Streptococcus suis is a significant zoonotic agent affecting both human and pig health and poses a substantial public health concern. The pathogenicity of S. suis is intricately linked to its ability to form biofilms and express virulence factors, which are regulated by the LuxS/AI-2 quorum sensing (QS) system. Herein, we uncover a novel therapeutic avenue by demonstrating that 5-fluorouracil (5-FU), an FDA-approved anti-cancer agent, effectively mitigates biofilm formation and attenuates the virulence of S. suis. Mechanistically, we observe a significant reduction in capsular polysaccharide and extracellular polysaccharide production upon 5-FU treatment, elucidating a potential mechanism for biofilm weakening. Additionally, 5-FU down-regulates virulence traits, diminishing S. suis's ability to adhere to host cells and evade phagocytosis. Crucially, our study identifies the thymidylate synthase regulatory gene thyA as a key mediator of 5-FU's effects on the LuxS/AI-2 QS system. Virtual molecular docking and gene knockout experiments provide compelling evidence that 5-FU modulates the LuxS/AI-2 QS system by targeting thyA. In vivo experiments further validate the therapeutic potential of 5-FU, showcasing a significant reduction in bacterial load and mitigation of tissue damage in a mouse model. In conclusion, our investigation unveils 5-FU as a potent disruptor of S. suis's biofilm formation and virulence, offering a promising avenue for the control of this devastating pathogen.

Newcastle disease virus (NDV) is a highly lethal and contagious viral pathogen, and it is also a potent oncolytic virus that selectively replicates in tumor cells.  NDV demonstrates high replication efficiency in avian and tumor cells, causing various types of cell death, including ferroptosis, necrosis, apoptosis and autophagic cell death, with apoptosis being the most thoroughly studied.  Organelles play critical and distinctive roles in the regulation and execution of apoptosis.  However, the involvement of peroxisomes, an important organelle that regulates redox balance and lipid biosynthesis, in virus-induced apoptosis remains unclear.  Our findings reveal that NDV infection promotes the downregulation of several peroxisome biogenesis factors (PEXs) at the mRNA level.  Peroxisomal biogenesis factor 5 (PEX5), a critical peroxisomal shuttle protein, was identified to be significantly downregulated at both the mRNA and protein levels.  Further, gain- and loss-of-function experiments demonstrated the negative regulation of NDV-induced apoptosis by PEX5.  In addition, PEX5 inhibits NDV-induced apoptosis by regulating the anti-apoptotic protein B-cell lymphoma-2 (Bcl-2) expression.  These findings reveal a novel mechanism by which NDV-induced apoptosis is modulated through the downregulation of PEXs, particularly PEX5, shedding light on the potential role of peroxisome in apoptosis regulation in response to virus infection.

Computer vision is widely recognized as an influential technology in the field of precision management of animals.  Emerging studies have demonstrated the potential to improve pig health and welfare through animal surveillance systems and computer vision (CV) algorithms.  However, the lack of benchmark datasets and robust fundamental algorithms restrict CV applications for the commercial use.  This study aims to bridge the gap between technology development and commercial applications in pig farming scenarios by introducing a general-purpose dataset (PigLife), comparing benchmark performances of foundational CV algorithms and model development workflows.  The PigLife dataset contains video clips and images (38 short video clips, 2K image frames, 22K pig instances) across most pig production phases in a typical commercial pig farm: Breeding and Gestation, Farrow to Wean, Weaning & Nursery, and Growth to Finish.  Three detection algorithms (Faster R-CNN, RetinaNet, TridentNet) and three segmentation algorithms (Mask R-CNN, MViTv2, PointRend) were trained on the PigLife dataset from scratch.  Fine-tuning of pre-trained models (YOLO8-m, Faster-RCNN-r50) and no-training from zero-shot models (CLIP-SAM, Grouddino-HQSAM) were also evaluated to suggest faster CV development workflows for commercial applications in pig farming.  This study emphasizes the necessity of a benchmark dataset for evaluating the robustness of algorithms and identifying the remaining difficulties and challenges across various algorithms.  Furthermore, developing CV models from pre-trained algorithms or zero-shot models showed better performance and a faster process, which could reduce barriers when developing high-performance CV products in pig production industry.

