The temperament of ruminants has been investigated for several decades and has similarities with human temperament. Temperament represents an animal’s response to situations perceived as challenging. This behavioral trait can influence numerous biological functions, primarily the stress axis, affecting production characteristics and animal welfare. A crucial aspect of temperament expression lies in how individuals perceive stressors. Molecular research has begun to elucidate the central pathways involved in the expression of temperament. Recent investigations suggest that the rumen microbiome may influence temperament, like the way that the effect of the gut microbiota on the brain in monogastric species. Further research is necessary to understand the relationship between the gut microbiome and ruminant temperament. Future applications may include modifying the temperament of production animals through the manipulation of the rumen microbiota and potentially enhancing their health and welfare.

Fusarium head blight (FHB), mainly caused by Fusarium graminearum, is one of the most destructive fungal diseases affecting global wheat production.  Elymus repens (2n=6×=42, StStStStHH), a wild relative of wheat, exhibits numerous biotic and abiotic stress resistance characteristics.  In previous studies, FHB resistance of E. repens has been transferred into common wheat through a wheat–E. repens partial amphidiploid and derivative lines.  This study reports the development, characterization, and breeding utilization of K140-7, a novel wheat–E. repens translocation line conferring resistance to FHB.  Genomic in situ hybridization (GISH) and fluorescence in situ hybridization (FISH) analyses demonstrated that K140-7 contained 40 common wheat chromosomes and two 7D·St translocation chromosomes.  Subsequent characterization using oligonucleotide-FISH painting and single-gene FISH markers confirmed that the 7D fragment was a 7D short arm and the St fragment was a 7St long arm.  Therefore, K140-7 was further identified as a 7DS·7StL translocation line with genetic compensation.  Wheat 55K SNP array analysis of K140-7 demonstrated the 7DS·7StL translocation event.  Field evaluations demonstrated that K140-7 exhibits agronomic performance comparable to its wheat parent.  Based on St reference genome of Pseudoroegneria libanotica, 21 simple sequence repeats (SSR) markers specific to 7StL were developed.  Genetic analysis established that 7StL confers FHB resistance and carries the dominant FHB resistance locus, designated as QFhb.Er-7StL.  Introgression of QFhb.Er-7StL into elite wheat cultivars has generated three second-generation 7DS·7StL translocation lines with enhanced agronomic traits.  This study provides valuable novel germplasms and specific molecular markers for FHB resistance breeding in wheat.

Barley (Hordeum vulgare L.) ranks as the fourth most cultivated cereal crop globally by planting area.  Kernel characteristics, including grain length, grain width, and thousand-grain weight (TGW), are essential determinants of barley yield and quality.  The identification and cloning of genes related to kernel traits, along with the detection of superior alleles, are fundamental for marker-assisted selection in barley breeding.  This study presents the cloning of HvGL7-2H from barley, based on the known rice GL7 gene.  The functional significance of HvGL7-2H in grain length was confirmed through ethyl methane sulfonate (EMS) mutants of the barley landrace “Hatiexi”.  A candidate gene-based association analysis was conducted using a panel of 363 barley accessions to identify superior haplotypes for HvGL7-2H.  The analysis revealed that Hap3 represented the superior haplotype for both grain length and TGW, while Hap4 emerged as the superior haplotype for TGW.  These findings indicate that genotypes carrying the superior allele serve as valuable genetic resources, and the molecular markers identified herein will facilitate grain size and yield improvement in barley breeding programs.

Salt stress is a major constraint to crop productivity and quality.  The limited availability of salt-tolerant genes poses significant challenges to breeding programs aimed at enhancing salt tolerance.  Sorghum displays a remarkable ability to withstand saline conditions; therefore, elucidating the genetic underpinnings of this trait is crucial.  This study entailed a comprehensive resequencing of 186 sorghum accessions to perform a genome-wide association study (GWAS) focusing on relative root length (RL) and root fresh weight (RFW) under salt stress conditions.  We identified eight candidate genes within a co-localized region, among which SbTEF1 - a gene encoding a transcription elongation factor protein - was deemed a potential candidate due to its annotation and expression pattern alterations under salt stress.  Haplotype analysis, gene cloning, linkage disequilibrium (LD) analysis, and allele effect analysis revealed that PAV284, located in the promoter region of SbTEF1, modulated gene expression under salt stress, which, in turn, influenced sorghum seedlings’ salt tolerance.  PAV284 holds promise as a genetic marker for selecting salt-tolerant germplasm via marker-assisted breeding, enhancing the development of salt-tolerant sorghum cultivars.

