Tumor necrosis factor receptor-associated factor (TRAF) proteins, originally identified in mammals, have since been found in most plants. TRAF proteins in plants have been shown to be involved in cellular autophagy, immunity, drought resistance, and ABA induction. However, the role in regulating sucrose and starch metabolism has not been reported. In this study, we confirmed that
Background The Albumin-Bilirubin (ALBI) score and grade are widely used to stratify patients with primary biliary cholangitis (PBC) into different disease statuses and risk levels. Recent studies have increasingly highlighted the role of gut microbiota in autoimmune liver diseases. This study aimed to investigate the differences in gut microbiota among PBC patients with varying ALBI grades. Methods Clinical data and stool samples were collected from outpatient and inpatient PBC patients between 2019 and 2022. Gut microbiota profiles were obtained using 16S rDNA sequencing of stool samples. We analyzed alpha diversity, beta diversity, LEfSe analysis and pathway function prediction. Additionally, various machine learning methods—including random forest (RF), lasso, gradient boosting machine (GBM) and support vector machine (SVM)—were employed to identify key features and to build and validate predictive models using bootstrap techniques. Results Clinical characteristics of ALBI grade 1 patients were comparatively better than those of ALBI grade 2 and 3 patients, including multiple laboratory indices. Gut microbiota analysis revealed that species richness and balance were higher in ALBI grade 1 patients. Both the comparison of the most abundant genera and the linear discriminant analysis (LDA) in LEfSe demonstrated that Lachnospira had a higher abundance and better discriminative ability in ALBI grade 1. Pathway function prediction indicated that sulfur metabolism was upregulated in higher ALBI grades. Furthermore, RF identified 10 specific genera, which were then used to build and validate models for discriminating PBC patients according to their ALBI grades. All three models, developed using different machine learning methods, demonstrated good discrimination ability (mean AUC 0.75–0.80). Conclusion This study highlights significant differences in gut microbiota profiles among PBC patients with different ALBI grades. The increased abundance of Lachnospira and upregulation of sulfur metabolism pathways are notable in patients with lower ALBI grades. The machine learning models developed based on gut microbiota features offer promising tools for discriminating between PBC patients with varying disease severities, which could enhance the precision of treatment strategies.
Pressure-induced evolution of the bandgap, structural phase transitions, and changes in exciton effects can significantly modulate the luminescent properties of lead halide perovskites (LHPs) quantum dots (QDs). Previous studies have indicated that CH3NH3PbBr3 (MAPbBr3) QDs, as a typical low-dimensional LHP material, and their photoluminescence (PL) at ambient conditions are mainly attributed to the radiative recombination of the initially generated excitons upon light absorption and the excitons involving surface states, while the existence of biexciton radiative recombination remains unclear. In this work, we confirm the existence of biexciton radiative recombination in MAPbBr3 QDs at ambient conditions through experimental measurements of excitation-intensity-dependent PL and time-resolved PL (TRPL) spectra at ambient conditions as well as temperature-dependent PL spectra (80–260 K) at ambient pressure. We also establish that the PL of MAPbBr3 QDs primarily originates from the combined effects of three excitons radiative recombination physical processes: biexcitons, initially generated excitons upon light absorption, and excitons involving surface states. Furthermore, through in situ high-pressure PL, absorption, and TRPL spectroscopy measurements, we reveal that the recombination lifetimes and the relative contributions of these three excitons in MAPbBr3 QDs are all subject to alteration in response to the pressure-induced bandgap evolution and the structural phase transitions, thereby modulating their PL emission characteristics.
Abstract Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) has emerged as a powerful technique for investigating open chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remain challenging due to its sparsity and noise. Genome Foundation Models (GFMs), pre-trained on massive DNA sequences, have proven effective at genome analysis. Given that open chromatin regions (OCRs) harbour salient sequence features, we hypothesize that leveraging GFMs’ sequence embeddings can improve the accuracy and generalizability of scATAC-seq modeling. Here, we introduce the Genome Foundation Embedded Topic Model (GFETM), an interpretable deep learning framework that combines GFMs with the Embedded Topic Model (ETM) for scATAC-seq data analysis. By integrating the DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state specific TF activity both with zero-shot inference and attention mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes.
