Search Results (2,529)

Background/Objectives : The IUCN endangered spotted pond turtle (Geoclemys hamiltonii) demonstrates markedly reduced resistance to Aeromonas hydrophila-induced infections compared to the red-eared slider (Trachemys scripta). This study investigates the immunological basis for this disparity by analyzing infection outcomes and splenic transcriptomes of both species post-pathogen challenge. Methods: In a preliminary experiment, 32 turtles (16 G. hamiltonii and 16 T. scripta) were exposed to A. hydrophila. Results: G. hamiltonii developed skin ulcer syndrome at a significantly higher incidence (81.25%) than T. scripta (12.5%) (p < 0.05). Comparative transcriptomic analysis identified 19 differentially expressed immune-related genes, with qPCR validation across five tissues (heart, liver, spleen, intestine, blood) revealing pronounced interspecies differences in IL18, STAT1, IFIH1, and CD28 expression. Notably, IL18 and its downstream effector STAT1 were robustly upregulated in T. scripta but were considerably lower in G. hamiltonii, correlating with delayed IFN-γ pathway activation and impaired epidermal barrier repair. Concurrently, CD28 upregulation in T. scripta facilitated rapid T-cell-mediated pathogen clearance, whereas its delayed induction in G. hamiltonii hindered adaptive immunity. These findings implicate dysregulated innate (IL18/STAT1) and adaptive (CD28) immune pathways as key determinants of G. hamiltonii’s susceptibility to bacterial infection. Conclusions: Despite the critical conservation status of G. hamiltonii, the immunological basis underlying its heightened susceptibility to bacterial infections remains largely unexplored; this study addresses this gap by comparing the splenic transcriptomes of G. hamiltonii and T. scripta following A. hydrophila challenge, identifying the dysregulated IL18-STAT1 Immune Axis and CD28-mediated adaptive immunity as key determinants, thereby providing actionable immune targets for conservation breeding and susceptibility screening in this endangered species. Full article

Automated pig-welfare monitoring needs scalable, non-invasive signals that work across ages and individuals. A key methodological contribution of this study is the use of subject-wise validation, which ensures generalization to unseen animals and prevents inflated accuracy caused by growth-related and individual ‘voice’ differences. Vocalizations can help, but growth and individual “voice” differences can confound distress patterns and overstate accuracy without subject-wise validation. In our study, we explicitly accounted for individual variability by including animal identity as a random effect in mixed models and by using grouped cross-validation, where models were tested only on pigs not seen during training. This approach ensures that the reported accuracy reflects generalization across different individuals rather than memorization of specific vocal signatures. We analyzed 2221 vocal samples from 40 pigs (20 males, 20 females) recorded across four growth phases (farrowing, nursery, growing, finishing) under six conditions (pain, hunger, thirst, cold stress, heat stress, normal). Acoustic features extracted in Praat included energy, duration, intensity, pitch, and formants (F1–F4). Using blockwise variance decomposition, we quantified contributions of distress exposure, growth phase, and sex, and estimated the additional variance explained by animal identity. Distress exposure dominated intensity and spectral traits, particularly Formant 2, whereas the growth phase produced systematic shifts in duration and pitch. Animal identity added a modest but consistent increment in explained variance (~+0.02–0.03 R² beyond sex, phase, and distress). For prediction, we used 5-fold cross-validation grouped by animal. A Random Forest achieved a modest balanced accuracy of 0.609 and macro-F1 of 0.597; pain was most separable (recall 0.825), while other states showed moderate recall, indicating overlap. These results support hierarchical acoustic encoding of distress and establish a benchmark for precision welfare monitoring. Furthermore, they highlight that resolving complex physiological overlaps, such as heat stress and resource competition, requires a shift from unimodal acoustic models to multimodal Precision Livestock Farming (PLF) systems that integrate bioacoustics with continuous environmental and behavioral data streams. Full article

Internet Gaming Disorder (IGD) has become a major behavioral health concern among adolescents, yet current assessment tools remain limited. These tools often fail to capture the disorder’s complex symptom variations and lack clinical interpretability. This study, taking an interdisciplinary approach that combines clinical psychology and psychometrics, summarizes recent progress in understanding adolescent IGD and the development of its assessment methods. We compare the diagnostic criteria of the DSM-5 TR and ICD-11 and argue that the nine DSM-5 TR criteria are particularly suited for transformation into distinct diagnostic attributes due to their detailed and actionable nature. We then review the strengths and weaknesses of Classical Test Theory (CTT), Item Response Theory (IRT), and Cognitive Diagnostic Models (CDMs) in assessing IGD. The review emphasizes the limitations of total-score and single latent-trait approaches in capturing the disorder’s multidimensional symptoms. Based on these insights, we propose a conceptual assessment framework, Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT), that integrates CDMs with computerized adaptive testing. Rather than presenting an empirically validated system, this framework offers a theoretically grounded proposal that specifies the key components, logical relationships, and methodological pathways necessary for advancing precision assessment of adolescent IGD. CD-CAT uses a system of attributes and a Q-matrix based on the DSM-5 TR criteria to efficiently classify IGD symptoms in adolescents, reducing the number of items required while enhancing clinical relevance. Lastly, we discuss the theoretical contributions of the proposed framework, acknowledge its limitations as a conceptual proposal, and outline directions for future empirical research. Full article

The desert ecosystem harbors a resilient microbial community that sustains plant life under extreme stress. Understanding the endophytic microbiota of desert flora provides key insights into how these microorganisms enable plant survival and maintain ecological balance in arid landscapes. To date, the endophytic bacterial communities of dominant desert plants in the Arabian Peninsula have not been comprehensively characterized. Here, we investigated the endophytic microbiota of five co-adapted desert species, namely, Schweinfurthia papilionacea, Sesuvium verrucosum, Ochtocloa compressa, Helianthemum nummularium, and Convolvulus arvensis. These plants coexist in hyper-arid habitats and exhibit exceptional tolerance to drought, salinity, and nutrient scarcity. We hypothesized that, despite their phylogenetic divergence, these plants host functionally convergent microbial communities shaped by desert selection pressures. Using 16S rRNA gene amplicon sequencing, we obtained 3.4 million high-quality reads from 25 samples. Clustering at 97% similarity revealed 35 phyla and 17 dominant genera, highlighting notable microbial richness and ecological complexity. Alpha-diversity indices showed comparable species richness across hosts, while beta-diversity indicated community differentiation driven by environmental filtering. The dominant phyla included Pseudomonadota, Actinomycetota, Cyanobacteriota, and Bacillota, reflecting microbial adaptation to extreme desert conditions. Functional pathway prediction revealed enrichment of genes associated with DNA repair and protein turnover, suggesting metabolic flexibility and enhanced survival under stress. Overall, this study provides a comparative metagenomic insight into the endophytic bacterial communities of five desert plant species, uncovering a consistent pattern of functional convergence across diverse hosts. The findings suggest the presence of shared functional traits among the endophytic microbiota examined here, offering preliminary evidence for microbial contributions to plant resilience in arid environments. Full article

Chickpea (Cicer arietinum L.) is a globally important legume crop whose grain yield is strongly influenced by environmental and agronomic variability. This study aimed to predict chickpea grain yield using artificial neural networks (ANNs) and to identify key traits associated with yield formation across different genotypes under semi-arid conditions. The dataset consisted of 96 chickpea genotypes evaluated over two growing seasons (2022–2023) in Sivas, Türkiye. The results demonstrated that reproductive traits, particularly seed weight per plant, number of pods per plant, and number of seeds per plant, were the most influential factors determining grain yield. Environmental variability also contributed significantly to yield prediction, highlighting the importance of genotype–environment interactions. The developed ANN model showed high predictive accuracy, indicating its robustness in capturing complex relationships among yield-related traits. Beyond prediction, the model provides biologically meaningful insights into trait prioritization, supporting its application in chickpea breeding programs. Overall, the findings suggest that ANN-based approaches can serve as effective decision-support tools in precision agriculture by enabling accurate yield estimation, facilitating the selection of high-performing genotypes, and identifying key breeding traits for sustainable crop improvement. Full article