Wheat–maize rotation is a widely used planting pattern in oasis-irrigated areas in Northwest China.  Although this planting pattern has the advantage of breaking the barrier of continuous cropping to some extent, it also presents some problems, such as large evaporation and prominent soil degradation during the fallow period, which seriously restricts the improvement of crop yield.  Planting green manure (GM) after wheat and returning it to the field can effectively improve soil physicochemical properties, regulate the photosynthetic characteristics of subsequent crops, and promote crop yield.  However, the photosynthetic physiological mechanism of crop yield improvement under different green manure return methods (GMRM) remains unclear.  Therefore, by exploring the relationships among soil moisture and temperature environment, maize root structure, photosynthetic characteristics, fluorescence characteristics and yield under different GMRM, this study aims to provide a theoretical basis for clarifying the photosynthetic physiological mechanism of GMRM to improve maize yield.  A three-year field experiment was conducted at a research station in the Shiyang River Basin (Gansu, China).  Five treatments were involved in this study: (i) conventional tillage without GM (CT), (ii) no-tillage with total GM mulching (NTG), (iii) no-tillage with removal of aboveground GM (NT), (iv) tillage with total GM incorporation (TG), and (v) tillage with only root incorporation (T).  Results showed that the NTG and TG significantly increased soil water content (SWC) in 0–110 cm soil layer, soil temperature (ST) of maize seedlings (V3) to jointing stage (V6), canopy cover (CC), leaf stay-greenness (SG), root length (RL), net photosynthetic rate (P_n), transpiration rate (T_r), actual photochemical efficiency of PSII (ΦPSII), maize biomass, and grain yield (GY) compared with CT.  In addition, NTG and TG significantly decreased the ST of maize from the big trumpet stage (V12) to the blister stage (R2), and the dissipation of excess energy (NPQ), compared with CT.  GM’s return to the field could improve root structure and canopy coverage of maize mainly by improving soil water content.  The optimization of maize root structure and canopy coverage increased the maize chlorophyll content (SPAD) value and promoted P_n.  The increase in P_n inhibits the increase in NPQ, thus promoting the activation of ΦPSII.  The increase in ΦPSII promoted the increase in maize biomass, ultimately leading to an increase in maize GY.

Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution, indirectly leading to variations in the leaf potassium content (LKC, %) at different leaf positions.  However, due to the interaction between light and leaf age, leaf sensitivity to this change varies at different positions, including the reflection and absorption of the spectrum.  Selecting the optimal leaf position for monitoring is a crucial factor in the rapid and accurate evaluation of cotton LKC using spectral remote sensing technology.  Therefore, this study proposes a comprehensive multi-leaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom, aiming to achieve an accurate estimation of cotton LKC and optimize the strategy for selecting the monitored leaf position.  Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, ..., n) during the cotton budding, flowering, and boll-setting stages.  Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated.  Additionally, the optimal range of the dominant leaf position for monitoring was determined.  Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were employed to develop LKC estimation models for single- and multi-leaf positions.  The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom; the average LKC of cotton reached its maximum value at the flowering stage.  The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.  The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively.  Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with the coefficient of determination of the validation set (R²val) being 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) being 0.168, 0.197, and 0.191, respectively.  The multi-leaf position LKC estimation model was constructed by EWM with R²val being 0.887, 0.728, and 0.703, and RMSEval being 0.134, 0.172, and 0.209, respectively.  In contrast, the newly developed multi-leaf position comprehensive estimation model yielded superior results, improving the model’s stability based on high accuracy, especially during the budding and flowering stages.  These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.

Melon is a globally cultivated horticultural crop with a predominantly hybrid commercial seed market in China.  Seedling morphology, particularly hypocotyl color, is a valuable trait for rapid F₁ hybrid seed purity assessment.  While green hypocotyls are common, white hypocotyls are rare in melon germplasm.  This study identified a mutant with white hypocotyl but green leaves from the heavy ion beam mutant library.  Genetic analysis revealed that a single recessive gene controlled the white hypocotyl, designated CmGhc1.  A single-base deletion in the fifth exon of CmGhc1 led to a truncated CmGhc1 lacking the HTH-MYB DNA binding domain, likely affecting its transcriptional activity.  CmGhc1 was localized in the nucleus, and yeast two-hybrid analysis and a dual-LUC assay demonstrated it as a transcription repressor.  Furthermore, a KASP marker (hc1) was developed and verified as a functional marker for breeding white hypocotyl germplasms in melon.  RNA-seq data revealed that CmGhc1 significantly affected the transcription of genes related to chlorophyll metabolism and photosynthesis in hypocotyl.  In summary, these findings contribute to our understanding of chloroplast biogenesis and provide a valuable tool for melon breeding.

Bud endodormancy represents an ecological adaptation mechanism in perennial deciduous fruit trees to endure winter cold conditions.  Sucrose serves a crucial role in bud endodormancy as both an energy metabolizer and signaling molecule.  Sugars Will Eventually be Exported Transporters (SWEETs) function as sugar-efflux transporters that respond to environmental stimuli and contribute to plant growth and development. While SWEET gene families have been identified in various plant species for sugar transport regulation, their mechanism in regulating peach bud endodormancy remains undefined.  In this study, we identified 15 SWEET genes in peach.  The nomenclature was established through homologous alignment with the Arabidopsis SWEET gene family, resulting in four distinct clades through phylogenetic analysis.  Covariance correlation analysis revealed 6 and 12 collinear SWEET genes in peach and Arabidopsis, respectively, forming 13 collinear gene pairs.  Real-time quantitative polymerase chain reaction (RT-qPCR) analysis demonstrated significantly elevated expression of PpSWEET6 during peach bud endodormancy release, correlating positively with sucrose content.  Transient overexpression of PpSWEET6 enhanced peach bud endodormancy release, while overexpressing PpSWEET6 in Arabidopsis enhanced seed germination and flowering.  Y2H and luciferase complementation imaging (LCI) assays confirmed PpSWEET6 interacted with PpABF2.  Additionally, dual luciferase reporter (DLR) assays showed that PpSWEET6 significantly decreased the activation of PpDAM6 (key dormancy-inducing gene) through PpABF2, thereby modulating peach bud endodormancy release.  These findings advance our understanding of SWEET genes in peach bud endodormancy regulation.

Sweet cherry (Prunus avium) represents a significant stone fruit crop in temperate regions worldwide.  While molecular breeding has progressed substantially following the initial sweet cherry genome release, existing genome assemblies contain unresolved gaps and comprise consensus chimeric sequences that fail to differentiate haplotype alleles, significantly constraining research on important agronomic trait inheritance.  This study presents a phased-resolved telomere-to-telomere reference genome of sweet cherry ‘Tieton’.  The assembly anchors 653.03 Mb of sequence onto 16 pseudochromosomes representing two haplotypes, with 67,012 coding genes identified (33,777 in hapA and 33,235 in hapB).  The genome demonstrates superior quality metrics, including a consensus accuracy exceeding QV44, contig N50 above 17.94 Mb, Benchmarking Universal Single-Copy Orthologs completeness of 98.7%, and a long terminal repeat (LTR) assembly index exceeding 20.  This genome provides phased and annotated chromosome pairs, offering a comprehensive view of sweet cherry’s diploid genome organization.  Utilizing this reference genome, we identified a large fragment deletion associated with yellow-skinned fruit in sweet cherry ‘13-33’.  This resource will significantly enhance breeding efforts and genetic research in sweet cherries.

Dendrobium officinale is an orchid herb distinguished by its exceptional drought resistance capabilities.  The remarkable drought tolerance of D. officinale stems from its structural and compositional features, including thick leaves and stems containing abundant polysaccharides and colloidal substances.  Despite these adaptations, the underlying molecular mechanisms responsible for enhanced drought tolerance remain inadequately understood.  This study subjected D. officinale to water restriction for periods ranging from 1 to 6 months, conducting physiological and RNA sequencing analyses to elucidate its long-term dehydration response mechanisms and identify drought-protective genes.  Following 6 months of dehydration, D. officinale maintained viability, demonstrated by rapid growth resumption after merely 2 d of rehydration.  Transcriptome analysis of D. officinale plants under 1-month dehydration revealed differential gene expression across various processes, predominantly in stress responses, photosynthesis, phytohormone signaling, carbon metabolism, and fructose/mannose pathways.  Among these, PEROXIDASE4 (POD4) and NAC37 showed significant upregulation and were selected for further investigation of their roles in drought protection.  Transgenic tomato plants overexpressing D. officinale’s POD4 and NAC37 genes exhibited superior drought tolerance compared to controls, displaying enhanced vigor, increased fruit production, higher respiration rates, elevated chlorophyll levels, and reduced oxidative damage.  This research demonstrates the value of exploring underutilized species for drought-tolerance genes and identifies POD4 and NAC37 as promising candidates for improving drought tolerance through breeding programs.