Abstract Pressure‐modulated self‐trapped exciton (STE) emission mechanism in all‐inorganic lead‐free metal halide double perovskites characterized by large Stokes‐shifted broadband emission, has attracted much attention across various fields such as optics, optoelectronics, and biomedical sciences. Here, by employing the all‐inorganic lead‐free metal halide double perovskite Cs 2 TeCl 6 as a paradigm, the authors elucidate that the performance of STE emission can be modulated by pressure, attributable to the pressure‐induced evolution of the electronic state (ES). Two ES transitions happen at pressures of 1.6 and 5.8 GPa, sequentially. The electronic behaviors of Cs 2 TeCl 6 can be jointly modulated by both pressure and ES transitions. When the pressure reaches 1.6 GPa, the Huang–Rhys factor S, indicative of the strength of electron‐phonon coupling, attains an optimum value of ≈12.0, correlating with the pressure‐induced photoluminescence (PL) intensity of Cs 2 TeCl 6 is 4.8‐fold that of its PL intensity under ambient pressure. Through analyzing the pressure‐dependent STE dynamic behavioral changes, the authors have revealed the microphysical mechanism underlying the pressure‐modulated enhancement and quenching of STE emission in Cs 2 TeCl 6 .
The BV antibody of Guangxi Primate laboratory animals were detected by the antigen of HSV 1、BV and BV DIA dot.The results showed by HSV\-1 was quite different from those detected by BV,BV DIA dot.The differences between them were very significant \%(P0.01)\%,but the difference between BV and BV DIA dot,were not significant \%(P0.05)\%.After 2,4 and 6\|month breeding,we used the same antigen to exam those monkeys with negative BV antibody detected by HSV\-1 and BV,BV DIA dot.The results showed that the turning positive rate of BV antibody detected by HSV\-1 was quite different from those of the two others \%(P0.01)\%.Since the mid of 1999,we have exported 1389 BV negative monkeys detected by BV antigen to USA,Holland,Japan,and German soon,and all of them meet the requirements of the guests.
SUMMARY Chemical compositions of crops are of great agronomical importance, as crops serve as resources for nutrition, energy, and medicines for human and livestock. For crop metabolomics research, the lack of crop reference metabolome and high‐quality reference compound mass spectra, as well as utilities for metabolic profiling, has hindered the discovery and functional study of phytochemicals in crops. To meet these challenging needs, we have developed the Crop Metabolome database (abbreviated as CropMetabolome) that is dedicated to the construction of crop reference metabolome, repository, and dissemination of crop metabolomic data, and profiling and analytic tools for metabolomics research. CropMetabolome contains a metabolomics database for more than 50 crops (belonging to eight categories) that integrated self‐generated raw mass spectral data and public‐source datasets. The reference metabolome for 59 crop species was constructed, which have functions that parallel those of reference genome in genomic studies. CropMetabolome also contains ‘Standard compound mass spectral library’, ‘Flavonoids library’, ‘Pesticide library’, and a set of related analytical tools that enable metabolic profiling based on a reference metabolome (CropRefMetaBlast), annotation and identification of new metabolites (CompoundLibBlast), deducing the structure of novel flavonoid derivatives (FlavoDiscover), and detecting possible residual pesticides in crop samples (PesticiDiscover). In addition, CropMetabolome is a repository to share and disseminate metabolomics data and a platform to promote collaborations to develop reference metabolome for more crop species. CropMetabolome is a comprehensive platform that offers important functions in crop metabolomics research and contributes to improve crop breeding, nutrition, and safety. CropMetabolome is freely available at https://www.cropmetabolome.com/ .
Objective To investigate the epidemiological distribution and gene evolution of influenza A (H3N2) virus in Hubei province from 2017 to 2020. Methods Based on the influenza etiological surveillance data in Hubei, the epidemiological distribution of influenza A (H3N2) virus in Hubei during 2017–2020 was analyzed. A total of 38 strains of influenza A (H3N2) virus sent by the influenza surveillance network laboratories in Hubei during 2017–2020 were selected for gene sequencing according to the annual distribution and area distribution. The amino acid sequences of HA and NA were obtained and their antigens were analyzed, the changes of amino acid sites in clusters were analyzed, and gene evolution and 3D modeling analyses were conducted. Results There were three detection peaks of influenza A (H3N2) virus in Hubei from 2017 to 2020. The first peak was predominated by 3C. 2a1 virus cluster, the second peak was predominated by 3C.2a1b +T131K virus cluster and the third peak was predominated by 3C.2a1b +T135K virus cluster. 3C.2a1b +T131K virus cluster and 3C.2a1b +T135K virus cluster had 8 different amino acid mutation sites on 3 antigenic determinants of HA protein. There were significant differences among sites 50, 131, 135, 140 of 3C.2a1b +T131K virus cluster and 3C.2a1b +T135K virus cluster in the three-dimensional simulation structure diagram. Conclusion The cluster of influenza A (H3N2) virus in Hubei evolved continuously from 2017 to 2020. Enhancement of evolutionary surveillance and antigen mutation analysis of the recent cluster of 3C. 2a1b +T135K virus would improve the epidemiological and gene evolution surveillance of influenza viruses in Hubei.
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