The integration of green manure (GM) crops into traditional cropping systems has regained attention for its potential to improve soil organic carbon (SOC) content in an environmentally sustainable way. However, the effects of carbon (C) input from different GM species on the SOC accumulation and recalcitrant C fractions across soil profile remain inadequately understood. This three-year North China Plain study assessed SOC changes and C fractions of easily oxidizable organic carbon (EOC) and recalcitrant organic carbon (ROC) in fallow, rye, rapeseed, and vetch systems, with δ¹³C analysis for GM-derived C fraction and microbial C-decomposition functional genes. Our results show that SOC was significantly increased by GMs. Rapeseed was the only species that improved SOC at 20-40 cm, the rapeseed-derived C contributed 2.48% of the SOC. Rye enhanced EOC and ROC at topsoil, rapeseed increased ROC at 20-60 cm, and vetch increased EOC at 40-60 cm. At the topsoil, the abundances of cellulose- and pectin-decomposition gene were increased in vetch and decreased in rye. At 20-40 cm, the pectin- and lignin-decomposition genes were markedly improved by rapeseed, while at 40-60 cm, the chitin-decomposition gene was increased in vetch, indicating the microbial promoting effects by deep roots of vetch and rapeseed. Our results suggest GM species influence SOC deposition depth and the recalcitrance of SOC decomposition, thereby affecting the distribution of SOC accumulation through microbial-driven C decomposition activities.

Home plant–soil feedbacks (home-PSFs) typically demonstrate negative effects in vegetable crops, substantially inhibiting their growth.  Phosphorus (P), an essential plant nutrient crucial for growth, influences vegetable crop growth patterns through soil availability levels.  However, the relationship between soil available P levels and home-PSFs in vegetable crops requires further investigation.  This study established a home PSF system incorporating 12 vegetable crops from 6 families to examine growth responses under two P conditions (low P level: 40 mg P kg^–1 soil; high P level: 200 mg P kg^–1 soil).  The findings revealed that low P conditions significantly decreased overall biomass across all vegetables, with preferential biomass allocation to root development.  Furthermore, low P conditions enhanced mycorrhizal colonization and rhizosphere acid phosphatase activity while notably decreasing root length.  While vegetables generally exhibited negative home PSFs, allium and nonmycorrhizal plants demonstrated positive responses under high P conditions.  Wild tomatoes displayed greater variation in feedback values across P levels compared to common tomatoes.  Under high-P conditions, mycorrhizal colonization showed positive correlations with feedback values of biomass and P concentration.  Root diameter and mycorrhizal colonization demonstrated distinct correlations with these feedback values under low-P conditions.  The research concludes that high P levels effectively mitigate negative home-PSFs in vegetables while increasing biomass production.  Additionally, high P levels demonstrated superior efficacy in alleviating negative home-PSFs in wild tomatoes compared to common tomatoes.

Phosphorus (P) availability influences the spatial distribution of carbon (C)-cycling enzyme activities in the rhizosphere through its effects on plant growth and microbial activity.  However, the influence of P availability on the spatial patterns of C and P hydrolase activities remains unclear in the rhizosphere of Maize (Zea mays L.) and narrow-leaf lupine (Lupinus angustifolius L.), which exhibit contrasting P deficiency adaptation and acquisition strategies.  This study analyzed the spatial patterns of C and P hydrolase activities through zymography and correlated them with bacterial community structure in maize and lupine rhizospheres.  Under P-deficient conditions, maize exhibited severe growth restriction while demonstrating a 2.2–9.6-fold increase in root exudation compared to P-sufficient conditions.  The enhanced exudation under P deficiency promoted r-strategist bacterial proliferation (e.g., Ktedonobacteria and Xanthomonadales) while reducing K-strategist abundance (Actinobacteriota, Chloroflexia, and Alphaproteobacteria).  Maize rhizosphere enzyme activities and hotspot areas demonstrated positive correlation with K-strategist abundance and negative correlation with r-strategist abundance.  P-sufficient maize exhibited 15–550% higher C- and P-cycle-related enzyme activity and hotspot areas, attributed to its enhanced root system and predominance of K-strategists with superior enzyme synthesis capabilities.  Lupine demonstrated superior P deficiency adaptation, producing 2–19 times more DOC and organic acids than maize.  Consequently, lupine showed no significant alterations in enzyme activity, hotspot areas, or bacterial community composition in response to P availability.  These findings demonstrate that plant-specific P deficiency adaptation mechanisms distinctly influence the spatial distribution of C-cycling enzyme activity and bacterial community structure in the rhizosphere.

