Pacemcintosh1778

The emerging field of hybrid DNA-protein nanotechnology brings with it the potential for many novel materials which combine the addressability of DNA nanotechnology with the versatility of protein interactions. However, the design and computational study of these hybrid structures is difficult due to the system sizes involved. To aid in the design and in silico analysis process, we introduce here a coarse-grained DNA/RNA-protein model that extends the oxDNA/oxRNA models of DNA/RNA with a coarse-grained model of proteins based on an anisotropic network model representation. Fully equipped with analysis scripts and visualization, our model aims to facilitate hybrid nanomaterial design towards eventual experimental realization, as well as enabling study of biological complexes. We further demonstrate its usage by simulating DNA-protein nanocage, DNA wrapped around histones, and a nascent RNA in polymerase.A new polymer acceptor, PS1, was developed by connecting the non-fullerene acceptor building block of dithienothiophen[3,2-b]pyrrolobenzotriazole capped with 3-(dicyanomethylidene)-indan-1-one through a thiophene spacer. The solubilizing alkyl side groups in the central unit enabled PS1 to be readily dissolved in non-chlorinated solvents. By using 2-methyltetrahydrofuran as the processing solvent, the all-polymer solar cell (all-PSC) containing PS1 and a polymer donor PTzBI-oF in the light-harvesting layer exhibited an impressively high power conversion efficiency of 13.8%.A peptoid trimer incorporating terpyridine and ethanol forms an intermolecular cobalt(iii) complex, which performs as a soluble electrocatalyst for water oxidation with a minimal overpotential of 350 mV and a high turnover frequency of 108 s-1. The ethanolic group facilitates water binding thus mimicking an enzymatic second coordination sphere.The present investigation describes the successful molecular modification of a regio- and stereo-specific nitrilase toward rac-ISBN to (S)-CMHA, a critical intermediate in the preparation of optically pure pregabalin. Two hotspots of Trp57 and Val134 were identified based on the classical binding free energy molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation method. Mutants W57F/V134M and W57Y/V134M were successfully obtained with high enantioselectivity (E >300). Furthermore, these two mutants were efficiently capable of kinetic resolution of rac-ISBN to (S)-CMHA, with both exhibiting a high e.e. (>99.9%), as well as conversion ratios of 43.8% and 40.9%, respectively. Docking and molecular dynamics simulation analysis clarified that the underlying mechanisms were related to a DC-S switch and the formation of a hydrogen bond in the active center of nitrilase. The successful utilization of the MM/PBSA method for identifying hotspots that modulate the stereoselectivity in our study could provide guidelines for the molecular modification of nitrilases, and the mutants obtained could be potentially utilized for the industrial preparation of optically pure pregabalin.The exploration of innovative molecular switches has resulted in large developments in the field of molecular electronics. buy Necrostatin 2 Focusing on a single molecular switch with different forms exhibiting different electride features, potassium-atom-doped all-cis 1,2,3,4,5,6-hexafluorocyclohexane K-F6C6H6 was studied theoretically. It was found that an oriented external electric field can drive excess electron transfer from the region outside of the K atom to that outside of F6C6H6. Subsequently, the electride-like molecule K-F6C6H6 (1) switches into the molecular electride K-F6C6H6e- (3) through another electride-like molecule K-F6C6H6 (2). The static first hyperpolarizabilities (β0) are increased over 12- and 5-fold when moving from 1 to 2 and 3, respectively. The rise of each β0 value constitutes an order of magnitude improvement. Between them, the different β0 values suggest that K-F6C6H6 is a good candidate for use as a multiple-response nonlinear optics switch. The order of the β0 values of 1-4 for M-F6C6H6 (M = Li and Na) coincide with that of K-F6C6H6, also exhibiting a switch effect.Nano-emulsions are defined as stable oil droplets sizing below 300 nm. Their singular particularity lies in the loading capabilities of their oily core, much higher than other kinds of carrier. On the other hand, functionalizing the dynamic oil/water interface, to date, has remained a challenge. To ensure the best anchoring of the reactive functions onto the surface of the droplets, we have designed specific amphiphilic polymers (APs) based on poly(maleic anhydride-alt-1-octadecene), stabilizing the nano-emulsions instead of surfactants. Aliphatic C18 chains of the APs are anchored in the droplet core, while the hydrophilic parts of the APs are poly(ethylene glycol) (PEG) chains. In addition, PEG chains are terminated with reactive (i) azide functions in order to prove the concept of the droplet decoration with clickable rhodamine (Rh-DBCO, specifically synthesized for this study), or (ii) biotin functions to verify the potential droplet functionalization with fluorescent streptavidin (streptavidin-AF-488). This study describes AP synthesis, physico-chemical characterization of the functional droplets (electron microscopy), and finally fluorescence labeling and droplet decoration. To conclude, these APs constitute an interesting solution for the stable functionalization of nano-emulsion droplets, paving a new way for the applications of nano-emulsions in targeting drug delivery.Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses needed to address the ongoing spread of COVID-19 in the United States. A data-driven Bayesian single parameter semi-empirical model was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. COVID-19 prevalence is well-approximated by the geometric mean of the positivity rate and the reported case rate. As of December 8, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI) 0.8%-1.9%] and a seroprevalence of 11.1% [CrI 10.1%-12.2%], with state-level prevalence ranging from 0.3% [CrI 0.2%-0.4%] in Maine to 3.0% [CrI 1.1%-5.7%] in Pennsylvania, and seroprevalence from 1.4% [CrI 1.0%-2.0%] in Maine to 22% [CrI 18%-27%] in New York. The use of this simple and easy-to-communicate model will improve the ability to make public health decisions that effectively respond to the ongoing pandemic.