Plant pathogens secrete various cell wall-degrading enzymes that compromise host cell wall integrity and facilitate pathogen invasion.  This study identified VdGH7a, a glycoside hydrolase family 7 (GH7) cellobiohydrolase from Verticillium dahliae, which demonstrated hydrolytic activity against 1,4-β-glucan.  Notably, VdGH7a induced cell death in Nicotiana benthamiana when signal peptides were present, though this effect was inhibited by the carbohydrate-binding type-1 (CBM1) protein domain.  The deletion of VdGH7a substantially reduced V. dahliae pathogenicity in cotton plants, as demonstrated by the mutants’ inability to penetrate cellophane membrane.  These knockout mutants also exhibited reduced carbon source utilization capacity and increased sensitivity to osmotic and cell wall stresses.  Through yeast two-hybrid screening, bi-molecular fluorescence complementation (BiFC), and luciferase complementation imaging (LCI), we identified that VdGH7a interacts with an osmotin-like protein (GhOLP1) in cotton.  Virus-induced gene silencing of GhOLP1 resulted in decreased salicylic acid (SA) content and reduced resistance to V. dahliae in cotton, while heterologous overexpression of GhOLP1 in Arabidopsis enhanced both resistance and SA signaling pathway gene expression.  These results reveal a virulence mechanism wherein the secreted protein VdGH7a from V. dahliae interacts with GhOLP1 to activate host immunity and contribute significantly to plant resistance against V. dahliae.

Glycosylphosphatidylinositol (GPI) anchoring represents a fundamental post-translational modification in eukaryotic cells.  In fungi, this modification facilitates diverse biological functions through protein targeting to the cell wall, yet research on its roles in plant pathogenic fungi remains limited.  This study elucidates the function of GPI anchoring in the maize fungal pathogen Cochlibolus heterostrophus.  The research demonstrates widespread accumulation of GPI-anchored proteins in hyphae, appressorium and infection hyphae of C. heterostrophus.  Deletion of ChGPI7, encoding a crucial enzyme in GPI anchor biosynthesis, substantially reduced vegetative growth, conidiation, and virulence through impaired appressorium formation and invasive growth.  The ΔChgpi7 mutants exhibited marked deficiencies in cell wall integrity, leading to decreased stress resistance.  Both ChGPI7 deletion and hydro fluoric acid (HF) pyridine treatment eliminated cell wall GPI-anchored proteins and exposed chitin, indicating that GPI-anchored proteins shield chitin from host immune recognition.  Analysis identified 124 predicted GPI-anchored proteins in C. heterostrophus, including the putative cell wall glycoprotein ChFEM1.  The deletion of ChFEM1 similarly reduced virulence and compromised infection structures and cell wall integrity.  Additionally, ChGPI7 influenced both the cell wall localization and protein abundance of ChFEM1.  These findings demonstrate that GPI anchoring mediates cell wall integrity and immune evasion during C. heterostrophus infection.

