Search Results (1,577)

The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the genetic mechanisms underlying adaptations in invasive species are inadequately understood. Taenioides sp. is an invasive worm goby, originally endemic to brackish waters in the estuaries of Southeastern China, and now colonizes multiple inland freshwaters of North China within decades as a byproduct of the East Route of South-to-North Water Transfer (ESNT) project. However, the molecular mechanisms underlying their adaptations to the climate of North China, especially the temperature regime, are unknown. Here, we performed genomic resequencing analysis to assess genetic diversity and population genetic structure, and further investigated the genomic signatures of local adaptation in the invasive population of Taenioides sp. during their northward invasion. We revealed that all invasive populations exhibited no genetic differentiation but low gene flow and an obvious signal of population bottleneck. Yangtze River estuary may serve as the source population, while Gaoyou Lake serves as a potential bridgehead of the invasion. Selective sweep analyses revealed 117 genomic regions, containing 673 candidate genes, under positive selection in populations at the invasive front. Redundancy analysis suggested that local temperature variables, particularly the monthly minimum temperature, represent critical evolutionary forces in driving adaptive divergence. Functional enrichment analyses revealed that multiple biological processes, including metabolism and energy production, substance transmembrane transport, and neural development and synaptic transmission, may play important roles in adaptation to regional temperature conditions. Our findings revealed a scenario of adaptive evolution in teleost species that underpins their regional climate adaptation and successful establishment of invasive populations in a human-facilitated invasion context. Proper management strategies should be established to manage Taenioides sp invasion as soon as possible. Full article

Background: The precise role of RNA modification in post-traumatic stress disorder (PTSD) remains incompletely understood. This study aims to elucidate the effects of five common RNA modifications in PTSD, specifically m⁶A, m⁵C, m¹A, m⁷G, and ψ. Methods: We extracted data from the GEO repository to conduct a series of bioinformatics analyses. These included differential analysis to identify key regulators of five common RNA modifications, model construction using random forest (RF), least absolute shrinkage and selection operator (LASSO), and nomogram techniques, as well as consensus clustering of RNA modification subtypes. Furthermore, GO enrichment analysis was performed on DEGs associated with various RNA modification patterns. Immune cell infiltration was assessed using PCA and ssGSEA. RT-qPCR was performed to validate RNA modification-related genes (RMGs). Results: Twenty-one differentially expressed RMGs were identified. LASSO and RF intersection yielded eight signature genes (YTHDC1, IGFBP1, IGF2BP1, ALKBH5, NSUN4, TET2, TET3, WDR4) that robustly diagnosed PTSD (AUC = 0.804). Furthermore, these feature genes were validated using RT-qPCR, which was basically consistent with the results of bioinformatics analysis. Consensus clustering analysis may reveal two distinguishable subtypes: clusterA marked by high immunoinflammation, and clusterB characterized by high-neuroendocrine dysregulation. Conclusions: RMGs may play a crucial role in the pathogenesis of PTSD. Analyzing RNA modification patterns could offer potential diagnostic markers and help to guide immunotherapeutic approaches or neurotransmitter system interventions for PTSD in the future. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Analysis of selection signatures in the genomes of farm animals enables the detection of genomic regions affected by selection and contributes to the identification of genes underlying adaptive and productive traits. This research aimed to identify loci under selection pressure and to detect candidate genes in Dzhalgin Merino sheep by performing a comparative genomic analysis with the related Australian Merino and Rambouillet breeds. A total of 293 animals were included in the analysis, comprising Dzhalgin Merino (n = 53), Australian Merino (n = 50), Australian Industry Merino (n = 88), and Rambouillet (n = 102). Whole-genome SNP genotyping data for Dzhalgin Merino were generated within this study, while data for Australian Merino, Australian Industry Merino, and Rambouillet were obtained from the SheepHapMap project. For the purposes of analysis, Australian Merino and Australian Industry Merino were combined into a single group (n = 138). To enhance the reliability of the results, three independent methods were employed to detect selection signatures: the fixation index (FST), analysis of linkage disequilibrium variation (varLD), and the cross-population number of segregating sites by length (xp-nSL). The study showed that Dzhalgin Merino have unique genetic signatures potentially associated with adaptation and productivity, which opens up new opportunities for their selection. The identified genes can become the basis for developing new breeding programs aimed at improving both the productive qualities and the adaptive abilities of the breed. Further research should be aimed at a detailed investigation of gene structure within loci under selection pressure and at clarifying the mechanisms by which these genes influence animal phenotypes. A total of 185 genes were identified within genomic regions exhibiting selection signatures. Among these, particular attention was given to EPHA6, MLLT3, ROBO1, KIAA0753, MED31, SLC13A5, and ELAVL4, which are involved in biological processes such as growth, development, and reproduction. The identified genes represent potential targets for breeding programs aimed at increasing productivity and adaptive capacity of the breed. Full article

