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Creators/Authors contains: "Ge, Yuan"

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  1. Abstract

    Cobamides, a class of essential coenzymes synthesized only by a subset of prokaryotes, are model nutrients in microbial interaction studies and play significant roles in global ecosystems. Yet, their spatial patterns and functional roles remain poorly understood. Herein, we present an in-depth examination of cobamide-producing microorganisms, drawn from a comprehensive analysis of 2862 marine and 2979 soil metagenomic samples. A total of 1934 nonredundant metagenome-assembled genomes (MAGs) potentially capable of producing cobamides de novo were identified. The cobamide-producing MAGs are taxonomically diverse but habitat specific. They constituted only a fraction of all the recovered MAGs, with the majority of MAGs being potential cobamide users. By mapping the distribution of cobamide producers in marine and soil environments, distinct latitudinal gradients were observed: the marine environment showed peak abundance at the equator, whereas soil environments peaked at mid-latitudes. Importantly, significant and positive links between the abundance of cobamide producers and the diversity and functions of microbial communities were observed, as well as their promotional roles in essential biogeochemical cycles. These associations were more pronounced in marine samples than in soil samples, which suggests a heightened propensity for microorganisms to engage in cobamide sharing in fluid environments relative to the more spatially restricted soil environment. These findings shed light on the global patterns and potential ecological roles of cobamide-producing microorganisms in marine and soil ecosystems, enhancing our understanding of large-scale microbial interactions.

     
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  2. Abstract

    Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual‐purpose model for simultaneously estimating students' overall ability and the presence and absence of misconceptions. The expectation‐maximization algorithm was developed to estimate the model parameters. A simulation study was conducted to evaluate to what extent the parameters can be accurately recovered under varied conditions. A set of real data in science education was also analyzed to examine the viability of the proposed model in practice.

     
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