An Agent-Based Modeling Approach to Determine Overwintering Habits of American Robins and Eastern Bluebirds

American Robins (Turdus migratorius) and Eastern Bluebirds (Sialia sialis) are two species of migratory thrushes that breed in Northwest Indiana but historically are uncommonly present during the winter season (November 1 - March 1). These trends have changed recently, and both species are seen more abundantly during the winter. Recently invaded non-native fruiting plants continue to provide nutrients for the birds throughout the winter and may contribute to the increased avian populations during that time. To measure the effect these food sources contribute to thrush wintering habits, we created an agent-based computer model to simulate the birds\u27 movement in Northwest Indiana along with their food consumption over the course of the winter season. The model incorporates availability of food sources, foraging and roosting behavior, bio-energetics, and starvation, with parameter values informed by the literature. Ultimately, this model will yield a carrying capacity that could explain changes in the birds\u27 migratory patterns

An Agent-Based Modeling Approach to Determine Winter Survival Rates of American Robins and Eastern Bluebirds

American Robins (Turdus migratorius) and Eastern Bluebirds (Sialia sialis) are two species of migratory thrushes that breed in Northwest Indiana but historically are uncommon during the winter season. These trends have changed recently, and both species are seen more abundantly during the winter. Recently invaded non-native fruiting plants continue to provide nutrients for the birds throughout the winter and may contribute to the increased avian populations during that time. To measure the effect these food sources contribute to thrush wintering habits, we created an agent-based computer model to simulate the birds\u27 movement in Northwest Indiana along with their food consumption over the course of the winter season. The model incorporates availability of food sources, foraging and roosting behavior, bio-energetics, and starvation, with parameter values informed by the literature. We obtained simulated winter survival rates of the birds that could begin to explain the changes in the birds\u27 migratory patterns

A prenylated dsRNA sensor protects against severe COVID-19

Inherited genetic factors can influence the severity of COVID-19, but the molecular explanation underpinning a genetic association is often unclear. Intracellular antiviral defenses can inhibit the replication of viruses and reduce disease severity. To better understand the antiviral defenses relevant to COVID-19, we used interferon-stimulated gene (ISG) expression screening to reveal that OAS1, through RNase L, potently inhibits SARS-CoV-2. We show that a common splice-acceptor SNP (Rs10774671) governs whether people express prenylated OAS1 isoforms that are membrane-associated and sense specific regions of SARS-CoV-2 RNAs, or only express cytosolic, nonprenylated OAS1 that does not efficiently detect SARS-CoV-2. Importantly, in hospitalized patients, expression of prenylated OAS1 was associated with protection from severe COVID-19, suggesting this antiviral defense is a major component of a protective antiviral response

L(4,3,2,1) labeling

An L(4, 3, 2, 1)-labeling of a vertex-edge graph G is a function f that assigns either 0 or a specific positive integer as a label to each vertex with the following condition: given 2 vertices, the sum of the difference of their labels and their distance in the graph must be at least 5. Symbolically: |f(u) − f(v)| + d(u, v) ≥ 5 if u ≠ v. The L(4, 3, 2, 1)-labeling number of a vertex-edge graph G is the smallest positive integer k, such that the condition previously stated is followed, and there is no label greater than k. In this paper, we show the L(4, 3, 2, 1)-labeling number of several types of graphs including cycles, paths, spider graphs, stars, and some caterpillars

Mathematical Modeling of the Evolution of the Domestic Dog

The domestication of the gray wolf (Canis lupus) is generally thought to be the earliest example of animal domestication by humans. Yet, the processes which gave rise to it are still relatively unknown. There are two prominent hypotheses: that the wolf was domesticated by human intentional breeding or that wolves essentially domesticated themselves. In the latter case, wolves who were more tolerant of humans and more willing to enter early human settlements gained an evolutionary advantage over those that were not willing to do so. We have developed an agent-based (mathematical and computer) model (ABM) to simulate this second scenario. The model incorporates availability of food sources, time spent with humans, the tameness of the wolves, reproduction, and death, with the values of these parameters being informed by the literature. Ultimately, we would also like to build an ABM of the first scenario (human intentional breeding) with the goal of comparing simulated domestication times of the two scenarios to archaeology evidence of when wolves were domesticated. This would allow us to determine which hypothesis is most probable

Web Application for Student Analytics

Schools collect a considerable amount of important data about their students, but using that data effectively can be difficult. In support of Joseph Haines, Adjunct Instructor of Mathematics and Statistics, this group of 5 students has developed a web tool that allows schools to search for patterns in their student data, especially patterns related to test scores and other quantitative data. This tool was developed in an Agile environment using a combination of Python, its Flask and Bokeh libraries, and HTML/CSS, which creates an interactive web tool that can support multiple concurrent users, as well as enhanced maintainability through centralized support capabilities. After a user uploads their data, the tool has three key functions, including pivot table views, data visualization, and multiple regressions. This project will allow teachers and school administrators to gain insight into how students are performing, and allow them to identify problems and teach students more effectively

Web Application for Student Analytics

Schools collect a considerable amount of important data about their students, but using that data effectively can be difficult. In support of Joseph Haines, Adjunct Instructor of Mathematics and Statistics, this group of 5 students has developed a web tool that allows schools to search for patterns in their student data, especially patterns related to test scores and other quantitative data. This tool was developed in an Agile environment using a combination of Python, its Flask and Bokeh libraries, and HTML/CSS, which creates an interactive web tool that can support multiple concurrent users, as well as enhanced maintainability through centralized support capabilities. After a user uploads their data, the tool has three key functions, including pivot table views, data visualization, and multiple regressions. This project will allow teachers and school administrators to gain insight into how students are performing, and allow them to identify problems and teach students more effectively