Testing of Indazole Inhibitors of KasA, a Vital Enzyme of M. tuberculosis

Tuberculosis is a disease that affects the lungs caused by Mycobacterium tuberculosis (M. tuberculosis). Although drug treatment options exist, increased rates of antibiotic resistant strains have become more prevalent in recent years, driving a need for new treatment approaches. KasA, a β-ketoacyl synthase, has been found to synthesize parts of the cell wall and been identified as an attractive drug target. Previous medicinal chemistry research has been completed to synthesize six effective competitive inhibitors of KasA that would potentially block the enzyme from binding the substrate, preventing elongation of the backbone and creation of the mycolic fatty acids that form the mycobacterial cell wall, ultimately killing the bacterium. With the sulfonamide and amine derivatives fully synthesized, theses were tested by means of the microdilution broth panel method using 96-well and 24-well titration plates, as well as through SPOTi assays to determine their effectiveness as potential drug candidates. Due to M. tuberculosis being highly contagious and infectious upon contact, the surrogate model Mycobacterium aurum (M. aurum) was used since it has the same target enzyme as in M. tuberculosis. Based on the high strength of the ligand-receptor binding energy values obtained from AutoDock Tools, a molecular binding simulation software, it was concluded that the six derivatives were suitable candidates for growth inhibition. Despite complications with the microdilution broth panel with the 96-well and 24-well titration plates, there is partial evidence through the SPOTi assay to support that the N-1-methyl-6-indazolyl benzene sulfonamide derivative potentially reduces mycobacterial growth on the plates containing M. aurum

Keys to successful mentoring of undergraduate research teams with an emphasis in applied mathematics research

Independent of institution size and faculty research expectations, a growing number of colleges and universities encourage their undergraduates to engage in some form of research experience. To meet the demand of students seeking such experiences and to ensure these experiences are of high quality, it is imperative to have qualified mentors. While senior faculty rely on years of experience in mentoring research projects, professors stepping into these undergraduate mentoring roles at the graduate student or junior faculty level may not be as equipped to handle the potential hurdles unique to working with teams of undergraduates. This article is aimed at such an audience. Although much of the article is relevant to mentoring projects in any area of mathematics, some comments and suggestions are directed more to working with students in applied mathematics. This article includes advice gleaned from the National Science Foundation-sponsored Center for Undergraduate Research in Mathematics (CURM) faculty workshop in conjunction with personal experiences from the author, a CURM mini-grant recipient. The primary goals of the paper are to answer questions one might have when starting a project with undergraduates and to provide the reader with concrete steps to follow in planning and successfully completing such a project

Conditional Dilated Attention Tracking Model - C-DATM

Current commercial tracking systems do not process images fast enough to perform target-tracking in real- time. State-of-the-art methods use entire scenes to locate objects frame-by-frame and are commonly computationally expensive because they use image convolutions. Alternatively, attention mechanisms track more efficiently by mimicking human optical cognitive interaction to only process small portions of an image. Thus, in this work we use an attention-based approach to create a model called C-DATM (Conditional Dilated Attention tracking Model) that learns to compare target features in a sequence of image-frames using dilated convolutions. The C-DATM is tested using the Modified National Institute of Standards and Technology handwritten digits. We also compare the results achieved by C-DATM to the results achieved by other attention-based networks like Deep Recurrent Attentive Writer and Recurrent Attention Tracking Model that appear in the literature. C-DATM builds on previous attention principles to achieve generic, efficient, and recurrent-less object tracking. The GOTURN(General Object Tracking using Regression Networks) model which won the VOT 2014 dataset challenge contains similar operating principles to C-DATM and is used as an exemplar to explore the advantages and disadvantages C-DATM. The results of this comparison demonstrate that C-DATM has a number of significant advantage over GOTURN including faster processing of image sequences and the ability to generalize to tracking new targets without retraining the system

Woodchip denitrification bioreactor for reducing nitrate in contaminated well water for St. Francis Retreat Center

The St. Francis Retreat Center, which serves over 7000 people each year, has had issues with the health of Flint Lake, located on their property, during the drought season. By utilizing a nearby water well, the retreat center looks to recharge the lake and sustain its water levels, essentially restoring its natural ecosystem. The issue is that the well water is contaminated with high amounts of nitrates, which is not only an issue for the lake’s health, but also is very unsafe for human consumption. In order to design a water treatment system that is ecofriendly, sustainable, and cost-efficient, the team looked to construct and test the effectiveness of a denitrifying woodchip bioreactor. This design will serve as a prototype for a much larger implementable system that will be able to handle the flow rates from the water being pumped from the contaminated well. To run the tests, a 300 gallon steel tank served as the bioreactor apparatus that facilitated the process of denitrification using heterotrophic bacteria which consumes the nitrates in the water and synthesizes them into nitrogen gas. The prototype demonstrated that denitrifying bacteria, using the woodchips as a growth source, effectively reduce nitrate levels to meet government-mandated standards, and can be implemented on a larger scale

The Grouping of the Germanic Languages: A Critical Review

The literature regarding the grouping of the Germanic languages will be reviewed and a potential solution to the problems of the division of the Germanic language will be proposed. This is however quite a complex task. Most of the Germanic languages share a great number of similarities, and individual languages often have features common to more than one group. Old Saxon, Old Low Franconian, and Old English are examples of languages that make the grouping much more complex than it appears on the surface. The grouping Germanic languages has been debated by linguists since the 19th century, and there are still dissenting views on this topic. Old English, Old Low Franconian and Old Saxon pose significant issues with regard to grouping, and the research for this thesis will attempt to clarify where these languages fit with other Germanic languages and what the best classification of the Germanic languages would be. The Stammbaum model and Wellentheorie will be reviewed among other methods like dialect geography and ethnography, but the listing of isoglosses will be the primary method employed in this study. The Germanic languages exist on a (dialect) continuum, and the divisions are much more fluid than the previous attempts at grouping would imply, even more so within West Germanic. Anglo-Frisian comprises North Sea Germanic, Old Saxon and Old Low Franconian form a transition zone, and Old High German constitutes the Elbe language

A Mathematical Model of the Effect of Social Distancing on the Spread of COVID-19

Social distancing is an effective method of impeding the spread of a novel disease such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but is dependent on public involvement and is susceptible to failure when sectors of the population fail to participate. A standard SIR model is largely incapable of modeling differences in a population due to the broad generalizations it makes such as uniform mixing and homogeneity of hosts, which results in lost detail and accuracy when modeling heterogeneous populations. By further compartmentalizing an SIR model, via the separation of people within susceptible and infected groups, we can more accurately model epidemic dynamics and predict the eventual outcome, highlighting the importance of societal participation in social distancing measures during novel outbreaks