Dr. Weihsueh A. Chiu, is a professor of environmental health sciences at Texas A&M University. He is an expert in data-driven Bayesian modeling of public health related dynamical systems. Dr. Martial L. Ndeffo-Mbah, is an Assistant Professor of Epidemiology at Texas A&M University. He is an expert in mathematical and computational modeling of infectious diseases.

Relying on reported cases and test positivity rates individually can result in incorrect inferences as to the spread of COVID-19, and public health decision-making can be improved by instead using their geometric mean as a measure of COVID-19 prevalence and transmission.

Relying on reported cases and test positivity rates individually can result in incorrect inferences as to the spread of COVID-19, and public health decision-making can be improved by instead using their geometric mean as a measure of COVID-19 prevalence and transmission.The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the US reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real-time represent a non-stationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model (MechBayes) that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes non-parametric modeling of varying transmission rates, non-parametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the US Centers for Disease Control, through the COVID-19 Forecast Hub. We examine the performance relative to a baseline model as well as alternate models submitted to the Forecast Hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes when compared to a baseline model and show that MechBayes ranks as one of the top 2 models out of 10 submitted to the COVID-19 Forecast Hub. Finally, we demonstrate that MechBayes performs significantly better than the classical SEIR model.Recent studies have provided insights into innate and adaptive immune dynamics in coronavirus disease 2019 (COVID-19). Yet, the exact feature of antibody responses that governs COVID-19 disease outcomes remain unclear. Here, we analysed humoral immune responses in 209 asymptomatic, mild, moderate and severe COVID-19 patients over time to probe the nature of antibody responses in disease severity and mortality. We observed a correlation between anti-Spike (S) IgG levels, length of hospitalization and clinical parameters associated with worse clinical progression. While high anti-S IgG levels correlated with worse disease severity, such correlation was time-dependent. Deceased patients did not have higher overall humoral response than live discharged patients. However, they mounted a robust, yet delayed response, measured by anti-S, anti-RBD IgG, and neutralizing antibody (NAb) levels, compared to survivors. Delayed seroconversion kinetics correlated with impaired viral control in deceased patients. Finally, while sera from 89% of patients displayed some neutralization capacity during their disease course, NAb generation prior to 14 days of disease onset emerged as a key factor for recovery. These data indicate that COVID-19 mortality does not correlate with the cross-sectional antiviral antibody levels per se , but rather with the delayed kinetics of NAb production.While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb); http//cpag.oit.duke.edu ) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases.