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ABSTRACT In starvingBacillus subtilisbacteria,the initiation of two survival programs—biofilm formation and sporulation—is controlled by the same phosphorylated master regulator, Spo0A~P. Its gene,spo0A,is transcribed from two promoters, Pvand Ps,that are, respectively, regulated by RNA polymerase (RNAP) holoenzymes bearing σ^Aand σ^H. Notably, transcription is directly autoregulated by Spo0A~P binding sites known as 0A1, 0A2, and 0A3 box, located in between the two promoters. It remains unclear whether, at the onset of starvation, these boxes activate or repressspo0Aexpression, and whether the Spo0A~P transcriptional feedback plays a role in the increase inspo0Aexpression. Based on the experimental data of the promoter activities under systematic perturbation of the promoter architecture, we developed a biophysical model of transcriptional regulation ofspo0Aby Spo0A~P binding to each of the 0A boxes. The model predicts that Spo0A~P binding to its boxes does not affect the RNAP recruitment to the promoters but instead affects the transcriptional initiation rate. Moreover, the effects of Spo0A~P binding to 0A boxes are mainly repressive and saturated early at the onset of starvation. Therefore, the increase inspo0Aexpression is mainly driven by the increase in RNAP holoenzyme levels. Additionally, we reveal that Spo0A~P affinity to 0A boxes is strongest at 0A3 and weakest at 0A2 and that there are attractive forces between the occupied 0A boxes. Our findings, in addition to clarifying how the sporulation master regulator is controlled, offer a framework to predict regulatory outcomes of complex gene-regulatory mechanisms. IMPORTANCECell differentiation is often critical for survival. In bacteria, differentiation decisions are controlled by transcriptional master regulators under transcriptional feedback control. Therefore, understanding how master regulators are transcriptionally regulated is required to understand differentiation. However, in many cases, the underlying regulation is complex, with multiple transcription factor binding sites and multiple promoters, making it challenging to dissect the exact mechanisms. Here, we address this problem for theBacillus subtilismaster regulator Spo0A. Using a biophysical model, we quantitatively characterize the effect of individual transcription factor binding sites on eachspo0Apromoter. Furthermore, the model allows us to identify the specific transcription step that is affected by transcription factor binding. Such a model is promising for the quantitative study of a wide range of master regulators involved in transcriptional feedback. 

We use mathematical modeling and simulation to evaluate the safety of in-person instruction in university classrooms under different interventions. We find that, for the Delta variant of COVID-19, requiring masking when more than 90% of students are vaccinated is safe and easy to implement, without needing to assign fixed seats or improving the ventilation in classrooms. Our model supported Cornell University to safely conduct in-person instruction in fall 2021. 

BackgroundCOVID-19 is constantly evolving, and highly populated communities consist of many different characteristics that may contribute to COVID-19 health outcomes. Therefore, we aimed to (1) quantify the relationships between county characteristics and severe and non-severe county-level health outcomes related to COVID-19. We also aimed to (2) compare these relationships across time periods where the Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants were dominant in the U.S. MethodsWe used multiple regression to measure the strength of relationships between healthcare outcomes and county characteristics in the 50 most populous U.S. counties. ResultsWe found many different significant predictors including the proportion of a population vaccinated, median household income, population density, and the proportion of residents aged 65+, but mainly found that socioeconomic factors and the proportion of a population vaccinated play a large role in the dynamics of the spread and severity of COVID-19 in communities with high populations. DiscussionThe present study shines light on the associations between public health outcomes and county characteristics and how these relationships change throughout Delta and Omicron’s dominance. It is important to understand factors underlying COVID-19 health outcomes to prepare for future health crises. 

Laboratory tests seeking to improve detection of COVID-19 have been widely developed by laboratories and commercial companies. This review provides an overview of molecular and antigen tests, presents the sensitivity and specificity for 329 assays that have received US FDA Emergency Use Authorization and evaluates six sample collection methods – nasal, nasopharyngeal, oropharyngeal swabs, saliva, blood and stool. Molecular testing is preferred for diagnosis of COVID-19, but negative results do not always rule out the presence of infection, especially when clinical suspicion is high. Sensitivity and specificity ranged from 88.1 to 100% and 88 to 100%, respectively. Antigen tests may be more easy to use and rapid. However, they have reported a wide range of detection sensitivities from 16.7 to 85%, which may potentially yield many false-negative results. 

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities’ unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University’s decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.