Search

ABSTRACT Gene innovation plays an essential role in trait evolution. Rhizobial symbioses, the most important N2‐fixing agent in agricultural systems that exists mainly in Leguminosae, is one of the most attractive evolution events. However, the gene innovations underlying Leguminosae root nodule symbiosis (RNS) remain largely unknown. Here, we investigated the gene gain event in Leguminosae RNS evolution through comprehensive phylogenomic analyses. We revealed that Leguminosae‐gain genes were acquired by gene duplication and underwent a strong purifying selection. Kyoto Encyclopedia of Genes and Genomes analyses showed that the innovated genes were enriched in flavonoid biosynthesis pathways, particular downstream of chalcone synthase (CHS). Among them, Leguminosae‐gain type Ⅱ chalcone isomerase (CHI) could be further divided into CHI1A and CHI1B clades, which resulted from the products of tandem duplication. Furthermore, the duplicated CHI genes exhibited exon–intron structural divergences evolved through exon/intron gain/loss and insertion/deletion. Knocking down CHI1B significantly reduced nodulation in Glycine max (soybean) and Medicago truncatula; whereas, knocking down its duplication gene CHI1A had no effect on nodulation. Therefore, Leguminosae‐gain type Ⅱ CHI participated in RNS and the duplicated CHI1A and CHI1B genes exhibited RNS functional divergence. This study provides functional insights into Leguminosae‐gain genetic innovation and sub‐functionalization after gene duplication that contribute to the evolution and adaptation of RNS in Leguminosae. [ABSTRACT FROM AUTHOR]

Previous studies have shown a bidirectional communication between human gut microbiota and the brain, known as the microbiota–gut–brain axis (MGBA). The MGBA influences the host's nervous system development, emotional regulation, and cognitive function through neurotransmitters, immune modulation, and metabolic pathways. Factors like diet, lifestyle, genetics, and environment shape the gut microbiota composition together. Most research have explored how gut microbiota regulates host physiology and its potential in preventing and treating neurological disorders. However, the individual heterogeneity of gut microbiota, strains playing a dominant role in neurological diseases, and the interactions of these microbial metabolites with the central/peripheral nervous systems still need exploration. This review summarizes the potential role of gut microbiota in driving neurodevelopmental disorders (autism spectrum disorder and attention deficit/hyperactivity disorder), neurodegenerative diseases (Alzheimer's and Parkinson's disease), and mood disorders (anxiety and depression) in recent years and discusses the current clinical and preclinical gut microbe‐based interventions, including dietary intervention, probiotics, prebiotics, and fecal microbiota transplantation. It also puts forward the current insufficient research on gut microbiota in neurological disorders and provides a framework for further research on neurological disorders. [ABSTRACT FROM AUTHOR]

Based on the stakeholder theory, this study investigates the relationship between green innovation and supply chain financing using the data of 3490 Chinese listed firms from 2012 to 2019. The results show that green innovation of firms could promote their supply chain financing. And the channel mechanism test indicates that green innovation could improve the peer recognition gained by firms from the industry, thus it would be more convenient for green-oriented firms to obtain financing along the supply chain, especially SOEs. Moreover, after launch of the Green Credit Guideline, the positive relationship between green innovation and supply chain financing became more significant. These findings remain consistent after robustness tests including instrumental variables (IV), propensity score matching (PSM) and replacing variable metrics. Further results present that green innovation in non-heavy pollution firms and firms with high-level environmental disclosure can significantly benefit supply chain financing. Our findings have important implications on how firms’ green efforts affect their short-time financing ability through the supply chain.

Previous studies have found that drivers' physiological conditions can deteriorate under noise conditions, which poses a potential hazard when driving. As a result, it is crucial to identify the status of drivers when exposed to different noises. However, such explorations are rarely discussed with short-term physiological indicators, especially for rail transit drivers. In this study, an experiment involving 42 railway transit drivers was conducted with a driving simulator to assess the impact of noise on drivers' physiological responses. Considering the individuals' heterogeneity, this study introduced drivers' noise annoyance to measure their self-noise-adaption. The variances of drivers' heart rate variability (HRV) along with different noise adaptions are explored when exposed to different noise conditions. Several machine learning approaches (support vector machine, K-nearest neighbour and random forest) were then used to classify their physiological status under different noise conditions according to the HRV and drivers' self-noise adaptions. Results indicate that the volume of traffic noise negatively affects drivers' performance in their routines. Drivers with different noise adaptions but exposed to a fixed noise were found with discrepant HRV, demonstrating that noise adaption is highly associated with drivers' physiological status under noises. It is also found that noise adaption inclusion could raise the accuracy of classifications. Overall, the random forests classifier performed the best in identifying the physiological status when exposed to noise conditions for drivers with different noise adaptions. [ABSTRACT FROM AUTHOR]