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Post-transcriptional modifications of RNA, such as RNA methylation, can epigenetically regulate behavior, for instance learning and memory. However, it is unclear whether RNA methylation plays a critical role in the pathophysiology of major depression disorder (MDD). Here, we report that expression of the fat mass and obesity associated gene (FTO), an RNA demethylase, is downregulated in the hippocampus of patients with MDD and mouse models of depression. Suppressing Fto expression in the mouse hippocampus results in depression-like behaviors in adult mice, whereas overexpression of FTO expression leads to rescue of the depression-like phenotype. Epitranscriptomic profiling of N6-methyladenosine (m 6 A) RNA methylation in the hippocampus of Fto knockdown (KD), Fto knockout (cKO), and FTO-overexpressing (OE) mice allows us to identify adrenoceptor beta 2 ( Adrb2 ) mRNA as a target of FTO. ADRB2 stimulation rescues the depression-like behaviors in mice and spine loss induced by hippocampal Fto deficiency, possibly via the modulation of hippocampal SIRT1 expression by c-MYC. Our findings suggest that FTO is a regulator of a mechanism underlying depression-like behavior in mice. Post-transcriptional modification of RNA can contribute to regulating behavior. Here, the authors show that modulating the expression of Fto results in epitranscriptomic changes in the mouse hippocampus associated with depression-like behavior.

Background With the intensification of global warming, rainbow trout ( Oncorhynchus mykiss ) suffer from varying degrees of thermal stimulation, leads to mass mortality, which severely restrict the development of aquaculture. Understanding the molecular regulatory mechanisms of rainbow trout under heat stress is useful to develop approaches to relieve symptoms. Results Changes in nonspecific immune parameters revealed that a strong stress response was caused in rainbow trout at 24 °C, so we performed multiple transcriptomic analyses of rainbow trout liver under heat stress (HS, 24 °C) and control conditions (CG, 18 °C). A total of 324 DEcircRNAs, 105 DEmiRNAs, and 1885 DEmRNAs were identified. A ceRNA regulatory network was constructed and a total of 301 circRNA-miRNA and 51 miRNA-mRNA negative correlation pairs were screened, and three regulatory correlation pairs were predicted: novel_circ_003889 - novel-m0674-3p - hsp90ab1 , novel_circ_002325 - miR-18-y - HSPA13 and novel_circ_002446 - novel-m0556-3p - hsp70 . Some target genes involved in metabolic processes, biological regulation or response to stimulus were highly induced at high temperatures. Several important pathways involved in heat stress were characterized, such as protein processing in the ER, the estrogen signaling pathway, and the HIF-1 signaling pathway. Conclusions These results extend our understanding of the molecular mechanisms of the heat stress response and provide novel insight for the development of strategies that relieve heat stress.

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively—improvements of 3.4%, 4.4%, and 6% over the original model—while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms.

Bisphenol A (BPA) can interact with nuclear receptors and affect the normal function of nuclear receptors in very low doses, which causes BPA to be one of the most controversial endocrine disruptors. However, the detailed molecular mechanism about how BPA interferes the normal function of nuclear receptors is still undiscovered. Herein, molecular dynamics simulations were performed to explore the detailed interaction mechanism between BPA with three typical nuclear receptors, including hERα, hERRγ and hPPARγ. The simulation results and calculated binding free energies indicate that BPA can bind to these three nuclear receptors. The binding affinities of BPA were slightly lower than that of E2 to these three receptors. The simulation results proved that the binding process was mainly driven by direct hydrogen bond and hydrophobic interactions. In addition, structural analysis suggested that BPA could interact with these nuclear receptors by mimicking the action of natural hormone and keeping the nuclear receptors in active conformations. The present work provided the structural evidence to recognize BPA as an endocrine disruptor and would be important guidance for seeking safer substitutions of BPA.

The seed industry stands as a vital strategic sector critical to a nation’s agricultural security and food sovereignty. Researching the optimization of resource allocation within seed enterprises is pivotal for advancing national food security and agricultural modernization. Utilizing micro-survey data from China’s corn seed enterprises spanning from 2018 to 2022, this paper empirically analyzes the impact and mechanisms of capital resource mismatch and labor resource mismatch on the competitiveness of corn seed enterprises. The findings reveal that: (1) Capital resource mismatch hinders the enhancement of competitiveness among corn seed enterprises, whereas labor resource mismatch boosts their competitiveness. (2) The phenomenon of “financial ownership discrimination” persists, with capital resource mismatch impeding the competitiveness of non-state-owned corn seed enterprises; concurrently, the “incentive” effect of labor resource mismatch is more pronounced among state-owned corn seed enterprises. (3) In comparison to technology-oriented corn seed enterprises, capital resource mismatch has a significant positive influence on the competitiveness of basic corn seed enterprises, whereas labor resource mismatch has a notable negative impact. (4) The dual technical barriers posed by executives and employees alleviate the inhibitory effect of capital resources on the competitiveness of corn seed enterprises but simultaneously impede the promotional effect of labor resource mismatch. (5) In line with the mismatch effects observed at the micro-enterprise level, labor resource mismatch in the macro factor market also demonstrates a significant positive effect on the competitiveness of corn seed enterprises. Our research findings offer a specific business context for enterprise resource allocation strategies, facilitating the efficient operation of the seed industry system and promoting the healthy development of the seed industry.

