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β-Substituted Styrenes in Heteroaryl-Directed Hydroalkylative Cross-Couplings: Regio-, Diastereo-, and Enantioselective Formation of β-Stereogenic Tertiary Alcohols
Digital fatigue and academic resilience among university students with grit and flexibility as mediators
General Light-Induced Pd-Catalyzed Allylic C–H Alkylation of Internal Alkenes: Direct Access to Tertiary and Quaternary Carbon Centers
The mechanism of luteolin suppressing pancreatic cancer (PC) via cyclin B1 (CCNB1)-mediated signalling
Mutanobactin D from the Human Microbiome: Chemistry, Biology, and Molecular Dynamics Studies
Optical deformation measurement for thin-walled honeycomb structure with contacting cell walls under loading based on color image processing
A Spin Frustrated Hourglass <b>{Gd</b> <sub> <b>9</b> </sub> <b>}</b> Molecular Nanomagnet with Unusual Magnetocaloric Properties
Assessing behavioral control across the adult lifespan using a novel outcome revaluation task
Abstract While many studies have investigated the effects of aging on cognition, relatively few have examined aging impacts on habitual behavioral control. To assess these relationships, 151 adults across the lifespan (47.09 ± 17.17 years old, range = 19–80) completed a novel instrumental outcome revaluation task, where participants made keyboard responses to abstract stimuli to gain digital currency before completing a revaluation test where the outcome of one stimulus was negatively altered while the other retained its value. Participants also completed questionnaires relating to psychiatric symptoms. Habitual responding was measured in terms of the response rate to the revalued stimulus relative to the response rate to the stimulus that was not revalued. There were significant positive effects of obsessive-compulsive symptoms and significant negative effects of depressive symptoms on habitual behavior. In addition, results revealed a modest effect of chronological age on habitual behavior These results indicate subtle changes in behavioral control across the adult lifespan and support previous work showing that certain psychological measures including obsessive-compulsive symptoms are associated with increased habitual responding.
Supramolecular Assembly of a Macrocyclic Rhodium(I) Isocyanide Complex with Long-Lived Near-Infrared Luminescence
Trends of ciprofloxacin use in Kazakhstan, impacts of COVID-19, and strategies for AMR control
Scalable, Universal In Situ Self-Heating Chemical Vapor Deposition Strategy for High-Quality Thick Turbostratic Graphene via Combined Twist–Tilt Configuration Engineering
The mediating effect of urban water system climate resilience in the impact of sponge city pilot policy on ecological welfare
Genomic GC bias correction improves species abundance estimation from metagenomic data
Abstract Metagenomic sequencing measures the species composition of microbial communities and has revealed the crucial role of microbiomes in the etiology of a range of diseases such as colorectal cancer. Quantitative comparisons of microbial communities are, however, affected by GC-content-dependent biases. Here, we present GuaCAMOLE, a computational method to detect and remove GC bias from metagenomic sequencing data. The algorithm relies on comparisons between individual species in a single sample to estimate the sequencing efficiency at levels of GC content, and outputs unbiased species abundances. GuaCAMOLE thus works regardless of the specific amount or direction of GC-bias present in the data and does not rely on calibration experiments or multiple samples. Applying our algorithm to 3435 gut microbiomes of colorectal cancer patients from 33 individual studies reveals that the type and severity of GC bias vary considerably between studies. In many studies, we observe a clear bias against GC-poor species in the abundances reported by existing methods. GuaCAMOLE successfully removes this bias and corrects the abundance of clinically relevant GC-poor species such as F. nucleatum (28% GC) by up to a factor of two. GuaCAMOLE thus contributes to a better quantitative understanding of microbial communities by improving the accuracy and comparability of species abundances across experimental setups.