Human Influence on the Climate System (Chapter 3)

The AR5 concluded that human influence on the climate system is clear, evident from increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. This chapter updates the assessment of human influence on the climate system for large-scale indicators of climate change, synthesizing information from paleo records, observations and climate models. It also provides the primary evaluation of large-scale indicators of climate change in this Report, complemented by fitness-for-purpose evaluation in subsequent chapters

An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

Citation: Shi, Z. Z., Chapes, S. K., Ben-Arieh, D., & Wu, C. H. (2016). An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. Plos One, 11(8), 39. doi:10.1371/journal.pone.0161131We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-a ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

The Association Among Receipt of Health Information, Body Mass Index, and Physical Activity and Dietary Behaviors in University Students

This presentation was given during the Georgia Southern University Phi Kappa Phi Honor Society Annual Research Symposium

The Association Among Receipt of Health Information, Body Mass Index, and Physical Activity and Dietary Behaviors in University Students

This presentation was given during the Georgia Southern University Annual Graduate Student Scholarship Day