Background: Hepatocellular carcinoma (HCC) is a highly aggressive and heterogeneous malignancy which predominantly arises in the setting of cirrhosis, and there is lack of models to predict prognosis in cirrhotic HCC. This study aims to develop and validate a prediction model based on the pathomics signature and clinicopathological characteristics to predict the prognosis of HCC with cirrhosis. Methods: In this multicenter, retrospective study, 389 patients were enrolled (training cohort: 268; independent validation cohort: 121). A total of 351 pathomics features were extracted from digital H-E–stained images, and a pathomics signature (PS_HCC) was constructed using a least absolute shrinkage and selection operator Cox regression model. Then two nomograms were established by combining the PS_HCC and clinicopathological characteristics. Further validation was performed in the validation cohort. Results: This study included 389 patients. A 24 feature-based PS_HCC was constructed. A higher PS_HCC was significantly associated with worse OS and DFS in both the training (OS: hazard ratio [HR], 4.341 [95% CI, 3.109–6.062]; DFS: HR, 3.058 [95% CI, 2.223–4.207]) and validation (OS: HR, 4.145 [95% CI, 2.357–7.291]; DFS: HR, 3.395 [95% CI, 2.104–5.479]) cohorts (p < 0.001 for all comparisons). Multivariable analysis revealed that the PS_HCC was an independent factor associated with OS and DFS. Integrating the PS_HCC into pathomics nomograms resulted in better performance for prognosis prediction than the traditional model in both cohorts. Conclusions: The PS_HCC may serve as a reliable surrogate for prognosis, and the nomograms offer promising tools to predict individual outcomes, facilitating personalized management of HCC with cirrhosis. Full article

The wild yak (Bos mutus) is a flagship species on the Qinghai–Tibet Plateau, possessing significant ecological functions and conservation value. Using single-nucleotide polymorphism markers from whole-genome resequencing, we systematically analyzed golden wild yak (n = 37), common wild yak (n = 106), and domestic yak (Bos grunniens) (n = 20) to characterize the population genetic structure and adaptive selection signals in the golden wild yak. Genetic diversity analyses revealed that the golden wild yak had the lowest nucleotide diversity (π = 0.00148) and the highest inbreeding coefficient (F_Hom = 0.043). Population structure analyses integrating principal component analysis, phylogenetic tree, and ancestral component clustering indicated that the golden wild yak formed a relatively independent evolutionary lineage. However, its genetic differentiation from sympatric common wild yak population was limited (fixation index = 0.031). Selective sweep analysis identified a set of candidate positively selected genes in the golden wild yak genome associated with key traits and physiological functions, including coat color (TYRP1), hypoxia adaptation (MYH11, POLQ), reproductive function (SLC9C1, SPAG16, CFAP97D1), and immune response (CASP8, PGGT1B, BIRC6). Overall, our study reveals a distinct genetic background and selection signatures in the golden wild yak and provides genomic insights to inform the conservation and management of the wild yak. Full article

Despite the increasing recognition of the role of urban orchard ecosystems in sustainable urban development, the mechanistic understanding of how tree species soil biochemical heterogeneity drives microbial community assembly, the spatial patterns governing microbe-environment interactions, and their collective contributions to ecosystem multifunctionality remain poorly characterized. This study investigated how Prunus species and soil depth affect microbial biodiversity and metabolomic signatures in an urban orchard in Cluj-Napoca, Romania. Soil samples were collected from five fruit tree species (apricot, peach, plum, cherry, and sour cherry) across three depths (0–10, 10–20, and 20–30 cm), resulting in 225 samples. The microbial community structure was analyzed through phospholipid fatty acid (PLFA) profiling, whereas the soil metabolome was analyzed by mass spectrometry techniques, including gas chromatography–mass spectrometry (GC–MS/MS) and MALDI time-of-flight (TOF/TOF) MS, which identified 489 compounds across 18 chemical classes. The results revealed significant tree species-specific effects on soil microbial biodiversity, with bacterial biomarkers dominating and total microbial biomass varying among species. The soils related to apricot trees presented the highest microbial activity, particularly in the surface layers. Metabolomic analysis revealed 247 distinct KEGG-annotated metabolites, with sour cherry exhibiting unique organic acid profiles and cherry showing distinctive quinone accumulation. Depth stratification influenced both microbial communities and metabolite composition, reflecting oxygen gradients and substrate availability. These findings provide mechanistic insights into urban orchard soil biogeochemistry, suggesting that strategic species selection can harness tree species-soil microbe interactions to optimize urban soil ecosystem services and enhance urban biodiversity conservation. Full article