Denitrification plays a critical role in mitigating anthropogenic nitrate (NO₃^–) accumulation in ecosystems.  The isotopic composition of NO₃^– (δ¹⁵N and δ¹⁸O) serves as a powerful tracer for identifying N sources and transformation processes.  Denitrification often superimposed on the isotope effects of NO₂^– oxidation, resulting in parallel enrichment of δ¹⁵N- and δ¹⁸O-NO₃^– (Δδ¹⁸O:Δδ¹⁵N trajectory) that causes them to be either below or above 1.  This study compared the Δδ¹⁸O:Δδ¹⁵N trajectory during denitrification, functional genes (narG, napA, and nxrA), and carbon sources from metabolites in the Δδ¹⁸O:Δδ¹⁵N trajectories below or above 1 in unsaturated zones.  The results revealed that NO₃^– reduction was more important for variation in the Δδ¹⁸O:Δδ¹⁵N trajectory because the difference in isotope effects (¹⁵ε_{NO3 reduction} and ¹⁸ε_{NO3 reduction}) between the two Δδ¹⁸O:Δδ¹⁵N trajectory groups was significant, whereas the difference in isotope effects (¹⁵ε_nxr and ¹⁸ε_nxr) upon NO₂^– oxidation was not.  Carbon sources in the group with Δδ¹⁸O:Δδ¹⁵N trajectories below 1 facilitated more efficient electron production to promote NO₃^– reduction because of their low molecular weight and simple structure.  Conversely, the lower electron production efficiency due to the high molecular weight and complex structures of carbon sources in the group with Δδ¹⁸O:Δδ¹⁵N trajectories above 1 downregulated the expression of the three functional genes (narG, napA, and nxrA).  The group with Δδ¹⁸O:Δδ¹⁵N trajectories below 1 showed significantly higher levels of ¹⁵ε_{NO3 reduction}, ¹⁸ε_{NO3 reduction}, NO₂^– oxidation ratio, and copy numbers of narG, napA, and nxrA genes compared to the other group, revealing that NO₃^– reduction at the cellular level was more active in the former group.  This study elucidated the integrated influence of isotope effects, NO₃^– reductase and NO₂^– oxidoreductase activities, and carbon sources from metabolites.  These findings are significant for understanding the Δδ¹⁸O:Δδ¹⁵N trajectories of N cycling in terrestrial ecosystems and support groundwater conservation by improving carbon supplementation approaches that stimulate denitrification, with Δδ¹⁸O:Δδ¹⁵N trajectories serving as effective tracers for assessing denitrification performance in terrestrial environments.

Composting represents a crucial component of sustainable waste management, providing significant resource recovery and environmental advantages.  However, nitrogen loss during composting remains a significant challenge, necessitating the development of a predictive model for nitrogen loss during the composting process.  This investigation implemented five machine learning models, utilizing 307 data points encompassing composting strategies, physicochemical properties, and composting time stages, to predict nitrogen loss during organic solid waste composting.  The findings demonstrated that the adaptive boosting (AdaBoost) algorithm achieved optimal performance with a coefficient of determination of 0.847 after eliminating redundant features (scale and C/N).  Moreover, Shapley additive explanation analysis identified several key factors significantly influencing nitrogen losses during composting, including composting time stages, bulking agents, raw materials, and ammonium nitrogen levels.  Notably, the initial phase of composting emerged as the most critical period for nitrogen loss.  The utilization of sawdust, rice husk, and corn stalk as bulking agents enhanced nitrogen retention in compost.  Furthermore, implementing static aeration for ventilation and applying chemical additives effectively reduced nitrogen losses during the composting process.  These results provide a scientific foundation for identifying optimal composting conditions to minimize nitrogen loss, thereby offering practical guidance for effective composting operations.

Soil salinization represents a primary manifestation of land degradation and presents a significant threat to sustainable agricultural development.  Remote sensing-based methodologies currently constitute the preferred approach for salinization monitoring.  Environmental factors’ spatial heterogeneity substantially constrains the modeling process in accurately capturing the soil salt content (SSC)-modeling factor relationship, thereby affecting monitoring accuracy.  This study proposes a classification modeling framework based on dominant salinization factors, establishing distinct remote sensing inversion models through categorization of soil texture and surface drainage conditions.  Results indicate that classification modeling substantially improves the capture of SSC-modeling factor relationships.  The efficacy of identical modeling indicators and methods varies significantly across different classification scenarios.  Among the three modeling approaches, random forest demonstrates superior overall robustness.  Of the three variable selection methods, light gradient boosting machine (LightGBM) shows the strongest compatibility with the modeling approaches.  The classification strategy significantly enhances model accuracy: compared to non-classified modeling (R²_V=0.62), the testing set R² increases by up to 24% (R²_V=0.77).  Models under poor surface drainage category demonstrate optimal performance, with coupled models achieving R²_C=0.82 (training set) and R²_V=0.77 (testing set).  This research provides valuable insights for remote sensing monitoring of soil salinization in precision agriculture contexts.