Sep. 15, 2023
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 wean-to-finish production phase, these pigs are more valuable due to added daily feed expenditure. Thus, reduction of weaning-to-finishing pig mortality is necessary to improve sustainability and profitability of current pig production operations. The objective of the current study was to identify health and climatic variables that…
Sep. 15, 2023
Enhancing Single Cell RNA-seq Analysis and Annotation for Understanding the Impact of CD137 Agonist on Cancer Immunoprevention through Advanced Informatics Algorithms
SA-4-1BBL, a novel CD137 receptor agonist, has been generated by our group and selected as a promising candidate for preventing cancer progression in research. The SA-4-1BBL demonstrates notable immunoprevention efficacy in diverse tumor models when utilized as a treatment in research subjects. Nevertheless, this efficacy stands in contrast to 3H3, another CD137-targeting antibody, which appears failing in generating cancer prevention outcomes in research subjects. The differing results obtained with SA-4-1BBL and 3H3 have raised questions about the reasons behind this disparity even though SA-4-1BBL and 3H3 target the same CD137 receptor. To uncover the underlying differences between SA-4-1BBL, 3H3,…
Aug. 30, 2023
Exploration of Geospatial Artificial Intelligence (AI) Using Remote Sensing to Discover Climate, Environment, and Anthropogenic Features Related to Population Health
Computational modeling of geospatial data can bring valuable insight into communicable diseases, noncommunicable diseases, and vector-borne diseases. However, each of these disease classes has its own challenges in terms of geospatial data analysis. Complex dynamics of environmental and social risk factors can determine the distribution of some diseases such as COVID-19, obesity and malaria, respectively. Environmental factors such as humidity, precipitation, rainfall, temperature, land use and land coverage, vegetation, and elevation play key roles in predicting prevalence or outbreaks. Measuring these features is time-consuming, costly, and biased since they are subject to human judgment. Remote sensing data, environmental and imagery,…
July 25, 2023
Data Science Innovation to Advance Journalism
COLUMBIA, Mo. (July 24, 2023) — Damon Kiesow is now the Knight Chair in Journalism Innovation at the Missouri School of Journalism, an update from his previous title of Knight Chair in Digital Editing and Producing. The new title reflects the evolution of the endowed chair position and Kiesow’s research and service as he finishes his fifth year at the School. Read more…
July 24, 2023
Productive Summer Curriculum Retreat at the MU Institute for Data Science and Informatics
The faculty members at the MU Institute for Data Science and Informatics recently concluded a highly successful curriculum retreat this summer. The primary focus of the retreat was to meticulously review the curriculum, spanning from bootcamps to capstone projects, with the ultimate goal of providing an unparalleled learning experience for our students. Throughout the retreat, our dedicated faculty engaged in rigorous discussions and collaborative sessions to ensure the continuous refinement of our curriculum. By doing so, we aim to maintain the utmost coherence in our courses, while simultaneously delivering cutting-edge AI/ML tools and techniques in our data science training. This…
July 21, 2023
Deer spread COVID to humans multiple times, new research suggests
Americans have transmitted COVID-19 to wild deer hundreds of times, an analysis of thousands of samples collected from the animals suggests, and people have also caught and spread mutated variants from deer at least three times. The analysis published Monday stems from the first year of a multiyear federal effort to study the virus as it has spread into American wildlife, spearheaded by the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service, or APHIS. Read More……
May 4, 2023
Exploring the Role of social media in Health Information Dissemination: A Focus on Vaccine Equity and Health Insurance
Social media has increasingly become a popular platform for sharing and seeking health information. However, the credibility and usefulness of this information are often questionable. The overall goal of my research program is to provide a comprehensive evaluation of chronic disease health information as well as other related topical health issues on social media channels and to explore the role of these channels in information dissemination using social network analysis. This presentation describes two studies on trending health topics during the COVID-19 pandemic. The first study aimed to evaluate health and vaccine equity trends during the Omicron wave on Twitter…
April 25, 2023
Tool Development for Resolving and Visualizing 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. 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 heart using standard cardiac magnetic resonance (CMR), which usually attempts to average the beats. Real-time CMR imaging, without averaging, is better suited to visualizing irregular cardiac cycles. However, radiology lacks methods to manage the irregularity. We developed tools for easy viewing and comparison heterogeneous heartbeats measured by CMR…
April 25, 2023
Utilizing Real-World EHR Data: Early Predictive and Prescriptive Analysis for Glaucoma Using Machine Learning Methods
Electronic health records (EHRs) have emerged as a crucial source of data for data-driven translational clinical research. Meanwhile, machine learning (ML) models have advanced rapidly and are now a potent tool for analyzing EHR data, allowing us to harness a large amount of diverse clinical information. Various ML techniques have been created and adapted to predict outcomes using EHR data. However, effectively utilizing an observational and predictive window from the sequential EHR data is vital to developing a reliable model. This investigation applies advanced ML methods for predictive and prescriptive analysis, targeting multiple clinical encounters to provide suitable decision-making recommendations…