Tangemcneil8369
The effective reproduction number
(
) is a critical measure of epidemic potential.
(
) can be calculated in near real time using an incidence time series and the generation time distribution the time between infection events in an infector-infectee pair. In calculating
(
), the generation time distribution is often approximated by the serial interval distribution the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts.
We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of
(
) for covid-19 in the Greater Toronto Area of Canada using negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.
We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of
(
) due to larger mean, while negative-permitting serial interval distribution caused underestimation of
(
) due to larger variance.
Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating
(
) may result in over or underestimation of transmission potential, respectively.
Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating R e (t) may result in over or underestimation of transmission potential, respectively.We demonstrate the ability of statistical data assimilation (SDA) to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead - and without assumed knowledge of the detection rates. Given a noiseless measurement of the recovered population, excellent estimates of all quantities are obtained using a temporal baseline of 101 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. With low noise added to the recovered population, accurate state estimates require a lengthening of the temporal baseline of measurements. Estimates of all parameters are sensitive to the contamination, highlighting the need for accurate and uniform methods of reporting. Nintedanib The aim of this paper is to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown parameters to a desired precision, in a model with the complexity required to capture important features of the COVID-19 pandemic.Multiple US agencies use acute oral toxicity data in a variety of regulatory contexts. One of the ad-hoc groups that the US Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) established to implement the ICCVAM Strategic Roadmap was the Acute Toxicity Workgroup (ATWG) to support the development, acceptance, and actualisation of new approach methodologies (NAMs). One of the ATWG charges was to evaluate in vitro and in silico methods for predicting rat acute systemic toxicity. Collaboratively, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and the US Environmental Protection Agency (US EPA) collected a large body of rat oral acute toxicity data (~16,713 studies for 11,992 substances) to serve as a reference set to evaluate the performance and coverage of new and existing models as well as build understanding of the inherent variability of the animal data. Here, we focus on evaluating in silico models for predicting the Lethal Dose (LD50) as implemented within two expert systems, TIMES and TEST. The performance and coverage were evaluated against the reference dataset. The performance of both models were similar, but TEST was able to make predictions for more chemicals than TIMES. The subset of the data with multiple (>3) LD50 values was used to evaluate the variability in data and served as a benchmark to compare model performance. Enrichment analysis was conducted using ToxPrint chemical fingerprints to identify the types of chemicals where predictions lay outside the upper 95% confidence interval. Overall, TEST and TIMES models performed similarly but had different chemical features associated with low accuracy predictions, reaffirming that these models are complementary and both worth evaluation when seeking to predict rat LD50 values.Rice (Oryza sativa L.) is one of the most important cereal crops for one third of the world population. However, the grain quality as well as yield of rice is severely affected by various abiotic stresses. Environmental stresses affect the expression of various microRNAs (miRNAs) which in turn negatively regulate gene expression at the post-transcriptional level either by degrading the target mRNA genes or suppressing translation in plants. Plant homeo-domain (PHD) finger proteins are known to be involved in the plant response to salinity stress. In the present study, we identified 44 putative OsPHD finger genes in Oryza sativa Indica, using Ensembl Plants Database. Using computational approach, potential miRNAs that target OsPHD finger genes were identified. Out of the 44 OsPHD finger genes only three OsPHD finger genes i.e., OsPHD2, OsPHD35 and OsPHD11, were found to be targeted by five newly identified putative miRNAs i.e., ath-miRf10010-akr, ath-miRf10110-akr, osa-miR1857-3p, osa-miRf10863-akr, and osa-miRf11806-akr.
