Knowledge of Assistive Technology and Services Available to Students With Disabilities

The purpose of this study was to investigate the knowledge of students and faculty regarding the assistive technology and services available to students with disabilities at a university in the southeastern United States. This study consists of 300 participants who were asked to respond to 8 questions which were designed to collect data aligned with the three research objectives. The findings may be used to determine if further education or communication is needed in order to better inform students, staff, and faculty of assistive technology and services available on campus

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Assumption without representation: the unacknowledged abstraction from communities and social goods

We have not clearly acknowledged the abstraction from unpriceable “social goods” (derived from communities) which, different from private and public goods, simply disappear if it is attempted to market them. Separability from markets and economics has not been argued, much less established. Acknowledging communities would reinforce rather than undermine them, and thus facilitate the production of social goods. But it would also help economics by facilitating our understanding of – and response to – financial crises as well as environmental destruction and many social problems, and by reducing the alienation from economics often felt by students and the public

\u3ci\u3eGregarina triboliorum\u3c/i\u3e (Eugregarinida: Gregarinidae) n. sp. from \u3ci\u3eTribolium confusum\u3c/i\u3e and Resolution of the Confused Taxonomic History of \u3ci\u3eGregarina minuta\u3c/i\u3e Ishii, 1914

The septate gregarine parasites of flour beetles (Tribolium spp.) include Gregarina minuta Ishii, 1914, a relatively small species in which both primite and satellite possess an obvious protomerite, and a larger species that lacks the satellite protomerite. The latter species has been placed in the genera Didymophyes and Hirmocystis by various authors, but studies reported here demonstrate that this species, herein described as Gregarina triboliorum, exhibits early pairing and produces oocyst chains, both characteristics of the genus Gregarina. The oocysts of this new species are described for the first time. In addition, experimental infections using oocysts from single gametocysts reveal that oocyst chain number is variable but is typically one, two or four. Prior experiments involving a related beetle, Tenebrio molitor, demonstrated extreme host specificity within the four Gregarina species parasitizing larval and adult hosts. However, G. triboliorum is not limited either stadially or specially, infecting both adults and larvae of Tribolium confusum and Tribolium castaneum

Circulating leptin and cortisol after burn injury: loss of diurnal pattern.

Leptin, a hormone involved in appetite and metabolic energy expenditure, could have a role in the reduced appetite and/or energy expenditure after burn injury. In this study, the diurnal pattern of circulating leptin concentrations was compared with body mass index (BMI), sex, glucose, insulin, and the diurnal cortisol rhythm in burn patients. Plasma samples were collected at 12:00 pm and 02:00 am from severely burned adults and children. Circulating leptin, insulin, and cortisol were measured by radioimmunoassay. Results were compared with previously published data from healthy control subjects. Overall, plasma leptin levels were lower in burn patients (5.7 +/- 1.2 ng/mL) compared with control subjects (10.5 +/- 1.7 ng/mL, P = .02). The normal nocturnal increase of circulating leptin concentrations observed in control subjects was completely absent in burn patients. Cortisol levels were higher in burn patients (20.4 +/- 1.0 mg/dL) than in control subjects (9.8 +/- 1.6 mg/dL, P < .0001) and the normal circadian decrease of circulating cortisol levels was markedly blunted in burn patients. Plasma cortisol did not correlate with circulating leptin levels. Plasma insulin and plasma glucose levels were significantly elevated in burn patients and the insulin:glucose ratio was dramatically increased compared with control subjects. Patients with burn injuries exhibited significantly decreased circulating leptin levels. This decrease may be the result of marked insulin resistance, as suggested by the elevated insulin to glucose ratio in burn patients. The loss of the diurnal pattern in burn patients is likely to result from the continuous nutritional supplementation. Because low leptin levels should induce appetite, burn-related anorexia is probably controlled by other regulatory systems