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ObjectiveA method of ultra performance liquid chromatography-mass spectrometry （UPLC-MS） was established for the determination of octylphenol ethoxylates （OPEO） and nonylphenol ethoxylates （NPEO） in coating agent.MethodsSamples were extracted with water， and purified by solid phase extraction column. And Thermo Acclaim RSLC 120 C18 （2.1 mm×100 mm， 2.2 μm） chromatographic column was used for separation. Acetonitrile and 0.1% formic acid water were mobile phase， gradient elution and quantification was performed in SIM mode with positive ion mode of electrospray ionization.ResultsOctylphenol ethoxylates （OPEO） and nonylphenol ethoxylates （NPEO） were in the range of 0-400 μg/L， and the linear relationships was good （r2>0.996）. The recoveries at three spiked levels （0.12， 0.24 mg/kg， 1.2 mg/kg） were 88.5%-96.9% with the relative standard deviation of 2.9%-7.0%， and the limits of quantitative was 0.12 mg/kg.ConclusionThe method was simple， rapid and accurate. Its sensitivity， accuracy and precision were suitable for the quantitative analysis and determination of alkylphenol ethoxylates in coating agent.

Abstract Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.

Abstract Background The new field of metagenomics studies microorganism communities by culture-independent sequencing. With the advances in next-generation sequencing techniques, researchers are facing tremendous challenges in metagenomic data analysis due to huge quantity and high complexity of sequence data. Analyzing large datasets is extremely time-consuming; also metagenomic annotation involves a wide range of computational tools, which are difficult to be installed and maintained by common users. The tools provided by the few available web servers are also limited and have various constraints such as login requirement, long waiting time, inability to configure pipelines etc. Results We developed WebMGA, a customizable web server for fast metagenomic analysis. WebMGA includes over 20 commonly used tools such as ORF calling, sequence clustering, quality control of raw reads, removal of sequencing artifacts and contaminations, taxonomic analysis, functional annotation etc. WebMGA provides users with rapid metagenomic data analysis using fast and effective tools, which have been implemented to run in parallel on our local computer cluster. Users can access WebMGA through web browsers or programming scripts to perform individual analysis or to configure and run customized pipelines. WebMGA is freely available at http://weizhongli-lab.org/metagenomic-analysis. Conclusions WebMGA offers to researchers many fast and unique tools and great flexibility for complex metagenomic data analysis.

Pemphigus vulgaris (PV) stands as a rare autoimmune bullous disease, while the precise underlying mechanism remains incompletely elucidated. High‐throughput proteomic methodologies, such as LC‐MS/MS, have facilitated the quantification and characterisation of proteomes from clinical skin samples, enhancing our comprehension of PV pathogenesis. The objective of this study is to elucidate the signalling mechanisms underlying PV through proteomic analysis. Proteins and cell suspension were extracted from skin biopsies obtained from both PV patients and healthy volunteers and subsequently analysed using LC‐MS/MS and scRNA‐seq. Cultured keratinocytes were treated with PV serum, followed by an assessment of protein expression levels using immunofluorescence and western blotting. A total of 880, 605, and 586 differentially expressed proteins (DEPs) were identified between the lesion vs. control, non‐lesion vs. control, and lesion vs. non‐lesion groups, respectively. The oxidative phosphorylation (OXPHOS) pathway showed activation in PV. Keratinocytes are the major cell population in the epidermis and highly expressed ATP5PF, ATP6V1G1, COX6B1, COX6A1, and NDUFA9. In the cellular model, there was a notable increase in the expression levels of OXPHOS‐related proteins (V‐ATP5A, III‐UQCRC2, II‐SDHB, I‐NDUFB8), along with STAT1, p‐STAT1, and p‐JAK1. Furthermore, both the OXPHOS inhibitor metformin and the JAK1 inhibitor tofacitinib demonstrated therapeutic effects on PV serum‐induced cell separation, attenuating cell detachment. Metformin notably reduced the expression of V‐ATP5A, III‐UQCRC2, II‐SDHB, I‐NDUFB8, p‐STAT1, p‐JAK1, whereas tofacitinib decreased the expression of p‐STAT1 and p‐JAK1, with minimal impact on the expression of V‐ATP5A, III‐UQCRC2, II‐SDHB, and I‐NDUFB8. Our results indicate a potential involvement of the OXPHOS and JAK‐STAT1 pathways in the pathogenesis of PV. [ABSTRACT FROM AUTHOR]

The purpose of this study is to utilize three machine learning models—random forest, logistic regression and extreme gradient boosting—to assess the landslide susceptibility of Wushan County and compare the predictive performance of each algorithm. To achieve this, the database was randomly divided into training (80%) and testing (20%) datasets. We considered the impact of soil thickness, which has rarely been explored in previous research. A spatial database of 19 conditioning factors related to the occurrence of a landslide was constructed to develop the landslide susceptibility maps. Thereafter, three models were estimated and compared using metrics such as accuracy, recall, F1 score and area under the receiver operating characteristic curve (AUC). The results show that the random forest model with the testing dataset has higher accuracy (0.848), F1 score (0.740) and AUC (0.904) values. Soil thickness is found to play a significant role in the occurrence of a landslide. The quantitative analysis of landslide susceptibility maps indicates the superiority of the random forest model. Finally, the outcomes of the random forest model incorporating multicollinearity analysis and factor selection were thoroughly discussed. In conclusion, the random forest is the recommended algorithm for evaluating landslide susceptibility to aid in disaster management in Wushan County. [ABSTRACT FROM AUTHOR]

Abstract Background The new field of metagenomics studies microorganism communities by culture-independent sequencing. With the advances in next-generation sequencing techniques, researchers are facing tremendous challenges in metagenomic data analysis due to huge quantity and high complexity of sequence data. Analyzing large datasets is extremely time-consuming; also metagenomic annotation involves a wide range of computational tools, which are difficult to be installed and maintained by common users. The tools provided by the few available web servers are also limited and have various constraints such as login requirement, long waiting time, inability to configure pipelines etc. Results We developed WebMGA, a customizable web server for fast metagenomic analysis. WebMGA includes over 20 commonly used tools such as ORF calling, sequence clustering, quality control of raw reads, removal of sequencing artifacts and contaminations, taxonomic analysis, functional annotation etc. WebMGA provides users with rapid metagenomic data analysis using fast and effective tools, which have been implemented to run in parallel on our local computer cluster. Users can access WebMGA through web browsers or programming scripts to perform individual analysis or to configure and run customized pipelines. WebMGA is freely available at http://weizhongli-lab.org/metagenomic-analysis. Conclusions WebMGA offers to researchers many fast and unique tools and great flexibility for complex metagenomic data analysis.