<em>Identification of Spatially Variable Genes</em>

Identification of Spatially Variable Genes

Invasive species pose a unique threat to native species and habitat through direct and indirect competition of resources. Management of invasive species depends on precise identification of their current range and knowledge of how they spread. This project will utilize Plantescope Satellite Imagery to identify the presence of Callery Pear, an invasive ornamental tree species

Modeling Pyrus calleryana spread in central Missouri using remote sensing and a non-parametric modeling approach

<strong>Relationships between mortality and respiratory health, medication usage, water disappearance, and climatic conditions in commercial weaning-to-finishing pigs</strong>

Relationships between mortality and respiratory health, medication usage, water disappearance, and climatic conditions in commercial weaning-to-finishing pigs

Mortality in pigs is of paramount economic importance to United States pig producers. According to data compiled by MetaFarms SMS and Pork Checkoff in 2022, 21.2% of pigs die before reaching slaughter, and this statistic has increased, in general, over the last 5 years. Although only 6.8% of these losses are attributed to pigs in the

DeepVariant – TrioTrain: Developing a transfer learning protocol using non-human genomes

Genomic data are widely available for investigating phenotypes that impact both human and animal health. Although the investigation of human health often begins with model organisms, genomics technologies and software are often initially developed with only the human genome in mind, severely limiting their comparative applicability. Translational research requires robust systems-focused, or “One Health,” solutions

Exploratory analysis of the use of Telemedicine in Primary care

This research is primarily focused on use of Telemedicine in Primary care and how that usage changed over time especially COVID 19. In this research, we did a scoping review to see how Primary care adapted Telemedicine during COVID-19 and what are some of the successes or challenges with the adaptation. In this research, we

Identifying Gene-Gene Interactions Protective Against Autism Using Contrast Mining

Many genetic variants have been linked with the development of ASD. ASD is also known to be more prevalent in males than in females. The underlying mechanism for this difference is unclear. The polygenic nature of the genetic component of ASD makes studying potential mechanisms difficult if the significance of variants is assessed independently, as

Early Warning of Health Changes for Older Adults: Implementing a Gaussian Mixture Components Clustering Algorithm to Detect Outliers in Daily Multi-feature Sensor Data Streams

In this case study, we evaluate the implementation of Sequential Possibilistic Gaussian Mixture Models (SPGMM) for accurately modeling changes in feature streams antecedent to known health events, thereby providing predictive relevance for clinical use, including identifying the preprocessing requirements for streams prior to algorithm input. SPGMM is a change detection algorithm developed for use in

Supporting Population Health Outcomes Studies Using a Framework of Social Determinants Linked EHR Data

Population health outcomes research based on social determinants of health (SDoH) needs to link electronic health record (EHR) data with social determinants using Identifiable information (patients’ addresses). The connectivity expects additional computational load, privacy risk, and storage for each research. A Data Lake that facilitates research data can provide a framework for SDoH-connected EHR data

Alzheimer’s disease mitigation: AI, neuroimaging and gut-brain axis

Alzheimer’s disease (AD) is the most common form of dementia and currently there are no effective therapeutics to reverse the course once the clinical symptoms have developed. Early identification of risk factors for AD and effective interventions thereof would be critical to mitigate AD pathological development and prevent the onset of clinical symptoms. In the

Overhead imagery training data quality control: Methods for deep feature label anomaly detection

Spatial analysis of large remotely-sensed imagery (RSI) training datasets for within-class variation and between-class separability is key to uncovering issues of data diversity and potential bias, not just when vetting datasets for usage, but also during the actual dataset creation stage. Project managers of complex imagery annotation campaigns have a largely unaddressed need for tools that continuously

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