Through the Media\u27s Eyes: The Media Coverage of the 1968 Memphis Sanitation Strike and the Assassination of Dr. Martin Luther King Jr.

In this professional project, the researcher aims to uncover media bias presented in two publications, The New York Times and The Commercial Appeal in the coverage of the 1968 Memphis Sanitation Strike and the assassination of Rev. Dr. Martin Luther King Jr.. The researcher gathered fifteen articles spanning a date range from February 13, 1968 to June 9, 1968 in order to successfully gain perspective on the various events that took place. Five research questions were presented to investigate the diversity of direct quotes, writing style, story placement and length of stories. Three individuals assisted in interpreting the articles using the guidelines presented in the research questions. The coder’s results were then entered into tables and seen as research findings used to establish answers to the research questions

Exploring Protein–Nanoparticle Interactions with Coarse‐Grained Protein Folding Models

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136668/1/smll201603748.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136668/2/smll201603748-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136668/3/smll201603748_am.pd

In-Context Policy Iteration

This work presents In-Context Policy Iteration, an algorithm for performing Reinforcement Learning (RL), in-context, using foundation models. While the application of foundation models to RL has received considerable attention, most approaches rely on either (1) the curation of expert demonstrations (either through manual design or task-specific pretraining) or (2) adaptation to the task of interest using gradient methods (either fine-tuning or training of adapter layers). Both of these techniques have drawbacks. Collecting demonstrations is labor-intensive, and algorithms that rely on them do not outperform the experts from which the demonstrations were derived. All gradient techniques are inherently slow, sacrificing the "few-shot" quality that made in-context learning attractive to begin with. In this work, we present an algorithm, ICPI, that learns to perform RL tasks without expert demonstrations or gradients. Instead we present a policy-iteration method in which the prompt content is the entire locus of learning. ICPI iteratively updates the contents of the prompt from which it derives its policy through trial-and-error interaction with an RL environment. In order to eliminate the role of in-weights learning (on which approaches like Decision Transformer rely heavily), we demonstrate our algorithm using Codex, a language model with no prior knowledge of the domains on which we evaluate it.Comment: 10 pages, 4 figures, submitted to ICLR 202

Flexible Modeling of Epidemics with an Empirical Bayes Framework

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic's behavior, policy makers can design and implement more effective countermeasures. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, as well as the season onset, duration, peak time, and peak height, with and without additional data from Google Flu Trends, as part of the CDC's 2013--2014 "Predict the Influenza Season Challenge". Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, these models may not accurately capture the range of possible behaviors that we may see in the future. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to other diseases as well. Another important advantage of this method is that it produces a complete posterior distribution for any desired forecasting target, rather than mere point predictions. We report prospective influenza-like-illness forecasts that were made for the 2013--2014 U.S. influenza season, and compare the framework's cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.Comment: 52 page

The Craft Academy Robotics Project

To begin, the team’s research project consisted of a robot that could compete on a 12-foot by 12-foot playing field alongside 3 other robots. The robot had to fit within an 18in-by-18in limit, it could be made of a variety of materials and motors specified in the game manual to help it achieve the most amount of points possible. There are many ways to earn points in Freight Frenzy divided into 3 sections, Autonomous, Tele-Operation or “Op” for short, and End-Game: In the Autonomous section, the match starts with a 30-second Autonomous Period where Robots are operated only via pre-programmed instructions. Teams are not allowed to control Robot behavior with the Driver Station or any other actions. The Control Hub is placed in a hands-off location during said Autonomous Period so that it is evident that there is no human control of Robots. The robots can obtain points by parking in the Alliance storage unit, parking in the closest to the robot’s alliance, moving freight into the shipping hubs, or alliance specific storage unit, with an additional bonus of using a camera to recognize and move a rubber duck or Team Shipping Element to a randomized part of the shipping hub. During the Tele-Op part, teams can score points by going over or around the PVC barrier to obtain whiffle balls or different weighted plastic cubes and placing them in either the Alliance Shipping Hub or Shared Shipping Hub. Finally, in End-Game, Teams can put the custom capping element on the Alliance Shipping Hub, spin the Carrousel to get up to 10 ducks, or park in a Warehouse.https://scholarworks.moreheadstate.edu/celebration_posters_2022/1043/thumbnail.jp

TeV-scale seesaw from a multi-Higgs model

We suggest new simple model of generating tiny neutrino masses through a TeV-scale seesaw mechanism without requiring tiny Yukawa couplings. This model is a simple extension of the standard model by introducing extra one Higgs singlet, and one Higgs doublet with a tiny vacuum expectation value. Experimental constraints, electroweak precision data and no large flavor changing neutral currents, are satisfied since the extra doublet only has a Yukawa interaction with lepton doublets and right-handed neutrinos, and their masses are heavy of order a TeV-scale. Since active light neutrinos are Majorana particles, this model predicts a neutrinoless double beta decay.Comment: 21 pages, 8 figure

