Notes

Peak performance Tactics Utilised by Physical exercise Professionals: A Hidden Report Investigation.
CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical "doses" of the HealtheRx shared their information with others ("social doses"). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population ("agents") using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agenformation spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.
An evaluation of postexposure prophylaxis (PEP) surveillance has not been conducted in over 10 years in the United States. An accurate assessment would be important to understand current rabies trends and inform public health preparedness and response to human rabies.

To understand PEP surveillance, we sent a survey to public health leads for rabies in 50 U.S. states, Puerto Rico, Washington DC, Philadelphia, and New York City. Of leads from 54 jurisdictions, 39 (72%) responded to the survey; 12 reported having PEP-specific surveillance, five had animal bite surveillance that included data about PEP, four had animal bite surveillance without data about PEP, and 18 (46%) had neither. Although 12 jurisdictions provided data about PEP use, poor data quality and lack of national representativeness prevented use of this data to derive a national-level PEP estimate. We used national-level and state specific data from the Healthcare Cost & Utilization Project (HCUP) to estimate the number of people who receiistration of PEP such as syndromic surveillance or identification of sentinel states should be considered to obtain an accurate assessment.Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arlial cell signaling to systemic immune models.Florian Naudet and co-authors discuss strengthening requirements for sharing clinical trial data.
Through a multisectoral approach, the DREAMS Partnership aimed to reduce HIV incidence among adolescent girls and young women (AGYW) by 40% over 2 years in high-burden districts across sub-Saharan Africa. DREAMS promotes a combination package of evidence-based interventions to reduce individual, family, partner, and community-based drivers of young women's heightened HIV risk. We evaluated the impact of DREAMS on HIV incidence among AGYW and young men in 2 settings.

We directly estimated HIV incidence rates among open population-based cohorts participating in demographic and HIV serological surveys from 2006 to 2018 annually in uMkhanyakude (KwaZulu-Natal, South Africa) and over 6 rounds from 2010 to 2019 in Gem (Siaya, Kenya). We compared HIV incidence among AGYW aged 15 to 24 years before DREAMS and up to 3 years after DREAMS implementation began in 2016. We investigated the timing of any change in HIV incidence and whether the rate of any change accelerated during DREAMS implementation. Comparable analuch a complex HIV prevention intervention and to help accelerate reductions in HIV incidence among young women.
Substantial declines in HIV incidence among AGYW were observed, but most began before DREAMS introduction and did not accelerate in the first 3 years of DREAMS implementation. Like the declines observed among young men, they are likely driven by earlier and ongoing investments in HIV testing and treatment. Longer-term implementation and evaluation are needed to assess the impact of such a complex HIV prevention intervention and to help accelerate reductions in HIV incidence among young women.Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However, reliably inferring the epidemical dynamics by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address this problem with a novel combination of epidemiological modeling with specialized neural networks. Our approach entails two computational phases In an initial training phase, a mathematical model describing the epidemic is used as a coach for a neural network, which acquires global knowledge about the full range of possible disease dynamics. In the subsequent inference phase, the trained neural network processes the observed data of an actual outbreak and infers the parameters of the model in order to realistically reproduce the observed dynamics and reliably predict future progression. Ipatasertib With its flexible framework, our simulation-based approach is applicable to a variety of epidemiological models. Moreover, since our method is fully Bayesian, it is designed to incorporate all available prior knowledge about plausible parameter values and returns complete joint posterior distributions over these parameters. Application of our method to the early Covid-19 outbreak phase in Germany demonstrates that we are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.The post-translational addition of SUMO plays essential roles in numerous eukaryotic processes including cell division, transcription, chromatin organization, DNA repair, and stress defense through its selective conjugation to numerous targets. One prominent plant SUMO ligase is METHYL METHANESULFONATE-SENSITIVE (MMS)-21/HIGH-PLOIDY (HPY)-2/NON-SMC-ELEMENT (NSE)-2, which has been connected genetically to development and endoreduplication. Here, we describe the potential functions of MMS21 through a collection of UniformMu and CRISPR/Cas9 mutants in maize (Zea mays) that display either seed lethality or substantially compromised pollen germination and seed/vegetative development. RNA-seq analyses of leaves, embryos, and endosperm from mms21 plants revealed a substantial dysregulation of the maize transcriptome, including the ectopic expression of seed storage protein mRNAs in leaves and altered accumulation of mRNAs associated with DNA repair and chromatin dynamics. Interaction studies demonstrated that MMS21 associates in the nucleus with the NSE4 and STRUCTURAL MAINTENANCE OF CHROMOSOMES (SMC)-5 components of the chromatin organizer SMC5/6 complex, with in vitro assays confirming that MMS21 will SUMOylate SMC5. Comet assays measuring genome integrity, sensitivity to DNA-damaging agents, and protein versus mRNA abundance comparisons implicated MMS21 in chromatin stability and transcriptional controls on proteome balance. Taken together, we propose that MMS21-directed SUMOylation of the SMC5/6 complex and other targets enables proper gene expression by influencing chromatin structure.A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at https//bioconductor.org/packages/NanoMethViz.World Health Organization goals against soil-transmitted helminthiases (STH) are pointing towards seeking their elimination as a public health problem reducing to less than 2% the proportion of moderate and heavy infections. Some regions are reaching WHO goals, but transmission could rebound if strategies are discontinued without an epidemiological evaluation. For that, sensitive diagnostic methods to detect low intensity infections and localization of ongoing transmission are crucial. In this work, we estimated and compared the STH infection as obtained by different diagnostic methods in a low intensity setting. We conducted a cross-sectional study enrolling 792 participants from a district in Mozambique. Two stool samples from two consecutive days were collected from each participant. Samples were analysed by Telemann, Kato-Katz and qPCR for STH detection. We evaluated diagnostic sensitivity using a composite reference standard. By geostatistical methods, we estimated neighbourhood prevalence of at least one STH infection for each diagnostic method.
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