Magnesium deficiency in rats is known to precipitate audiogenic seizures. An unknown mineral factor in a diet mixture was found to substantially reduce the seizure occurence in magnesium deficient vii animals. This was corrected when the same mineral mixture was remade. Subsequently the faulty mineral mixture was discarded. This research .. as aimed at determining the mineral factor responsible for the observed changes in seizure occurence, and also aimed at investigating the relationships of serum calcium, magnesium and phosphorus concentrations to seizure susceptibility. Treatments to change the serum concentrations of these minerals included dietary manipulation, subcutaneous injection of calcitonin, of 1 ,25-dihydroxycholecalciferol, of calcium and of phosphorus. Animals fed magnesium deficient, low phosphorus diet and magnesium deficient animals fasted over-night were found to have significantly lower audiogenic seizure susceptibility than all other magnesium deficient animals regardless of treatment. Reduced audiogenic seizure susceptibility was not produced by any of the injections. The reduction in seizure with the magnesium deficient, low phosphorus diet indicates that phosphorus is an important factor in the mechanism of audiogenic seizuring in magnesium deficient rats.

Indiana University-Purdue University Indianapolis (IUPUI) / Science, technology, engineering, and math (STEM) researchers and organizations recognize that a large gender and racial disparity exists in these fields. However, individuals with intersectional identities (i.e., Black women) have unique experiences of bias that preclude them from entering STEM careers and feeling a sense of belonging. As such, featuring an employee that demonstrates allyship for Black women on an organization’s website can be a useful identity-safe cue to signal that a Black woman’s identity will be valued and promote the recruitment of Black women in STEM organizations. Yet, research indicates that Black women who are high in stigma consciousness (i.e., sensitive to potential discrimination based on their identity) do not trust or believe a White woman ally presented in a written profile cares about helping Black women. The current study found that presenting an ally in a video profile mitigated these negative effects of stigma consciousness, and increased Black women’s anticipated belonging and trust in a fictional STEM organization via higher perceptions of allyship. Theoretical implications for research, practical implications for organizations, and future research avenues to explore are discussed

The cost of developing new drugs is high and the aim of computer-assisted drug discovery is to reduce that development cost, either through virtual screening or generating novel compounds. System biology is one approach to drug discovery where the response of a biological system is the subject of study, instead of drug target interaction. One way to observe a biological system is through microscopy images that are taken of cells perturbed with compounds. Image software extracts information called morphological profiles from the images that can be used for data hungry models. One of the ways artificial intelligence has been applied to drug discovery is with generative models that can generate new compounds. One such generative model is reinforcement learning that employs a critic to guide the generation of compounds towards desirable behaviors. In this study different machine learning models were tested if they could predict the morphological response of COVID-19 infected cells to compounds from their structure. No modells showed any promising results. The reason that no model performed well was because of the dataset. There is a lot of variance in the dataset, meaning that the response to the same compound varies. There was also a lot of difference between the compounds in the dataset, meaning that any representation that the model learns does not transfer over to other compounds. The data set was also imbalanced with more inactive compounds.

In this dissertation, methods utilizing matched illumination theory to optimally design waveforms for enhanced target detection and identification in the context of through-the-wall radar (TWR) are explored. The accuracy of assumptions made in the waveform design process is evaluated through simulation. Additionally, the moisture profile of an adobe wall is investigated, and it is shown that the moisture profile of the wall will introduce significant variations in the matched illumination waveforms and subsequently, affect the resulting ability of the radar system to correctly identify and detect a target behind the wall. Experimental measurements of adobe wall moisture and corresponding dielectric properties confirms the need for accurate moisture profile information when designing radar waveforms which enhance signal-to-interference-plus-noise ratio (SINR) through use of matched illumination waveforms on the wall/target scenario. Furthermore, an evaluation of the ability to produce an optimal, matched illumination waveform for transmission using simple, common radar systems is undertaken and radar performance is evaluated.

No description available.

Antimicrobial susceptibility profiles were assembled for isolates of Moraxella catarrhalis collected from the Mountain Home Veteran's Affairs Medical Center (VAMC) clinical laboratory in Johnson City, Tennessee. The goal of the study was to identify isolates for genetic characterization using comparisons of susceptibility profiles. Isolates of Moraxella catarrhalis collected from July 1984 through 1994 were analyzed for β-lactamase production using a Cefinase disk assay. A multi-drug profile consisting of 11 β-lactam antibiotics was performed on the 41 M. catarrhalis isolates. Kirby Bauer disk assays were performed for 7 cephalosporin and 4 non-cephalosporin antibiotics. In summary, 2 observations implicate more complex resistance determinants than the 2 known forms of the BRO β-lactamase. First, there was overlap in the ranges of inhibition zones. Second, several isolates had antibiotic-specific deviations from typical profiles. These data suggest either more variation in the M. catarrhalis BRO β-lactamase than described or contributions to resistance from undescribed determinants.

Similarity searches are an essential component to most bioinformatic applications. They form the bases of structural motif identification, gene identification, and insights into functional associations. With the rapid increase in the available genetic data through a wide variety of databases, similarity searches are an essential tool for accessing these data in an informative and productive way. In our chapter, we provide an overview of similarity searching approaches, related databases, and parameter options to achieve the best results for a variety of applications. We then provide a worked example and some notes for consideration. Homology detection is one of the most basic and fundamental problems at the heart of bioinformatics. It is central to problems currently under intense investigation in protein structure prediction, phylogenetic analyses, and computational drug development. Currently discriminative methods for homology detection, which are not readily interpretable, are substantially more powerful than their more interpretable counterparts, particularly when sequence identity is very low. Here I present a computational graph-based framework for homology inference using physiochemical amino acid properties which aims to both reduce the gap in accuracy between discriminative and generative methods and provide a framework for easily identifying the physiochemical basis for the structural similarity between proteins. The accuracy of my method slightly improves on the accuracy of PSI-BLAST, the most popular generative approach, and underscores the potential of this methodology given a more robust statistical foundation.

No description available.

Machine learning is fast growing field as it can be applied to solve a large amount of problems. One large subsection of machine learning are artificial neural networks (ANN), these work on pattern recognition and can be trained with data sets of known solutions. The objective of this thesis is to discuss the creation of an ANN capable of classifying differences in propylene glycol concentrations, up to 10%. Utilizing a micro pipette thermal sensor (MTS) it is possible to measure the heat propagation of a liquid from a laser pulse. The ANN can then be trained beforehand with simulated data and be tested in real time with temperature data from the MTS. This method could be applied to find the thermal conductivity of unknown fluids and biological samples, such as cells and tissues.

This paper presents calculations for forbidden emission-line profile shapes arising from colliding wind binaries. The main application is for systems involving a Wolf–Rayet (WR) star and an OB star companion. The WR wind is assumed to dominate the forbidden line emission. The colliding wind interaction is treated as an Archimedean spiral with an inner boundary. Under the assumptions of the model, the major findings are as follows. (i) The redistribution of the WR wind as a result of the wind collision is not flux conservative but typically produces an excess of line emission; however, this excess is modest at around the 10 per cent level. (ii) Deviations from a flat-toped profile shape for a spherical wind are greatest for viewing inclinations that are more nearly face-on to the orbital plane. At intermediate viewing inclinations, profiles display only mild deviations from a flat-toped shape. (iii) The profile shape can be used to constrain the colliding wind bow shock opening angle. (iv) Structure in the line profile tends to be suppressed in binaries of shorter periods. (v) Obtaining data for multiple forbidden lines is important since different lines probe different characteristic radial scales. Our models are discussed in relation to Infrared Space Observatory data for WR 147 and γ Vel (WR 11). The lines for WR 147 are probably not accurate enough to draw firm conclusions. For γ Vel, individual line morphologies are broadly reproducible but not simultaneously so for the claimed wind and orbital parameters. Overall, the effort demonstrates how lines that are sensitive to the large-scale wind can help to deduce binary system properties and provide new tests of numerical simulations.