The nascent polypeptide-associated complex (NAC) plays crucial roles in various biological functions in eukaryotes and has been extensively studied in animals and plants; however, its role in the biocontrol mechanisms of microorganisms requires further investigation.  This study examined the function of TbNACα, a NAC subunit, in the biocontrol activity of Trichoderma breve T069 against Sclerotium rolfsii.  Following deletion of the TbNACα gene from T. breve T069, the ΔTbNACα mutant exhibited significantly reduced mycelial growth, spore production, and spore germination.  While volatile substances from ΔTbNACα showed no significant effect on S. rolfsii, non-volatile substances demonstrated significant inhibition of S. rolfsii growth.  Transcriptome sequencing analysis revealed 3,398 differentially expressed genes in the ΔTbNACα mutant compared to wild-type T069, primarily regulating genes associated with secondary metabolite biosynthetic enzymes, hydrolases, and membrane transport proteins.  Untargeted metabolomics identified 50 upregulated metabolites (27 in positive ion mode and 23 in negative ion mode) in crude extracts from ΔTbNACα mutant metabolite broth.  Among these metabolic substances, ethyl caffeate demonstrated the strongest activity against S. rolfsii, with an EC₅₀ of 107.15 μg mL^–1.  Quantitative real-time PCR (qPCR) analysis indicated significant upregulation of genes involved in the ethyl caffeate synthesis pathway in ΔTbNACα strains.  This research establishes the negative regulation of ethyl caffeate synthesis and elucidates the antagonistic inhibition mechanism of TbNACα in T. breve T069.

The rice stem borer, Chilo suppressalis (Walker) (Lepidoptera: Crambidae), is one of the most serious pests in rice-growing areas, and it has developed resistance to most insecticides currently used in the field.  Cyproflanilide is a novel meta-diamide insecticide that has shown high activities to multiple pests.  Evaluating the risk of resistance to cyproflanilide in C. suppressalis is necessary for its preventive resistance management.  Here we established the baseline susceptibility of C. suppressalis to cyproflanilide by the rice-seedling dipping method and topical application, and the LC₅₀ and LD₅₀ values were 0.026 mg L^–1 and 0.122 ng/larva, respectively.  The LC₅₀ values of cyproflanilide in 37 field populations ranged from 0.012 to 0.061 mg L^–1, and 25 field populations exhibited resistance to chlorantraniliprole with the highest LC₅₀ value of 3,770.059 mg L^–1.  In addition, a logistic distribution model analysis indicated that only 0.048 mg L^–1 of cyproflanilide was required to kill 90% field chlorantraniliprole-resistant populations of C. suppressalis, compared to 2,087.764 mg L^–1 of chlorantraniliprole for a similar level of control.  Resistance screening over 19 generations did not result in resistance to cyproflanilide (RR=3.1-fold).  The realized heritability (h²) of resistance was estimated as 0.067 by using threshold trait analysis, suggesting a low risk of cyproflanilide resistance development in susceptible strains.  The Cypro-SEL population (F₁₀) had no obvious fitness cost (relative fitness=0.96), and no significant changes in sensitivity to seven tested insecticides.  These findings suggested that cyproflanilide is a promising insecticide for the management of chlorantraniliprole-resistant C. suppressalis.  Moreover, this integrated risk assessment provides scientific application guidelines for the sustainable resistance management of cyproflanilide for controlling C. suppressalis.

Resilience traits in pig populations allow animals to deal better with infectious disease and suboptimal production environments.  The data on daily weight, feed intake and feed behaviors in pigs are collected in test period by automated feeding stations, which facilitate to evaluate the resilience traits.  In this study, we adopted the root mean square error (RMSE) of ordinary least squares (OLS) and the negative residuals of quantile regression (QR) to generate four different novel resilience traits using daily records of feed intake and feed duration between 90 and 180 days of age in a population of commercial Duroc pigs.  The genome-wide association studies (GWAS) based on single- and two-trait mixed models were carried out on 550 pigs using 48,603 single nucleotide polymorphisms (SNPs) to identify genomic regions associated with resilience traits in growing pigs.  We further focused on the GWAS signals to conduct gene annotation, colocalization with multi-tissue eQTL summary statistics of PigGTEx project and identification of enhancers and promoters using the publicly available data.  The genomic heritabilities of four novel resilience traits ranged from 0.09 to 0.41.  The pairwise genetic and phenotypic correlations ranged from 0.16 to 0.95 and from 0.05 to 0.36, respectively.  Twenty-seven SNPs were identified to be significantly associated with these resilience traits.  They were distributed on nine chromosomes (SSC1, SSC2, SSC6, SSC7, SSC8, SSC12, SSC14, SSC16 and SSC17).  After annotation, 39 QTLs and 49 candidate genes were identified.  Several of these are functionally relevant candidate genes including OTUD4, TIFA and CARD14, which are involved in the host immune response, disease susceptibility and signal transduction.  Eight unique SNPs were found to be causal in both GWAS and eQTL analyses across 15 tissues.  Notably, one SNP (rs80794541) was associated with eQTLs identified concurrently across seven tissues/cell types, including the macrophage cell type.  Furthermore, four significant SNPs (rs81467127, rs81356029, rs80794541 and rs81305085) were linked to the function of the primed enhancer, active element, and poised promoter in five pig tissues.  Using the porcine fibroblast HiC dataset, SNP (rs81356029) on SSC2 regulates the CARNS1 and SSH3, while SNP (rs80794541) on SSC7 regulates the H2AC6.  In this study, we generated four novel resilience traits and identified SNPs significantly associated with these resilience traits in a Duroc pig population.  GWAS signals were associated with candidate genes involving in the immune traits, and were linked to the crucial regulatory elements as well.  Our findings will contribute to elucidating the genetic mechanism that can enhance genome-enabled breeding and inform further research on resilience in domestic pigs.