This study evaluated morphological variation in 45 accessions of cultivated Perilla frutescens var. frutescens (PFF) and var. crispa (PFC) collected from South Korea and Japan, together with their weedy counterparts, var. frutescens (WPFF) and var. crispa (WPFC) from South Korea, using ten quantitative and ten qualitative traits. Clear morphological differentiation was observed between cultivated and weedy forms in both varieties, particularly in pigmentation, plant fragrance, and seed-related traits. PFF cultivars were characterized by predominantly green pigmentation, a typical frutescens aroma, and significantly larger and heavier seeds, suggesting stronger phenotypic differentiation in leaf and seed utilization. In contrast, PFC cultivars showed substantial morphological overlap with WPFC and WPFF accessions, indicating comparatively weaker phenotypic differentiation. Principal component analysis showed that the first principal component (PC1) explained 25.7% of the total phenotypic variance and was strongly associated with five quantitative traits (plant height, inflorescence length, floret number, seed size, and 100-seed weight) and five qualitative traits (adaxial and abaxial leaf color, flower color, seed color, and seed hardness). Along PC1, PFF cultivars formed a well-defined cluster, whereas PFC cultivars and the weedy WPFF and WPFC accessions exhibited broader dispersion, reflecting greater morphological variability. These results provide morphological insights into the differentiation between cultivated and weedy Perilla accessions and indicate potential domestication-related patterns. However, these interpretations are based primarily on morphological observations, and further genetic and evolutionary studies will be necessary to clarify the domestication history of these taxa. The identified trait complexes provide a useful phenotypic foundation for marker-assisted breeding, informed cultivar selection, and effective germplasm conservation and management. Full article

Acinetobacter baumannii is a leading cause of healthcare-associated infections and is classified among the highest-priority antimicrobial-resistant pathogens. Its clinical success reflects the convergence of antimicrobial resistance (AMR) and biological traits that promote environmental persistence and transmission. Acinetobacter baumannii has undergone a remarkable transformation over the past few decades, evolving from a relatively obscure environmental bacterium into a globally recognized multidrug-resistant pathogen. Its prevalence in healthcare settings, particularly intensive care units, has made it a leading cause of ventilator-associated pneumonia, bloodstream infections, wound infections, and urinary tract infections. Beyond its antibiotic resistance, the bacterium’s ability to persist in hospital environments and adapt to host defences has amplified its clinical significance. Recent research has uncovered complex networks of virulence factors, regulatory systems, and metabolic strategies that enable A. baumannii to thrive in hostile environments and evade host immunity, providing new insights into its pathogenesis and potential therapeutic vulnerabilities. This review summarizes the main mechanisms underlying its pathogenicity, including desiccation tolerance, biofilm formation, disinfectant resistance, metal acquisition, motility, and the ability to enter viable but non-culturable states. In A. baumannii, AMR functions as a pathogenesis-adjacent trait, enhancing survival and clonal dissemination through genomic plasticity, resistance islands, efflux systems, and envelope remodeling. Key resistance pathways involve carbapenem-hydrolyzing oxacillinases, metallo-β-lactamases, permeability defects, and multidrug efflux, often coexisting within high-risk clones. From a clinical perspective, management of carbapenem-resistant strains requires accurate infection diagnosis, reliable susceptibility testing, site-specific and PK/PD-optimized therapy, and early reassessment. Overall, the success of A. baumannii reflects the integration of resistance and persistence within healthcare ecosystems, highlighting the need for coordinated strategies combining stewardship, infection control, improved diagnostics, and anti-biofilm or anti-virulence approaches. Full article

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R² = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R² = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R² = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices. Full article

Genome-wide association studies (GWASs) are powerful and flexible tools for identifying single nucleotide polymorphisms (SNPs) associated with quantitative traits (yield, stress resistance) in plants. Variable selection and machine learning are two effective approaches in GWAS. However, both face limitations in complex, noisy data analysis in the big-data era. In this study, we integrated variable selection and machine learning under the mixed linear model framework, proposing a novel method, the improved LASSO screening and sparse Bayesian learning algorithm (ILSBL). The ILSBL first corrects the polygenic and environmental noise, then reduces genotypic dimensionality by LASSO-based variable selection, and finally performs parameter estimation using sparse Bayesian learning. Two simulation experiments and association analyses of three flowering-time-related traits in Arabidopsis thaliana were conducted to validate the new algorithm. The results showed that, compared to established methods, the ILSBL exhibited flexibility in simulation studies and maintained robust performance under complex genetic backgrounds, achieving a favorable balance among statistical power, parameter estimation accuracy, runtime efficiency, and false-positive rate. The analysis of the real Arabidopsis datasets further confirmed the advantages of ILSBL for GWASs, with 30 candidate genes adjacent to significant quantitative trait nucleotides (QTNs) associated with flowering-related traits. These results provide valuable insights for a better understanding of the genetic basis underlying flowering-related traits in Arabidopsis. Full article

This study investigates the genetic diversity, population structure, and adaptive differentiation of Yunnan native cattle (YNC) using whole-genome SNP data from 457 individuals, representing eight cattle populations and two closely related bovine species (Zhongdian yak and Dulong gayal). Genetic diversity analyses revealed a distinct latitudinal gradient from north to south, with the highest diversity observed in the northern Diqing (DQC) and Zhaotong (ZTC) populations. The observed population structure was largely consistent with geographic distribution, identifying distinct ancestral components and complex admixture patterns. Genome-wide selective sweep scans revealed several key candidate genes underlying local adaptation. Notably, GRIA4 and DUOXA2 were associated with cold tolerance in northern populations, and ST3GAL3 and MST1 were implicated in heat stress adaptation in southern populations. Genome-wide balancing selection analyses further detected significant loci, such as MGST1 and SLC36A1, where divergent haplotype frequencies reflected differential selective pressures on milk-related traits between northern and southern populations. Additionally, we detected signals of historical introgression from Zhongdian yak into DQC cattle, highlighting the introgressed gene SLIT3 as a potential candidate associated with high-altitude thermogenesis. Collectively, these results provide a comprehensive genomic framework for the management and conservation of indigenous bovine genetic resources in Southwest China. Full article

The soil microbiome is a complex assemblage of microorganisms that are important in restoring and maintaining soil function, ecosystem stability, and sustainable agroecosystem development. However, soil microbial responses to environmental or land-use gradients in agroecosystems and the consequent implications for soil functional integrity and sustainable agroecosystem development remain poorly understood. In this review, we present the current state of the science on: (1) shifts in microbial community composition in response to environmental or land-use gradients within conventional dryland small grains farms, temperate evergreen forests, and riparian areas in the inland Pacific Northwest (iPNW) where the precipitation regime is considered mediterranean, and (2) microbial traits link to soil function as a response to soil health management. Upon conclusion of this review, the lack of information is still apparent in terms of understanding how to intentionally manage the soil microbiome after land-use conversions, especially given that soil health and ecosystem services are driven by the soil microbiome. This review, therefore, motivates future research into the primary land management regimes to better link specific microbial taxa to soil microbial and ecosystem processes across land-use gradients. Full article

Background: Improving grain yield in wheat remains a top priority, requiring integrated breeding and genetic strategies. This complexity poses a major challenge, driven by quantitative polygenic inheritance, environmental influence, and intricate genetic interactions. We investigated genetic factors and their interactions for agronomic and yield traits in two high-yielding winter wheat cultivars adapted to the US Southern Great Plains. Methods: A bi-parental mapping population consisting of 221 F₇ recombinant inbred lines (RIL) derived from ‘TAM 204’ and ‘Iba’ was evaluated for three years in 11 Texas environments. Both parents and RIL population were genotyped on Illumina NovaSeq 6000 and sequences were aligned to IWGSC RefSeq v1.0 using Bowtie2 for SNP calling. For QTL analyses, each trait was analyzed by individual environment, across multiple environments and mega-environments. Results: A total of 86 QTL were mapped for five traits and among them 32 were consistent in more than one environment or analysis. Among consistent QTL, four were pleiotropic to more than one agronomic or yield traits mapped on chromosomes 2B (57.18, 59.47 Mb) and 2D (29.34, 40.64 Mb). The consistent QTL on chromosome 2D (29.34 Mb) was pleiotropic to GYLD, DTH, TW, TKW and explained maximum phenotypic variation for all traits, representing photoperiod gene (Ppd-D1). Another QTL on chromosome 2D (40.64 Mb) was pleiotropic to GYLD and TW and based on the physical position comparisons it likely reflects a unique locus in Iba. The pleiotropic consistent QTL Qgyld.tamu.2B.59 from TAM 204 represents Ppd-B1 gene. Moreover, it is more likely that Qdth.tamu.5B.575 represents the Vrn-B1 gene in Iba. A total of 23 digenic epistatic interactions involved consistent QTL for all traits. Amongst these, epistatic interactions between the consistent QTL on 2B (57.18 Mb) and 2D (29.34 Mb) were observed for GYLD, DTH and TKW. Conclusions: Our findings revealed key allelic diversity and interaction effects in elite wheat cultivars, paving the way for marker development for identified pleiotropic loci and implementation in marker-assisted selection and recombination breeding. Full article

Microplastics can bind with toxic metals via surface complexation and chelation, forming combined pollutants. However, research regarding the toxicological impacts of these combined pollutants on soil fauna remains limited. This study employed Folsomia candida in a 28-day incubation experiment to investigate the ecotoxicological effects of combined pollution by polylactic acid microplastics (PLA-MPs) and thallium (Tl) on the functional traits of Folsomia candida, including biology, morphology, and gut microbiota. The results showed that the combined effects of PLA-MPs and Tl on these functional traits were characterized by amplified toxicity and trait-specific responses. Morphological traits exhibited lower sensitivity to the pollution treatments compared to other indicators. Exposure to high-concentration PLA-MPs (10%) significantly affected mortality and fecundity, and reduced gut bacterial diversity. Conversely, low-concentration Tl (1 mg/kg) significantly inhibited body length and antenna length while increasing gut bacterial diversity. Structured equation modeling further revealed that the pollution treatments exerted significant negative effects on the functional traits of Folsomia candida, both directly and indirectly by altering soil properties and soil microbiota. These findings provide valuable insights into the ecotoxicological effects of combined PLA-MPs and Tl pollution on soil fauna, contributing to ecological health risk assessments of microplastics and toxic metals in terrestrial ecosystems. Full article

Psychopathy is a complex personality trait involving emotional and behavioral deficits that often overlap with alexithymia and reduced empathy. While it is reasonable to assume that the cognitive and behavioral traits associated with this construct may be influenced by specific sociocultural factors, research examining these cross-cultural variations remains scarce. In this cross-sectional study, we examined the relationship between psychopathy traits, empathy, and alexithymia in Brazilian (n = 171) and British (n = 167) adults. Participants completed the Levenson Self-Report Psychopathy Scale, the Basic Empathy Scale, and the Toronto Alexithymia Scale. British participants scored significantly higher on primary, secondary, and total psychopathy, as well as on difficulties describing feelings, compared to Brazilians. Regression analyses indicated that affective empathy and alexithymia dimensions were statistically associated with psychopathy scores in both groups. The regression models accounted for substantially more variance in primary psychopathy, marked by narcissism, grandiosity, and emotional detachment, in the British group than in the Brazilian one (36.4% vs. 13.4%, p < 0.05). Our findings are consistent with sociocultural differences in psychopathy traits and highlight the importance of investigating these constructs from a cross-cultural perspective to better characterize contextual differences and refine assessment and intervention. Full article

In 2013, plants tentatively identified as Potamogeton nodosus Poir. were discovered in the Biebrza River (NE Poland). In this study, the authors confirm the presence of P. nodosus by collecting new specimens at the original location and analyzing their microscopic characteristics, an essential step due to significant overlap in macromorphological traits with the closely related P. × fluitans complex. Additionally, new occurrences of the species within Biebrza National Park are reported, and the possibility that its spread is linked to rising river water temperatures is discussed. The authors provide evidence of an increasing average water temperature in the Biebrza River and of a northbound expansion of P. nodosus in Europe. Given similar trends observed elsewhere in Northern Europe, it is likely that P. nodosus will continue to expand its range northward in response to ongoing climate change. Full article