’s research interests include: 1) mathematical modeling of genetic regulatory networks, 2) development of computational methods to analyze multitude of high throughput multi-omics data to identify disease biomarkers, and 3) computational models to diagnose patients or predict patient outcomes, for example, disease subtypes or drug response. His studies have had a large influence on the development of computational tools to study underlying mechanisms for cancer development and better understand the molecular mechanisms behind cancer biology and biological systems.

Abstract

Autonomous systems are systems that can accomplish a task, achieve a goal, or interact with their surroundings with minimal to no human involvement. Autonomous systems have quickly grown in popularity. Today, different facets of artificial intelligence are seamlessly integrated using a systems approach to ensure the effective functioning of autonomous systems. Such systems serve as excellent examples of leading-edge artificial intelligence-based applications. This talk introduces autonomous systems with a focus on smart sensors for environment perception, navigation and obstacle avoidance, mission and path planning, and autonomous systems motion control. Applications in healthcare, manufacturing, agriculture, energy, public service, security, and transportation will also be discussed.

Speaker Bio

Dr. Yong Wang is an accomplished researcher with a dual Ph.D. background in Industrial Engineering & Operations Research from the University of Illinois at Chicago (2015) and Energy & Power Engineering from Huazhong University of Science and Technology, China (2010). His academic journey includes a significant stage as a Visiting Scholar at the Department of Mechanical Engineering, University of Michigan, Ann Arbor (2007-2009). Presently, Dr. Wang serves as Associate Professor and Associate Chair of the Systems Science and Industrial Engineering Department within the Watson College of Engineering and Applied Science at Binghamton University, USA.

His research endeavors at Binghamton University are dedicated to the design, modeling, and management of various industrial systems, spanning energy, healthcare, manufacturing, and transportation sectors. Dr. Wang has published 42 peer-reviewed journal papers and 44 peer-reviewed conference papers in industrial and systems engineering. He has also contributed as an invited panelist for funding agencies and served as a reviewer for numerous prestigious journals and international conferences. At Binghamton University, Dr. Wang offers a diverse range of courses in industrial and systems engineering, computer programming, and engineering management, reflecting his multidisciplinary expertise and commitment to education.

Abstract

Predicting gene expression in DREAM Challenges 2022 is beneficial for gaining insights into gene function, biological pathways, and genes involved in regulating development, cell behavior, and signaling. In this study, a novel ensemble model that integrates “proformer” and ensemble learning techniques is proposed. The approach begins with preprocessing a large dataset of gene sequences provided by the DREAM Challenges 2022. Multiple “proformers” are then constructed, each capable of independently predicting gene expression. The resulting gene expression predictions from the “proformers” are combined through averaging weighted summation of individual prediction from each “proformer” to produce final predictions. Experimental results demonstrated that the proposed model is able to effectively enhance the prediction performance through comprehensive evaluation with various metrics, and even outperformed the winner in the DREAM Challenges 2022.

Speaker Bio

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and CREDIT, and Assistant Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received B.S. degree in computer science and technique at Harbin University of Science and Technology, M.S. degree in computer software and theory at Harbin Engineering University, and Ph.D. in computer application at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.