Skeletal muscle satellite cells are stem cells characterized by their multipotency and capacity for in vitro proliferation.  However, primary skeletal muscle satellite cells demonstrate limited proliferative capacity in vitro, which impedes their investigation in poultry skeletal muscle research.  Cell immortalization techniques have emerged as valuable tools to address this limitation and facilitate the study of skeletal muscle satellite cell functions.  This study achieved the immortalization of chicken skeletal muscle satellite cells through the transduction of primary cells with TERT (telomerase reverse transcriptase) amplified from chicken (chTERT) using a lentiviral vector via telomerase activity reconstitution.  While the cells successfully overcame replicative senescence, complete immortalization was not achieved.  Initial functional characterization revealed that the proliferative properties and cell cycle characteristics of the immortalized chicken skeletal muscle satellite cell lines (ICMS) closely resembled those of chicken primary muscle satellite cells (CPMSCs).  Serum dependency analysis and soft agar assays confirmed that ICMS did not undergo malignant transformation.  Furthermore, induced differentiation experiments demonstrated preserved differentiation capacity in ICMS.  The established cell lines provide a fundamental framework for developing immortalized poultry cell lines and offer a cellular model for investigating poultry skeletal muscle-related functional genes.

Streptococcus suis serotype 2 (SS2) is an emerging zoonotic pathogen that causes meningitis in humans and pigs. It not only brings huge economic losses to the pig industry but also seriously threatens public health security. However, the mechanisms by which SS2 enters the brain and induces meningitis is not fully understood. Here, we investigated the role and mechanism of the SS2 collagenase-like protease (Clp) in promoting the passage of the bacterium across the blood-brain barrier (BBB). We found that SS2 Clp enhanced virulence and tissue colonization, and promoted the destruction of the BBB in mice. Compared with wild-type SS2, the ability of a Δclp mutant to cross human brain microvascular endothelial (hCMEC/D3) cell monolayers decreased, whereas the addition of recombinant protein rClp increased permeability. rClp also significantly promoted the adhesion of SS2 to hCMEC/D3, inhibited the expression of intercellular tight junction proteins ZO-1, Occludin, and Claudin-5 independent of its enzyme activity, and induced hCMEC/D3 apoptosis through the cell receptor ligand apoptosis and mitochondrial apoptosis pathways partly dependent on its enzyme activity, resulting in BBB destruction and increased permeability. Moreover, Clp increased macrophage (F4/80+), monocytes (F4/80-Ly6C+), and neutrophils (Ly6G+) infiltration into the brain after SS2 infection. Thus, SS2 Clp is required for the passage of the bacterium across the BBB, and the results, provide a theoretical basis for better prevention and treatment of SS2-induced meningitis.