Emerging evidence highlights the critical role of phagocytosis-related genes in CRC progression, underscoring the need for novel phagocytosis-based prognostic models to predict clinical outcomes. In this study, a four-gene (SPHK1, VSIG4, FCGR2B and FPR2) signature associated with CRC prognosis was developed using single-sample gene set enrichment analysis (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression, and univariate Cox analysis. Pathway enrichment analysis was conducted on the prognostic genes, along with evaluations of the tumor microenvironment and sensitivity to immunotherapy and chemotherapy across the high- and low-risk groups. Prognostic gene validation was performed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) using CRC cDNA and tissue microarrays. High-risk patients showed enhanced responsiveness to immunotherapy, while chemotherapy sensitivity varied across risk subgroups. qRT-PCR results revealed upregulation of SPHK1 and FPR2 in cancer tissues, whereas FCGR2B and VSIG4 were downregulated. IHC assays confirmed increased SPHK1 and FPR2 expression in cancer samples. Single-cell RNA sequencing analysis demonstrated a decrease in SPHK1 and FCGR2B, while VSIG4 and FPR2 progressively increased during macrophage differentiation. These findings provide a potential framework for targeted therapy. Full article

Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advantage of directly providing more objective and less susceptible-to-manipulation results compared to traditional polygraph methods. To this end, this study proposes a novel multi-scale entropy approach by fusing fuzzy entropy (FE), time-shifted multi-scale fuzzy entropy (TSMFE), and hierarchical multi-band fuzzy entropy (HMFE), which enables the multidimensional characterization of EEG signals. Subsequently, using machine learning classifiers, the fused feature vector is applied to lie detection, with a focus on channel selection to investigate distinguished neural signatures across brain regions. Experiments utilize a publicly benchmarked LieWaves dataset, and two parts are performed. One is a subject-dependent experiment to identify representative channels for lie detection. Another is a cross-subject experiment to assess the generalizability of the proposed approach. In the subject-dependent experiment, linear discriminant analysis (LDA) achieves impressive accuracies of 82.74% under leave-one-out cross-validation (LOOCV) and 82.00% under 10-fold cross-validation. The cross-subject experiment yields an accuracy of 64.07% using a radial basis function (RBF) kernel support vector machine (SVM) under leave-one-subject-out cross-validation (LOSOCV). Furthermore, regarding the channel selection results, PZ (parietal midline) and T7 (left temporal) are considered the representative channels for lie detection, as they exhibit the most prominent occurrences among subjects. These findings demonstrate that the PZ and T7 play vital roles in the cognitive processes associated with lying, offering a solution for designing portable EEG-based lie detection devices with fewer channels, which also provides insights into neural dynamics by analyzing variations in multi-scale entropy. Full article

Background: Cancer stem cells (CSCs) are key drivers of tumorigenesis and metastasis. However, the precise roles of CSC-associated genes in these processes remain unclear. Methods: This study integrates cancer stem cell biomarkers and clinical data from The Cancer Genome Atlas (TCGA) specific to bladder cancer (BLCA). By combining differentially expressed genes (DEGs) from TCGA-BLCA samples with CSC-related biomarkers, we conducted comprehensive functional analyses and developed an 8-gene prognostic signature through Cox regression, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression. This model was validated with GEO datasets (GSE13507 and GSE32894), and the single-cell RNA seq dataset GSE222315 was subsequently analyzed to characterize the signature genes and elucidate their interactions. And a nomogram was created to stratify TCGA-BLCA patients into risk categories. The ‘oncoPredict’ algorithm based on the GDSC2 dataset assessed drug sensitivity in BLCA. Result: From the TCGA cohort, 665 CSC-related genes were identified, with 120 showing significant differential expression. The 8-gene signature (ALDH1A1, CBX7, CSPG4, DCN, FASN, INHBB, MYC, NCAM1) demonstrated strong predictive power for overall survival in both TCGA and GEO cohorts, as confirmed by Kaplan–Meier and ROC analyses. The nomogram, integrating age, tumor stage and risk scores, demonstrated high predictive accuracy. Additionally, the oncoPredict algorithm indicated varying drug sensitivities across patient groups. Based on retrospective data, we identified a novel CSC-related prognostic signature for BLCA. This finding suggests that targeting these genes could offer promising therapeutic strategies. Full article

Background: Prostate cancer is the most commonly occurring cancer amongst men. The detection and treatment of this cancer is therefore of great importance. The severity level of this cancer, which is established as a score in the Gleason Grading Group (GGC), guides the treatment of the cancer. Methods: In this paper, traditional machine learning (ML) classification methods such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), which have recently been shown to accurately identifying biomarkers for computational biology, are leveraged to find potential biomarkers for the different GGC scores. A ML framework that maps the Gleason Grading Group (GGG) into five severity levels—low, intermediate-low, intermediate, intermediate-high, and high—has been developed using the above methods. The microarray data for this ML method have been derived from immunohistochemical tests. The study includes severity level-wise biomarker identification, incorporating missing value imputation, class imbalance handling using the SMOTE-Tomek link method, and stratified k-fold validation to ensure robust biomarker selection. Results: The framework is evaluated on prostate cancer tissue microarray gene expression data from 1119 samples. A combination of high-aggressive and low-aggressive signatures are used in four experimental setups. The results demonstrate the effectiveness of the approach in distinguishing between critical biomarkers with highly accurate models, obtaining 96.85% accuracy using the XGBoost method. Conclusions: Leveraging ML gives a potential ground to involve the domain experts and the satisfactory results have approved that. For the future physician-in-the-loop approach can be tested to ensure further diagnosis impact. Full article

Chronic social isolation (CSIS), a known risk factor for the development of major depressive disorders, is associated with hippocampal dysfunction. In rodent models, CSIS produces two phenotypes: CSIS-susceptible, which develop depressive- and anxiety-like behaviors, and CSIS-resilient, which maintain normal behavior despite stress. However, the biological mechanisms underlying resilience to stress remain elusive. Mitochondria, as central regulators of neuronal energy metabolism and redox balance, are potential mediators of stress susceptibility and resilience. This review summarizes comparative proteomic analyses of hippocampal nonsynaptic mitochondria (NSM) and synaptosome-enriched mitochondria from CSIS-susceptible and CSIS-resilient rats along with controls. In NSM of resilient rats relative to susceptible rats, remodeling enhanced energy production, limited reactive oxygen species, stabilized phosphate transport, and promoted removal of damaged components. Compared with controls, these changes optimized energy production, and selectively downregulated oxidative stress-promoting proteins. Conversely, synaptosome-enriched mitochondria from resilient rats showed downregulation of proteins related to synaptic energy metabolism and redox balance relative to CSIS-susceptible rats, but demonstrated upregulation of bioenergetic and antioxidant enzymes, molecular chaperones, and neuroprotective factors compared with controls. These proteomic signatures both highlight mitochondrial adaptability in promoting stress resilience and identify mitochondria as promising targets for the development of novel antidepressant therapies. Full article

Real-time insect monitoring is essential for sustainable agriculture and biodiversity conservation. The traditional method of attracting insects to colored glue traps and manually counting the catch is time-intensive and requires specialized taxonomic expertise. Moreover, these traps are often lethal to pests and beneficial insects alike, raising both ecological and ethical concerns. Camera-based trap designs have recently emerged to lower the amount of manual labor involved in determining insect species, yet they are still deadly to the catch. This study presents the design and evaluation of a non-lethal acoustic monitoring system capable of detecting and classifying insect species based on their sound signatures. A first prototype was developed with a focus on low self-noise and suitability for autonomous field deployment. The system was initially validated through laboratory experiments, and subsequently tested in six rapeseed fields over a 25-day period. More than 3400 h of acoustic data were successfully collected without system failures. Key findings highlight the importance of carefully selecting each component to minimize self-noise, as insect sounds are extremely low in amplitude. The results also underscore the need for efficient data and energy management strategies in long-term field deployments. This paper aims to share the development process, design decisions, technical challenges, and practical lessons learned over the course of building our IoT sensor system. By outlining what worked, what did not, and what should be improved, this work contributes to the advancement of non-invasive insect monitoring technologies. Full article

Salinity is a pivotal environmental factor that governs crustacean survival and development through its regulatory effects on key physiological processes, including osmoregulation and metabolic homeostasis. In the mud crab Scylla paramamosain, salinity tolerance of the megalopa plays an important role in larval survival rates and aquaculture yield. Here, we employed a combined transcriptomic and proteomic strategy to comprehensively dissect the molecular adaptive mechanisms of S. paramamosain megalopa exposed to acute and prolonged low-salinity stress (8‰) compared to control condition (17‰). Illumina-based transcriptome sequencing generated 81.71 Gb of high-quality clean data, which were assembled into 42,210 unigenes. LC-MS/MS-based proteomic profiling identified 51,390 unique peptides, corresponding to 5909 confidently quantified proteins. Transcriptomic profiling identified 2627 differentially expressed genes (DEGs) under acute low-salinity stress, comprising 1332 upregulated and 1295 downregulated genes compared to the control group. In contrast, a total of 733 DEGs were identified under prolonged low-salinity exposure, including 390 upregulated and 343 downregulated genes. Parallel proteomic analysis identified 199 differentially expressed proteins (DEPs) in the acute stress group, with 105 upregulated and 94 downregulated relative to the control group. Under prolonged stress, 206 DEPs were detected, including 124 upregulated and 82 downregulated proteins compared to the control group. Significant GO term and KEGG pathway enrichments contained metal ion binding, oxidoreductase activity, nucleus, apoptotic process, innate immune response, and amino acid metabolism, suggesting that megalopa employ coordinated regulatory mechanisms involving metabolic reprogramming, immunity system modulation, ion homeostasis maintenance and cell cycle regulation to adapt to hypoosmotic stress. Integrated multi-omics analysis identified 17 genes displaying significant concordant differential expression at both mRNA and protein levels during acute hypoosmotic stress, versus only 5 gene-protein pairs during prolonged stress exposure, indicating extensive post-transcriptional regulation and protein turnover mechanisms in sustained hypoosmotic condition. To the best of our knowledge, this study established the first integrative transcriptome-proteome framework elucidating hypoosmotic adaptation (8‰) mechanisms in S. paramamosain megalopa. The identified molecular signatures offer actionable targets for selective breeding of salinity-tolerant strains and precision management of megalopa culture under suboptimal salinity condition, while fundamentally advancing our mechanistic understanding of osmoregulatory plasticity across decapod crustaceans. Full article

Background/Objective: The rise of multidrug-resistant bacteria underscores the urgent need for alternative antimicrobial strategies. Metal-based compounds and bacteriophage (phage) therapy have emerged as promising candidates, but the evolutionary trade-offs associated with these selective pressures and their combination remain poorly understood. This study aimed to investigate how prior exposure to T4 phage influences Escherichia coli B’s subsequent adaptation to iron (III) stress and to assess the resulting phenotypic and genomic signatures of dual resistance. Method: In this study, we performed experimental evolution using Escherichia coli B to investigate adaptive responses under four conditions: control (LB broth), T4 phage-only, iron (III) sulfate-only, and sequential phage followed by iron (III) exposure. Each treatment consisted of ten independently evolved populations (biological replicates), all derived from a common ancestral strain and passaged daily for 35 days. Phage resistance evolved rapidly, with complete resistance observed within 24 h of exposure. Results: In contrast, iron-selected populations evolved tolerance to high iron concentrations (1000–1750 mg/L) over time at a cost to resistance in other metals (gallium and iron (II) and antibiotics (tetracycline). Notably, prior phage exposure altered these outcomes: phage/iron-selected populations retained phage resistance and iron tolerance but showed diminished resistance to iron (II) and distinct antibiotic sensitivity profiles. Whole-genome sequencing revealed stressor-specific adaptations: large deletions in phage receptor-related genes (waaA and waaG) under phage pressure, and selective sweeps in iron-adapted populations affecting regulatory and membrane-associated genes (qseB, basR, aroK, fieF, rseB, and cpxP). Conclusions: These results demonstrate that the sequence of environmental stressors significantly shapes phenotypic and genetic resistance outcomes. Our findings highlight the importance of fitness epistasis and historical contingency in microbial adaptation, with implications for the design of evolution-informed combination therapies. Full article