The Vaginal Microbiome: Disease, Genetics and the Environment

The vagina is an interactive interface between the host and the environment. Its surface is covered by a protective epithelium colonized by bacteria and other microorganisms. The ectocervix is nonsterile, whereas the endocervix and the upper genital tract are assumed to be sterile in healthy women. Therefore, the cervix serves a pivotal role as a gatekeeper to protect the upper genital tract from microbial invasion and subsequent reproductive pathology. Microorganisms that cross this barrier can cause preterm labor, pelvic inflammatory disease, and other gynecologic and reproductive disorders. Homeostasis of the microbiome in the vagina and ectocervix plays a paramount role in reproductive health. Depending on its composition, the microbiome may protect the vagina from infectious or non-infectious diseases, or it may enhance its susceptibility to them. Because of the nature of this organ, and the fact that it is continuously colonized by bacteria from birth to death, it is virtually certain that this rich environment evolved in concert with its microbial flora. Specific interactions dictated by the genetics of both the host and microbes are likely responsible for maintaining both the environment and the microbiome. However, the genetic basis of these interactions in both the host and the bacterial colonizers is currently unknown. _Lactobacillus_ species are associated with vaginal health, but the role of these species in the maintenance of health is not yet well defined. Similarly, other species, including those representing minor components of the overall flora, undoubtedly influence the ability of potential pathogens to thrive and cause disease. Gross alterations in the vaginal microbiome are frequently observed in women with bacterial vaginosis, but the exact etiology of this disorder is still unknown. There are also implications for vaginal flora in non-infectious conditions such as pregnancy, pre-term labor and birth, and possibly fertility and other aspects of women’s health. Conversely, the role of environmental factors in the maintenance of a healthy vaginal microbiome is largely unknown. To explore these issues, we have proposed to address the following questions:

*1.	Do the genes of the host contribute to the composition of the vaginal microbiome?* We hypothesize that genes of both host and bacteria have important impacts on the vaginal microbiome. We are addressing this question by examining the vaginal microbiomes of mono- and dizygotic twin pairs selected from the over 170,000 twin pairs in the Mid-Atlantic Twin Registry (MATR). Subsequent studies, beyond the scope of the current project, may investigate which host genes impact the microbial flora and how they do so.
*2.	What changes in the microbiome are associated with common non-infectious pathological states of the host?* We hypothesize that altered physiological (e.g., pregnancy) and pathologic (e.g., immune suppression) conditions, or environmental exposures (e.g., antibiotics) predictably alter the vaginal microbiome. Conversely, certain vaginal microbiome characteristics are thought to contribute to a woman’s risk for outcomes such as preterm delivery. We are addressing this question by recruiting study participants from the ~40,000 annual clinical visits to women’s clinics of the VCU Health System.
*3.	What changes in the vaginal microbiome are associated with relevant infectious diseases and conditions?* We hypothesize that susceptibility to infectious disease (e.g. HPV, _Chlamydia_ infection, vaginitis, vaginosis, etc.) is impacted by the vaginal microbiome. In turn, these infectious conditions clearly can affect the ability of other bacteria to colonize and cause pathology. Again, we are exploring these issues by recruiting participants from visitors to women’s clinics in the VCU Health System.

Three kinds of sequence data are generated in this project: i) rDNA sequences from vaginal microbes; ii) whole metagenome shotgun sequences from vaginal samples; and iii) whole genome shotgun sequences of bacterial clones selected from vaginal samples. The study includes samples from three vaginal sites: mid-vaginal, cervical, and introital. The data sets also include buccal and perianal samples from all twin participants. Samples from these additional sites are used to test the hypothesis of a per continuum spread of bacteria in relation to vaginal health. An extended set of clinical metadata associated with these sequences are deposited with dbGAP. We have currently collected over 4,400 samples from ~100 twins and over 450 clinical participants. We have analyzed and deposited data for 480 rDNA samples, eight whole metagenome shotgun samples, and over 50 complete bacterial genomes. These data are available to accredited investigators according to NIH and Human Microbiome Project (HMP) guidelines. The bacterial clones are deposited in the Biodefense and Emerging Infections Research Resources Repository ("http://www.beiresources.org/":http://www.beiresources.org/). 

In addition to the extensive sequence data obtained in this study, we are collecting metadata associated with each of the study participants. Thus, participants are asked to complete an extensive health history questionnaire at the time samples are collected. Selected clinical data associated with the visit are also obtained, and relevant information is collected from the medical records when available. This data is maintained securely in a HIPAA-compliant data system as required by VCU’s Institutional Review Board (IRB). The preponderance of these data (i.e., that judged appropriate by NIH staff and VCU’s IRB are deposited at dbGAP ("http://www.ncbi.nlm.nih.gov/gap":http://www.ncbi.nlm.nih.gov/gap). Selected fields of this data have been identified by NIH staff as ‘too sensitive’ and are not available in dbGAP. Individuals requiring access to these data fields are asked to contact the PI of this project or NIH Program Staff. 
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Towards effective small scale microbial fuel cells for energy generation from urine

© 2016 The Authors. Published by Elsevier Ltd. To resolve an increasing global demand in energy, a source of sustainable and environmentally friendly energy is needed. Microbial fuel cells (MFC) hold great potential as a sustainable and green bioenergy conversion technology that uses waste as the feedstock. This work pursues the development of an effective small-scale MFC for energy generation from urine. An innovative air-cathode miniature MFC was developed, and the effect of electrode length was investigated. Two different biomass derived catalysts were also studied. Doubling the electrode length resulted in the power density increasing by one order of magnitude (from 0.053 to 0.580 W m-3). When three devices were electrically connected in parallel, the power output was over 10 times higher compared to individual units. The use of biomass-derived oxygen reduction reaction catalysts at the cathode increased the power density generated by the MFC up to 1.95 W m-3, thus demonstrating the value of sustainable catalysts for cathodic reactions in MFCs