Accurately mapping the spatial distribution of soil organic carbon (SOC) is crucial for guiding agricultural management and improving soil carbon sequestration, especially in fragmented agricultural landscapes.  Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes, its relationship with SOC remains unclear.  In this study, we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions.  Taking the Qilu Lake watershed in Yunnan, China, as a case study area and based on 216 topsoil samples collected from irrigation areas, we applied the extreme gradient boosting (XGBoost) model to investigate the contributions of vegetation indices (VI), brightness indices (BI), moisture indices (MI), and spectral transformations (ST, principal component analysis and tasseled cap transformation) to SOC mapping.  The results showed that ST contributed the most to SOC prediction accuracy, followed by MI, VI, and BI, with improvements in R² of 29.27, 26.83, 19.51, and 14.43%, respectively.  The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information.  The optimal SOC prediction model integrated soil properties, topographic factors, location factors, and landscape metrics, as well as remote sensing-derived variables, and achieved RMSE and MAE of 15.05 and 11.42 g kg^–1, and R² and CCC of 0.57 and 0.72, respectively.  The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture, vegetation status, soil brightness and SOC.  Compared with traditional linear regression models, interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC.  Overall, this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy.  Through interpretable machine learning, we can further elucidate the causes of SOC variation, which is important for sustainable soil management and agricultural practices.

Widespread soil acidification driven by nitrogen (N) fertilization and precipitation challenges the conventional notion of the long-term stability of soil inorganic carbon (SIC) in agroecosystems.  However, the changes in SIC with precipitation and N fertilization remain ambiguous.  Based on 4,000+ soil samples collected in the 1980s and 2010s and by developing machine learning models to fill the missing SIC of soil samples, this study generated 3,697 paired soil samples between the two periods and then investigated the cropland SIC change and explored its relationship with precipitation and N fertilization across the Sichuan Basin, China.  The results showed an overall SIC loss, with a decline of the mean SIC by 15.73%.  SIC change varied with initial soil pH and initial SIC and exhibited an exponential relationship with soil pH change, indicating the changing role of carbonates in providing acid-buffering capacity.  There was a parabolical relationship between the magnitude of SIC decline and N fertilizer rates, and low N fertilizer rates contributed to a reduction in SIC loss, while SIC loss was promoted by N fertilization occurred when N fertilizing rates exceeded 250 kg ha^–1 yr^–1.  The change in SIC showed a sinusoidal variation with precipitation, with 950 mm being the threshold controlling whether SIC increased or decreased.  Meanwhile, N fertilization did not alter the sinusoidal relationship between SIC change and precipitation.  In areas with rainfall<950 mm, the high N fertilizer rate did not cause SIC loss, while higher precipitation could also cause larger SIC loss in areas with lower N fertilizer rates.  These results suggest that SIC dynamics are jointly driven by precipitation and N fertilization and are controlled by acid-buffering mechanisms associated with initial pH and SIC, with precipitation being the predominant driver.  These findings emphasize the need for more regional soil observations and in-depth studies of SIC change and its mechanisms for accurately estimating SIC change.

Cropland abandonment has become a global issue that poses significant threats to sustainable cropland management, national food security, and the ecological environment.  Remote sensing technology is crucial for identifying and monitoring abandoned cropland in large-scale areas.  However, limited information is available on the effective identification methods and spatial distribution patterns of abandoned cropland in the hilly and gully regions.  This study introduced two methods - the land-use trajectory and normalized difference vegetation index (NDVI) time series - for monitoring abandoned cropland and evaluating its spatial distribution in Yanhe River Basin using Landsat-8 images from 2019 to 2021.  The results showed that using a random forest algorithm, high-precision annual land-use classifications were achieved with the generation of reliable land-cover samples and an optimized feature dataset.  The overall accuracy (OA) and Kappa coefficient of the land-use maps exceeded 90% and 0.88, respectively, demonstrating the effectiveness of the classification over three years.  These two distinct change detection methods were used to identify abandoned cropland in the study area, and their accuracy and effectiveness were evaluated.  The land-use trajectory method performed better than the NDVI time series method for extracting abandoned cropland, with an OA of 83.5% and an F1 score of 84.7%.  According to the land-use trajectory detection results, the study area had 164.6 km2 of abandoned cropland area in 2021, with an abandonment rate of 16.3%.  Furthermore, cropland abandonment mainly occurred in the northwestern part of the region, which has harsh natural conditions, while abandonment was rare in the southern and eastern regions.  Topography and landforms significantly influenced the spatial distribution of abandoned cropland, with most abandoned cropland located in mountainous regions with higher elevations and steeper slopes.  The abandonment rate generally increased with the elevation and slope.  These findings provide valuable references and guidance for selecting appropriate methods to identify abandoned cropland and analyze its spatial distribution in the hilly and gully regions.  Our proposed methods offer robust solutions for monitoring abandoned cropland and optimizing land-use change detection in similar regions with complex landforms.