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A Computational Respiration Factor to Detect Abnormal Respiratory Patterns Using a Hydraulic Bed Sensor for Older Adults Aging-in-place

Hydraulic bed sensors are efficient non-wearable, passive sensors for unobtrusive and continuous collection of health data for older adults aging-in-place. Continuous collection of data can speak volumes about onset and development of a disease much before it is diagnosed and that is our vision through this work. Hydraulic bed sensor data yields signal from which

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Dissertation Defense: DESIGN AND DEVELOPMENT OF GEOSPATIAL ANALYTICAL RESEARCH KNOWLEDGEBASE (GeoARK)

A consistent finding across health, social, business, and environmental literature is that location matters. To conduct impactful research that can be applied to real-world issues and problems, the research must be grounded within the context of the real world in both place and culture. Significant differences exist and can vary across scales from blocks to

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MUIDSI Online Data Science and Analytics Program Ranked #7 in Fortune Magazine

The MS Data Science and Analytics (DSA) Program of the MU Institute for Data Science and Informatics (MUIDSI) was  recently ranked the 7th best online master’s degree in Data Science Programs in 2022 by Fortune magazine. The rankings were composed of a selectivity score and a demand score. The selectivity score made up 85% of the ranking which

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Harnessing the power of AI to advance knowledge of Type 1 diabetes

An interdisciplinary team of researchers from the University of Missouri, Children’s Mercy Kansas City and Texas Children’s Hospital has used a new data-driven approach to learn more about persons with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The team gathered its information through health informatics and applied artificial intelligence (AI)

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Dissertation Defense – UNDERSTANDING GENOME COMPOSITION OF EUSOCIAL HYMENOPTERAN INSECTS

Genome sequencing of the Western honey bee (Apis mellifera), a model for the biology and evolution of eusocial behavior, has revealed unusual genome compositional characteristics, including a low but heterogeneous GC content, bimodal GC content distribution, and a biased tendency of genes to be located in low GC regions. In this dissertation, we sought to

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Comprehensive Exam: Accelerating Data-Driven Discovery In Type 1 Diabetes:An Informatics-Based Approach

Type 1 diabetes (T1D) is an immune-associated or immune-mediated chronic disease characterized by the progressive failure or targeted destruction of insulin-producing beta (β) cells in the pancreas. Management of the disease is challenging, involving lifelong exogeneous insulin replacement and 24/7 blood glucose monitoring. Although more than 1.6 million Americans are living with T1D, most diabetes research is

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Development of AI Models for Remote Sensing City Fitness

Tracking a city’s fitness is particularly important for the continued urbanization of civilization. Urban environmental and air quality and overall fitness are decreasing due to natural and anthropogenic events, causing degradation of living quality and leading to various population health issues, including heart and lung problems and even premature death. City fitness monitoring is mostly

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Tool development for resolving and visualizing irregular heartbeats measured by cardiac magnetic resonance

Atrial fibrillation (AFib) is the most common irregularity of heartbeats. it can cause significant symptoms and impair heart function and daily life. Its irregular and often very rapid heart rhythm can lead to blood clots that cause stroke or heart attack, especially as the patient ages. The irregularity of heartbeats has prevented visualization of the

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Single-cell immune profiling of Q fever vaccination in mice

Human Q fever is a worldwide zoonotic disease caused by intracellular Gram-negative bacterium, Coxiella burnetii. Human Q fever has high infectious nature. However, there is no FDA approved vaccine in the US. Previous studies showed that after serial passages in eggs and tissue cultures, C. burnetiid undergoes a lipopolysaccharide (LPS) phase variation in which its

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Identification of biomarkers and therapeutic combinations for immune checkpoint inhibitors (ICIs) using explanatory subgroup discovery for cancer patients without EGFR mutation

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients’ heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches to treat cancer. However, 50% of cancer patients that are eligible for treatment with ICIs will not respond well to this kind of therapies. Over the years, multiple patient

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