Localized Scarlatiniform Rash of the Ears and Antecubital Fossa in COVID-19.

The worldwide spread of the novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a global pandemic since its identification in Wuhan, China in December 2019. Few cases of COVID-19-associated dermatologic manifestations have been reported in the literature to date. This report describes the clinical features of a localized pruritic scarlatiniform rash of the ears and antecubital fossa on defervescence in a 29-year-old patient with COVID-19.

Prediction of aquatic toxicity mode of action using linear discriminant and random forest models.

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA.

Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis.

The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets.

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models.

Precursors to dinitrogen reduction: structures and reactivity of trans-[Fe(DMeOPrPE)2(eta(2)-H2)H]+ and trans-[Fe(DMeOPrPE)2(N2)H]+.

trans-[Fe(DMeOPrPE)(2)(H(2))H](+) and trans-[Fe(DMeOPrPE)(2)(N(2))H](+) (DMeOPrPE = 1,2-bis(dimethoxypropylphosphino)ethane) were synthesized and their structures determined by X-ray crystallography. These complexes are important species in a dinitrogen reduction scheme involving protonation of an iron(0) dinitrogen complex to produce ammonia. The rates of substitution of the coordinated H(2) and N(2) molecules with acetonitrile were monitored in a variety of organic solvents.

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals.

A hierarchical clustering methodology for the estimation of toxicity.

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices).

Addition of Cl2C: to (-)-O-menthyl acrylate under sonication-phase-transfer catalysis. Efficient synthesis of (+)- and (-)-(2-chlorocyclopropyl)methanol.

[reaction: see text] Dichlorocyclopropanation of (-)-O-menthyl acrylate under conditions of phase-transfer catalysis (CHCl3, KOH, tetramethylammonium bromide), with sonication, gives excellent yields (85-94%) of the corresponding dichlorocyclopropanecarboxylate ester compared to thermal conditions (90 degrees C, 56%). No diastereoselectivity was observed, but one isomer was isolated pure by fractional crystallization. The measured kinetic isotope effect (initial rate (CHCl3)/rate (CDCl3) approximately 1.

Prediction of growth and biotransformation rates of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the presence of barium.

The biotransformation of explosives has been investigated by many researchers. Bioremediation of soil and water contaminated with hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is becoming the method of choice for clean-up of a variety of sites. In this study, we investigated biotransformation of RDX in the presence of barium.

Life cycle assessment of gasoline blending options.

A life cycle assessment has been done to compare the potential environmental impacts of various gasoline blends that meet octane and vapor pressure specifications. The main blending components of alkylate, cracked gasoline, and reformate have different octane and vapor pressure values as well as different potential environmental impacts. Because the octane and vapor pressure values are nonlinearly related to impacts, the results of this study show that some blends are better for the environment than others.