EXPLORATION OF DROSOPHILA’S IMD PATHWAY THROUGH PHIC31-MEDIATED TRANSGENESIS

Microbial infections of gram-negative bacteria in Drosophila are recognized through the immune deficiency (IMD) pathway by a molecular mechanism not completely understood. This project initiated an analysis of the potential IMD role of PGRP-LE, a peptidoglycan recognition protein that binds bacterial cell wall fragments to activate intracellular signaling. Four PGRP-Le mutants were created (E231L, S232E, R254T, and a S232E/R254T double mutant) using genomic rescue transgenes cloned into a pattB vector. A MDP transporter, Yin, was tested as a potential intracellular transporter for TCT molecules, but found as improbable. This research will allow further study of the molecular mechanism of the IKK-mediated Relish activation in IMD pathways

Beaver Lake Numeric Chlorophyll-a and Secchi Transparency Standards, Phases II and III: Uncertainty and Trend Analysis

The objective of Phases II and III of this study were to 1) assess the variation in chl‐a and ST across multiple spatial and temporal scales in Beaver Lake in order to validate the assessment method, and 2) quantify trends in chl‐a, ST, and nutrient (total phosphorus and total nitrogen) concentrations in Beaver Lake and the major inflowing rivers to verify any potential water quality impairment

Interacting Mechanisms Driving Synchrony in Neural Networks with Inhibitory Interneurons

Computational neuroscience contributes to our understanding of the brain by applying techniques from fields including mathematics, physics, and computer science to neuroscientific problems that are not amenable to purely biologic study. One area in which this interdisciplinary research is particularly valuable is the proposal and analysis of mechanisms underlying neural network behaviors. Neural synchrony, especially when driven by inhibitory interneurons, is a behavior of particular importance considering this behavior play a role in neural oscillations underlying important brain functions such as memory formation and attention. Typically, these oscillations arise from synchronous firing of a neural population, and thus the study of neural oscillations and neural synchrony are deeply intertwined. Such network behaviors are particularly amenable to computational analysis given the variety of mathematical techniques that are of use in this field. Inhibitory interneurons are thought to drive synchrony in ways described by two computational mechanisms: Interneuron Network Gamma (ING), which describes how an inhibitory network synchronizes itself; and Pyramidal Interneuron Network Gamma (PING), which describes how a population of interneurons inter-connected with a population of excitatory pyramidal cells (an E-I network) synchronizes both populations. As first articulated using simplified interneuron models, these mechanisms find network properties are the primary impetus for synchrony. However, as neurobiologists uncover interneurons exhibiting a vast array of cellular and intra-connectivity properties, our understanding of how interneurons drive oscillations must account for this diversity. This necessitates an investigation of how changing interneuron properties might disrupt the predictions of ING and PING, and whether other mechanisms might interact with or disrupt these network-driven mechanisms. In my dissertation, I broach this topic utilizing the Type I and Type II neuron classifications, which refer to properties derived from the mathematics of coupled oscillators. Classic ING and PING literature typically utilize Type I neurons which always respond to an excitatory perturbation with an advance of the subsequent action potential. However, many interneurons exhibit Type II properties, which respond to some excitatory perturbations with a delay in the subsequent action potential. Interneuronal diversity is also reflected in the strength and density of the synaptic connections between these neurons, which is also explored in this work. My research reveals a variety of ways in which interneuronal diversity alters synchronous oscillations in networks containing inhibitory interneurons and the mechanisms likely driving these dynamics. For example, oscillations in networks of Type II interneurons violate ING predictions and can be explained mechanistically primarily utilizing cellular properties. Additionally, varying the type of both excitatory and inhibitory cells in E-I networks reveals that synchronous excitatory activity arises with different network connectivities for different neuron types, sometimes driven by cellular properties rather than PING. Furthermore, E-I networks respond differently to varied strengths of inhibitory intra-connectivity depending upon interneuron type, sometimes in ways not fully accounted for by PING theory. Taken together, this research reveals that network-driven and cellularly-driven mechanisms promoting oscillatory activity in networks containing inhibitory interneurons interact, and oftentimes compete, in order to dictate the overall network dynamics. These dynamics are more complex than those predicted by the classic ING and PING mechanisms alone. The diverse dynamical properties imparted to oscillating neural networks by changing inhibitory interneuron properties provides some insight into the biological need for such variability.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143981/1/sbrich_1.pd

The role of adaptation current in synchronously firing inhibitory neural networks with various topologies

http://deepblue.lib.umich.edu/bitstream/2027.42/134564/1/12868_2015_Article_4162.pd

Bounded Confidence: How AI Could Exacerbate Social Media’s Homophily Problem

The advent of the Internet was heralded as a revolutionary development in the democratization of information. It has emerged, however, that online discourse on social media tends to narrow the information landscape of its users. This dynamic is driven by the propensity of the network structure of social media to tend toward homophily; users strongly prefer to interact with content and other users that are similar to them. We review the considerable evidence for the ubiquity of homophily in social media, discuss some possible mechanisms for this phenomenon, and present some observed and hypothesized effects. We also discuss how the homophilic structure of social media makes it uniquely vulnerable to artificial-intelligence-driven, automated influence campaigns

Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks

Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of "if" but a matter of "when" in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.Comment: 8 pages, 4 figures, submitted to IEEE Transaction

The experience of competition stress and emotions in cricket

The purpose of the study was to conduct an in-depth examination of the stress and emotion process experienced by three sub-elite level male cricketers over a series of five competitive performances. Using reflective diaries and follow up semi-structured interviews, the findings highlighted the impact of appraisal, coping, and emotion on performance, with perceptions of control and self-confidence emerging as variables that can influence the emotive and behavioral outcomes of a stressful transaction. Post-performance, guided athlete reflection was advanced as a valuable tool in the production and application of idiographic coping behaviors that could enhance perceptions of control and self-confidence and influence stress and emotion processes

Human Error and Accident Causation Theories, Frameworks and Analytical Techniques: An Annotated Bibliography

Over the last several decades, humans have played a progressively more important causal role in aviation accidents as aircraft have become more [complex]. Consequently, a growing number of aviation organizations are tasking their safety personnel with developing safety programs to address the highly complex and often nebulous issue of human error. However, there is generally no “off-the-shelf” or standard approach for addressing human error in aviation. Indeed, recent years have seen a proliferation of human error frameworks and accident investigation schemes to the point where there now appears to be as many human error models as there are people interested in the topic. The purpose of the present document is to summarize research and technical articles that either directly present a specific human error or accident analysis system, or use error frameworks in analyzing human performance data within a specific context or task. The hope is that this review of the literature will provide practitioners with a starting point for identifying error analysis and accident investigation schemes that will best suit their individual or organizational needs