Inferences Drawn from a Risk Assessment Compared Directly with a Randomized Trial of a Home Drinking Water Intervention

Risk assessments and intervention trials have been used by the U.S. Environmental Protection Agency to estimate drinking water health risks. Seldom are both methods used concurrently. Between 2001 and 2003, illness data from a trial were collected simultaneously with exposure data, providing a unique opportunity to compare direct risk estimates of waterborne disease from the intervention trial with indirect estimates from a risk assessment. Comparing the group with water treatment (active) with that without water treatment (sham), the estimated annual attributable disease rate (cases per 10,000 persons per year) from the trial provided no evidence of a significantly elevated drinking water risk [attributable risk = −365 cases/year, sham minus active; 95% confidence interval (CI), −2,555 to 1,825]. The predicted mean rate of disease per 10,000 persons per person-year from the risk assessment was 13.9 (2.5, 97.5 percentiles: 1.6, 37.7) assuming 4 log removal due to viral disinfection and 5.5 (2.5, 97.5 percentiles: 1.4, 19.2) assuming 6 log removal. Risk assessments are important under conditions of low risk when estimates are difficult to attain from trials. In particular, this assessment pointed toward the importance of attaining site-specific treatment data and the clear need for a better understanding of viral removal by disinfection. Trials provide direct risk estimates, and the upper confidence limit estimates, even if not statistically significant, are informative about possible upper estimates of likely risk. These differences suggest that conclusions about waterborne disease risk may be strengthened by the joint use of these two approaches

Injury rates and injury risk factors among federal bureau of investigation new agent trainees

<p>Abstract</p> <p>Background</p> <p>A one-year prospective examination of injury rates and injury risk factors was conducted in Federal Bureau of Investigation (FBI) new agent training.</p> <p>Methods</p> <p>Injury incidents were obtained from medical records and injury compensation forms. Potential injury risk factors were acquired from a lifestyle questionnaire and existing data at the FBI Academy.</p> <p>Results</p> <p>A total of 426 men and 105 women participated in the project. Thirty-five percent of men and 42% of women experienced one or more injuries during training. The injury incidence rate was 2.5 and 3.2 injuries/1,000 person-days for men and women, respectively (risk ratio (women/men) = 1.3, 95% confidence interval = 0.9-1.7). The activities most commonly associated with injuries (% of total) were defensive tactics training (58%), physical fitness training (20%), physical fitness testing (5%), and firearms training (3%). Among the men, higher injury risk was associated with older age, slower 300-meter sprint time, slower 1.5-mile run time, lower total points on the physical fitness test (PFT), lower self-rated physical activity, lower frequency of aerobic exercise, a prior upper or lower limb injury, and prior foot or knee pain that limited activity. Among the women higher injury risk was associated with slower 300-meter sprint time, slower 1.5-mile run time, lower total points on the PFT, and prior back pain that limited activity.</p> <p>Conclusion</p> <p>The results of this investigation supported those of a previous retrospective investigation emphasizing that lower fitness and self-reported pain limiting activity were associated with higher injury risk among FBI new agents.</p

In vitro nuclear interactome of the HIV-1 Tat protein

<p>Abstract</p> <p>Background</p> <p>One facet of the complexity underlying the biology of HIV-1 resides not only in its limited number of viral proteins, but in the extensive repertoire of cellular proteins they interact with and their higher-order assembly. HIV-1 encodes the regulatory protein Tat (86–101aa), which is essential for HIV-1 replication and primarily orchestrates HIV-1 provirus transcriptional regulation. Previous studies have demonstrated that Tat function is highly dependent on specific interactions with a range of cellular proteins. However they can only partially account for the intricate molecular mechanisms underlying the dynamics of proviral gene expression. To obtain a comprehensive nuclear interaction map of Tat in T-cells, we have designed a proteomic strategy based on affinity chromatography coupled with mass spectrometry.</p> <p>Results</p> <p>Our approach resulted in the identification of a total of 183 candidates as Tat nuclear partners, 90% of which have not been previously characterised. Subsequently we applied <it>in silico </it>analysis, to validate and characterise our dataset which revealed that the Tat nuclear interactome exhibits unique signature(s). First, motif composition analysis highlighted that our dataset is enriched for domains mediating protein, RNA and DNA interactions, and helicase and ATPase activities. Secondly, functional classification and network reconstruction clearly depicted Tat as a polyvalent protein adaptor and positioned Tat at the nexus of a densely interconnected interaction network involved in a range of biological processes which included gene expression regulation, RNA biogenesis, chromatin structure, chromosome organisation, DNA replication and nuclear architecture.</p> <p>Conclusion</p> <p>We have completed the <it>in vitro </it>Tat nuclear interactome and have highlighted its modular network properties and particularly those involved in the coordination of gene expression by Tat. Ultimately, the highly specialised set of molecular interactions identified will provide a framework to further advance our understanding of the mechanisms of HIV-1 proviral gene silencing and activation.</p

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

Prognostic significance of MCM2, Ki-67 and gelsolin in non-small cell lung cancer

BACKGROUND: Uncontrolled proliferation and increased motility are hallmarks of neoplastic cells, therefore markers of proliferation and motility may be valuable in assessing tumor progression and prognosis. MCM2 is a member of the minichromosome maintenance (MCM) protein family. It plays critical roles in the initiation of DNA replication and in replication fork movement, and is intimately related to cell proliferation. Ki-67 is a proliferation antigen that is expressed during all but G(0 )phases of the cell cycle. Gelsolin is an actin-binding protein that regulates the integrity of the actin cytoskeletal structure and facilitates cell motility. In this study, we assessed the prognostic significance of MCM2 and Ki-67, two markers of proliferation, and gelsolin, a marker of motility, in non-small cell lung cancer (NSCLC). METHODS: 128 patients with pathologically confirmed, resectable NSCLC (stage I-IIIA) were included. Immunohistochemistry was utilized to measure the expressions of these markers in formalin-fixed, paraffin-embedded tumor tissues. Staining and scoring of MCM2, Ki-67 and gelsolin was independently performed. Analyses were performed to evaluate the prognostic significance of single expression of each marker, as well as the prognostic significance of composite expressions of MCM2 and gelsolin. Cox regression and Kaplan-Meier survival analysis were used for statistical analysis. RESULTS: Of the three markers, higher levels of gelsolin were significantly associated with an increased risk of death (adjusted RR = 1.89, 95% CI = 1.17–3.05, p = 0.01), and higher levels of MCM2 were associated with a non-significant increased risk of death (adjusted RR = 1.36, 95% CI = 0.84–2.20, p = 0.22). Combined, adjusted analyses revealed a significantly poor prognostic effect for higher expression of MCM2 and gelsolin compared to low expression of both biomarkers (RR = 2.32, 95% CI = 1.21–4.45, p = 0.01). Ki-67 did not display apparent prognostic effect in this study sample. CONCLUSION: The results suggest that higher tumor proliferation and motility may be important in the prognosis of NSCLC, and composite application of biomarkers might be of greater value than single marker application in assessing tumor prognosis

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale