Search Results (44)

The El Tor biotype of Vibrio cholerae caused the seventh cholera pandemic (7CP). Although V. cholerae variants of this biotype frequently emerge, studies on their microevolution and spatiotemporal transmission in epidemics caused by a single clone are limited. During the cholera outbreak in Sichuan Province, China, in the 1990s, strains belonging to phage type 6 (PT6) but resistant to typing phage VP5 due to a deletion mutation in ompW, which is the gene associated with the VP5 receptor were identified. In this study, we analyzed PT6 strains using genome sequencing to reveal the genomic and transmission characteristics of such a transient phage type in China’s cholera epidemic history. The findings revealed that the PT6 strains formed an independent clone during the four-year epidemic and emerged in wave 2. Most of them carried multiple CTX^classΦ genome copies on chromosome 2 (Chr. 2) and two copies each of RS1^ET and RS1-4** on chromosome 1 (Chr. 1). Frequent cross-regional transmission and local outbreaks within Sichuan Province, China, were revealed for this clone. A variety of spontaneous mutations in the ompW gene, conferring resistance to the VP5 phage, were observed under VP5 infection pressure, showing the incident mutation of OmpW for the survival adaptation of V. cholerae to phage pressure. Therefore, this genomic epidemiological revisit of these distinct phage-resistant phenotype strains reveals their clonal genetic structure, improves our understanding of the spread of V. cholerae by tracking their variation, and assists in epidemic source tracing and disease control. Full article

Background: The peony (Paeonia suffruticosa Andr.), a globally valued woody ornamental species, suffers severe heat-induced floral damage that compromises its horticultural value. While the HSP20 proteins are critical for plant thermotolerance, their genomic organization and regulatory dynamics remain uncharacterized in the peony. This study aims to systematically identify the PsHSP20 genes, resolve their molecular features, and elucidate their heat-responsive expression patterns to enable targeted thermotolerance breeding. Methods: The genome-wide identification employed HMMER and BLASTP searches against the peony genome. The physicochemical properties and protein structures of the gene family were analyzed using online websites, such as Expasy, Plant-mPLoc, and SOPMA. The cis-regulatory elements were predicted using PlantCARE. Expression profiles under different times of 40 °C heat stress were validated by qRT-PCR (p < 0.05). Results: We identified 58 PsHSP20 genes, classified into 11 subfamilies. All members retain the conserved α-crystallin domain, and exhibit predominant nuclear/cytoplasmic localization. Chromosomal mapping revealed uneven distribution without lineage-specific duplications. The promoters were enriched in stress-responsive elements (e.g., HSE, ABRE) and in 24 TF families. The protein networks linked 13 PsHSP20s to co-expressed partners in heat response (GO:0009408) and ER protein processing (KEGG:04141). Transcriptomics demonstrated rapid upregulation of 48 PsHSP20s within 2 h of heat exposure, with PsHSP20-12, -34, and -51 showing the highest induction (>15-fold) at 6 h/24 h. Conclusions: This first genome-wide study resolves the architecture and heat-responsive dynamics of the PsHSP20 family. The discovery of early-induced genes (PsHSP20-12/-34/-51) provides candidates for thermotolerance enhancement. These findings establish a foundation for molecular breeding in the peony. Full article

With the rapid development of e-commerce and the logistics industry, the importance of logistics packaging defect detection as a key link in product quality control is becoming increasingly prominent. However, existing target detection models often face the problems of difficulty in improving detection accuracy and high model complexity when dealing with small-scale targets in logistics packaging. For this reason, an improved target detection model, DScanNet, is proposed in this paper. To address the problem that the model’s detailed feature extraction for small target defects is not sufficient and thus leads to low detection accuracy, the MEFE module, the local feature extraction module (LFEM Block), and the PCR module of the multi-scale convolution and feature enhancement strategy are proposed to enhance the model’s capability of capturing defective features and focusing on specific features, and to improve the detection accuracy. To address the problem of excessive model complexity, a Mamba module incorporating a channel attention mechanism is proposed to optimize the model via its linear complexity. Through experiments on its own dataset, BIGC-LP, DScanNet achieves a high accuracy of 96.8% on the defect detection task compared with the current mainstream detection algorithms, while the number of model parameters and the computational volume are effectively controlled. Full article

To address the limitations of traditional methods in modeling complex nonlinear relationships in horizontal in situ stress prediction for shale reservoirs, this study proposes an integrated framework that combines well logging interpretation with machine learning to accurately predict horizontal in situ stress in shale reservoirs. Based on the logging data from five wells in the Luzhou Block of the Sichuan Basin (16,000 samples), Recursive Feature Elimination (RF-RFE) was used to identify nine key factors, including Stoneley wave slowness and caliper, from 30 feature parameters. Bayesian optimization was employed to fine-tune the hyperparameters of the XGBoost model globally. Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). It achieves R² values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. This framework enhances the model’s adaptability to complex geological conditions through multi-well data training and eliminating redundant features, providing a reliable tool for hydraulic fracturing design and wellbore stability assessment in shale gas development. Full article

Owing to the insufficient understanding of the mechanical properties and damage mechanisms of concrete-rock bonding interfaces in dam foundations, this study establishes a refined three-dimensional simulation model for direct shear tests of concrete-rock bonding interfaces based on in-situ direct shear tests conducted at a reservoir. The damage evolution process and failure mechanisms of the concrete-rock interface under different loading conditions are investigated. The results indicate that under varying normal stresses, the shear stress-shear displacement curve exhibits an initial increase followed by a gradual decrease, with peak shear strength ranging from 1.074 MPa to 2.073 MPa and a maximum error of 8.48%, meeting engineering requirements. The damage evolution process of the concrete-rock interface under different normal forces was simulated and compared with in-situ direct shear test results, confirming the accuracy of the simulation. The failure modes of the concrete-rock interface under different loading conditions can be categorized into three types: bonding interface failure, mixed shear failure, and rock failure. The failure mode is closely related to the magnitude of normal stress—as normal stress increases, the area of shear fracture along the bonding interface expands, and the fracture surface becomes smoother. The findings provide a theoretical basis for the design, anti-sliding stability, and risk analysis of similar concrete gravity dams. Full article

In this study, a novel rigid–flexible coupled computational model for floating offshore wind turbines (FOWTs) is developed using the vector-form intrinsic finite element (VFIFE) method, named VFIFE-FOWT. This framework integrates multi-body dynamics and the VFIFE method to establish a comprehensive dynamic model for FOWTs, enabling high-fidelity simulations of FOWT systems. Validation of the VFIFE-FOWT model is conducted through comparisons with results from the industry-standard software OpenFAST, together with experimental data from a 1:80 scale model test of a shallow-draft stepped Spar platform equipped with a NREL 5MW wind turbine. The results demonstrate good agreement, verifying the accuracy and reliability of the proposed VFIFE-FOWT framework for predicting the dynamic behavior of FOWTs. Full article

The present study investigated the host cell response to EHV-8 infection in rabbit kidney (RK-13) cells through transcriptomic and proteomic approaches. At 24 h post-infection, a total of 2118 differentially expressed genes (DEGs) were identified, with 1338 upregulated and 780 downregulated. At 48 h, 7388 DEGs were detected, with 4342 upregulated and 3046 downregulated genes. Proteomic analysis revealed 932 differentially expressed proteins (DEPs) at 24 h (364 upregulated and 568 downregulated) and 3866 DEPs at 48 h (2285 upregulated and 1581 downregulated). Of these, 237 upregulated and 336 downregulated proteins were common across both time points. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the majority of DEGs and DEPs were enriched in key inflammation-related pathways, notably the TNF and NF-κB signaling pathways. Validation of the transcriptomic and proteomic data was performed using RT-PCR and parallel reaction monitoring (PRM), respectively, and confirmed consistent trends for TNFR1, NF-κB p65, and MAP3K8, as reported in the transcriptomic and proteomic screening. These findings suggest that EHV-8 infection may modulate host immune responses by activating the TNF signaling pathway. However, given that RK-13 cells may not fully replicate viral–host interactions in equine species, further in vivo studies in horses and donkeys are required to provide a more comprehensive understanding of the viral pathogenesis in these animals. Full article

Equine herpesvirus type 8 (EHV-8) is predominantly isolated from donkeys, but its biological properties and pathogenic potential remain underexplored. This study aimed to investigate the biological characteristics and pathogenicity of the EHV-8 LCDC01 isolate by examining its effects in rabbit kidney (RK-13) cells and BALB/c mice. The virus was assessed for its ability to induce viral replication, pathological changes, and alterations in pro-inflammatory responses. In vitro, the EHV-8 infection of RK-13 cells induced characteristic cytopathic effects, including cell contraction, the formation of grapevine bundle-like structures, and detachment. In vivo, mice infected with the virus exhibited no clinical signs other than weight loss. Polymerase chain reaction (PCR) analysis detected viral DNA exclusively in the lungs of infected mice, while TaqMan PCR further confirmed the presence of EHV-8 nucleic acids in the lungs, liver, brain, and intestines. Furthermore, ELISA assays revealed a significant increase in the secretion of pro-inflammatory cytokines, including IL-1β, IL-6, IL-8, and IFN-α, in the lungs (p < 0.05). These findings suggest that EHV-8 primarily replicates in the lung tissue of mice and can induce inflammatory responses. This study provides valuable insights into the pathogenic mechanisms of EHV-8 and lays the groundwork for further investigation into its potential impact on equine and other animal populations. Full article

The transitional shale system of the Longtan Formation (LTF) is widely distributed in the Sichuan Basin. However, the lithofacies of the LTF shale system exhibit vertical variations, with frequent interbedding of blocks, and shale–sand–coal sequences, which makes identifying “sweet spots” a challenging task. To address this issue, lithofacies associations were investigated based on well log analysis from 30 wells, and experimental data from 19 well samples, including X-ray diffraction, total organic carbon (TOC), pore structure characterization, and methane isothermal adsorption tests. Four lithofacies associations were classified: carbon–shale interbedding (I-1), shale(carbon)–coal interbedding (I-2), shale–sand interbedding (II), and shale–sand–coal assemblage (III). A favorable lithofacies association index (Com) was developed, providing a quantitative method for identifying favorable lithofacies. The results indicate that among the four lithofacies associations, I-2 is the most favorable lithofacies association. The Com index threshold for favorable lithofacies is defined as 0.6, and for the most favorable lithofacies, it is 0.7. Overall, favorable lithofacies are primarily distributed in the Suining-Dazu and Lujiao areas. Full article

Shale gas is a critical energy resource, and estimating its ultimate recoverable reserves (EUR) is a key indicator for evaluating the development potential and effectiveness of gas wells. To address the challenges in accurately predicting shale gas EUR, this study analyzed production data from 200 wells in the CN block. Sixteen potential factors influencing EUR were considered, and key geological, engineering, and production factors were identified using Spearman correlation analysis and mutual information methods to exclude highly linearly correlated variables. An attention mechanism was introduced to weight input features prior to model training, enhancing the interpretability of feature contributions. The hyperparameters of the model were optimized using the Rabbit Optimization Algorithm (ROA), and 10-fold cross-validation was employed to improve the stability and reliability of model evaluation, mitigating overfitting and bias. The performance of four machine learning models was compared, and the optimal model was selected. The results indicated that the ROA-CatBoost-AM model exhibited superior performance in both fitting accuracy and prediction effectiveness. This model was subsequently applied for EUR prediction and for identifying the primary factors controlling productivity, providing effective guidance for development practices. The dominant factors and production forecasts determined by the model offer valuable references for optimizing block development strategies. Full article

Aflatoxin B1 (AFB1) is a prevalent environmental and forage contaminant that poses significant health risks to both humans and livestock due to its toxic effects on various organs and systems. Among its toxicological effects, nephrotoxicity is a hallmark of AFB1 exposure. However, the precise mechanisms underlying AFB1-induced kidney damage in donkeys remain poorly understood. To investigate this, we established a donkey model exposed to AFB1 by administering a diet supplemented with 1 mg AFB1/kg for 30 days. Kidney apoptosis was assessed using TUNEL staining, while gene expression and protein levels of Endonuclease G (EndoG), as well as genes related to endoplasmic reticulum (ER) stress and apoptosis, were quantified by RT-qPCR and Western blotting. Our findings indicate that AFB1 exposure resulted in significant kidney injury, apoptosis, and oxidative stress. Notably, AFB1 exposure upregulated the expression of EndoG and promoted its translocation to the ER, which subsequently induced ER stress and activated the mitochondrial apoptotic pathway. These results suggest that AFB1-induced kidney damage in donkeys is mediated through the oxidative stress and mitochondrial apoptosis pathways, primarily involving the EndoG-IRE1/ATF6-CHOP signaling axis. Full article

CTXΦ is a lysogenic filamentous phage that carries the genes encoding cholera toxin (ctxAB), the main virulence factor of Vibrio cholerae. The toxigenic conversion of environmental V. cholerae strains through CTXΦ lysogenic infection is crucial for the emergence of new pathogenic clones. A special allelic form of CTXΦ, called pre-CTXΦ, is a precursor of CTXΦ and without ctxAB. Different members of the pre-CTXΦ and CTXΦ families are distinguished by the sequence of the transcriptional repressor-coding gene rstR. Multiple rstR alleles can coexist within a single strain, demonstrating the diverse structure and complex genomic integration patterns of CTXΦ/pre-CTXΦ prophage on the chromosome. Exploration of the diversity and co-integration patterns of CTXΦ/pre-CTXΦ prophages in V. cholerae can help to understand the evolution of this phage family. In this study, 21 V. cholerae strains, which were shown to carry the CTXΦ/pre-CTXΦ prophages as opposed to typical CTX^ETΦ-RS1 structure, were selected from approximately 1000 strains with diverse genomes. We identified two CTXΦ members and six pre-CTXΦ members with distinct rstR alleles, revealing complex chromosomal DNA integration patterns and arrangements of different prophages in these strains. Promoter activity assays showed that the transcriptional repressor RstR protected against CTXΦ superinfection by preventing the replication and integration of CTXΦ/pre-CTXΦ phages containing the same rstR allele, supporting the co-integration of the diverse CTXΦ/pre-CTXΦ members observed. The numbers and types of prophages and their co-integration arrangements in serogroup O139 strains were more complex than those in serogroup O1 strains. Also, these CTXΦ/pre-CTXΦ members were shown to present the bloom period of the CTXΦ/pre-CTXΦ family during wave 2 of the seventh cholera pandemic. Together, these analyses deepen our comprehension of the genetic variation of CTXΦ and pre-CTXΦ and provide insights into the evolution of the CTXΦ/pre-CTXΦ family in the seventh cholera pandemic. Full article

Since rail-to-ground insulation decreases, large-level direct currents (DCs) leak from railways and form metro stray currents, corroding the buried metal. To locate the rail-to-ground insulation deterioration area, a location method is proposed based on parameter identification methods and the monitored information including the station rail potentials, currents at the traction power substations (TPSs), and train traction currents and train positions. According to the monitoring information of two adjacent TPSs, the section location model of the metro line is proposed, in which the rail-to-ground conductances of the test section are equivalent to the lumped parameters. Using the rail resistivity and traction currents as the known information, the rail-to-ground conductances are calculated with the least square method (LSM). The rail-to-ground insulation deterioration sections are identified by comparing the calculated conductances with thresholds determined by the standard requirements and section lengths. Then, according to the section location results, a detailed location model of the degradation section is proposed, considering the location distance accuracy. Using the genetic algorithm (GA) to calculate the rail-to-ground conductances, degradation positions are located by comparing the threshold calculated with the standard requirements and location distance accuracy. The location method is verified by comparing the calculation results under different degradation conditions. Moreover, the applications of the proposed method to different degradation lengths and different numbers of degradation sections are analyzed. The results show that the proposed method can locate rail-to-ground insulation deterioration areas. Full article

In the present study, an acid catalyst (UiO-66-SO₃H) with Brønsted and Lewis acid sites was synthesised for the preparation of highly efficient biodiesel from oleic acid and methanol using chlorosulphonic acid sulfonated metal–organic frameworks (UiO-66) prepared with acetic acid as a moderator. The prepared catalysts were characterised using XRD, SEM, FT-IR and BET. The catalytic efficiency of the sulfonated catalysts was significantly improved and successful sulfonation was demonstrated by characterisation techniques. Biodiesel was synthesised by the one-pot method and an 85.0% biodiesel yield was achieved under optimum conditions of the reaction. The esterification reaction was determined to be consistent with a proposed primary reaction and the kinetics of the reaction was investigated. A reusability study of the catalyst (UiO-66-SO₃H) was also carried out with good reproducibility. In conclusion, the present study provides some ideas for the synthesis of catalysts with high catalytic activity for the application in the catalytic preparation of biodiesel. Full article

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters. Full article