Pediatric inflammatory bowel disease (IBD), especially Crohn's disease, significantly affects gut health and quality of life. Although gut microbiome research has advanced, identifying reliable biomarkers remains difficult due to microbial complexity. We used RNA-seq-based microbial profiling and machine learning (ML) to find robust biomarkers in pediatric IBD. Microbial taxa were profiled at phylum, genus, and species levels using kraken2 on Crohn's disease and non-IBD ileal biopsies. We performed abundance-based analyses and applied four ML models (Logistic Regression, Random Forest, Support Vector Machine, XGBoost) to detect discriminative taxa. An independent cohort of 36 pediatric stool samples assessed by 16S rRNA sequencing validated top ML results. Traditional abundance-based methods showed compositional shifts but identified few consistently significant taxa. ML models had better discriminatory performance, with XGBoost outperforming others and pinpointing Orthotospovirus and Vescimonas as key genera. These findings were confirmed in the validation cohort, where only one traditionally noted genus, , maintained significance. Integrating conventional omics with AI-driven analytics boosts reproducibility and clinical relevance of microbial biomarker discovery, opening new possibilities for targeted therapies and precision medicine in pediatric IBD.

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

Background Intramuscular fat (IMF) content has become one of the most important indicators for measuring meat quality, and levels of IMF are affected by various genes. Long non-coding RNAs (lncRNAs) are widely expressed non-coding RNAs that play an important regulatory role in a variety of biological processes; however, research on the lncRNAs involved in sheep IMF deposition is still in its infancy. Aohan fine-wool sheep (AFWS), one of China’s most important meat-hair, dual-purpose sheep breed, provides a great model for studying the role of lncRNAs in the regulation of IMF deposition. We identified lncRNAs by RNA sequencing in Longissimus thoracis et lumborum (LTL) samples of sheep at two ages: 2 months (Mth-2) and 12 months (Mth-12). Results We identified a total of 26,247 genes and 6935 novel lncRNAs in LTL samples of sheep. Among these, 199 mRNAs and 61 lncRNAs were differentially expressed. We then compared the structural characteristics of lncRNAs and mRNAs. We obtained target genes of differentially expressed lncRNAs (DELs) and performed enrichment analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). We found that target mRNAs were enriched in metabolic processes and developmental pathways. One pathway was significantly enriched, namely tight junction. Based on the analysis of critical target genes, we obtained seven candidate lncRNAs that potentially regulated lipid deposition and constructed a lncRNA-mRNA co-expression network that included MSTRG.4051.3- FZD4 , MSTRG.16157.3- ULK1, MSTRG.21053.3- PAQR3 , MSTRG.19941.2- TPI1, MSTRG.12864.1- FHL1 , MSTRG.2469.2 -EXOC6 and MSTRG.21381.1- NCOA1 . We speculated that these candidate lncRNAs might play a role by regulating the expression of target genes. We randomly selected five mRNAs and five lncRNAs to verify the accuracy of the sequencing data by qRT-PCR. Conclusions Our study identified the differentially expressed mRNAs and lncRNAs during intramuscular lipid deposition in Aohan fine-wool sheep. The work may widen the knowledge about the annotation of the sheep genome and provide a working basis for investigating intramuscular fat deposition in sheep.

Traffic sign detection is critically important for intelligent transportation systems, yet persistent challenges like multi-scale variation and complex background interference severely degrade detection accuracy and real-time performance. To address these limitations, this study presents CPB-YOLOv8, an advanced multi-scale detection framework based on the YOLOv8 architecture. A Cross-Stage Partial-Partitioned Transformer Block (CSP-PTB) is incorporated into the feature extraction stage to preserve semantic information during downsampling while enhancing global feature representation. For feature fusion, a four-level bidirectional feature pyramid BiFPN integrated with a P2 detection layer significantly improves small-target detection capability. Further enhancement is achieved via an optimized loss function that balances multi-scale objective localization. Comprehensive evaluations were conducted on the TT100K, the CCTSDB, and a custom multi-scenario road image dataset capturing urban and suburban environments at 1920 × 1080 resolution. Results demonstrate compelling performance: On TT100K, CPB-YOLOv8 achieved 90.73% mAP@0.5 with a 12.5 MB model size, exceeding the YOLOv8s baseline by 3.94 percentage points and achieving 6.43% higher small-target recall. On CCTSDB, it attained a near-saturation performance of 99.21% mAP@0.5. Crucially, the model demonstrated exceptional robustness across diverse environmental conditions. Rigorous analysis on partitioned CCTSDB subsets based on weather and illumination, alongside validation using a separate self-collected dataset reserved solely for inference, confirmed strong adaptability to real-world distribution shifts and low-visibility scenarios. Cross-dataset validation and visual comparisons further substantiated the model’s robustness and its effective suppression of background interference.

Chronic kidney disease (CKD) is a major health issue, with podocyte injury with senescence playing a central role in glomerulosclerosis. This study investigates the link between glycolysis-derived serine metabolism and podocyte injury with senescence, focusing on the role of phosphoglycerate kinase 1 (PGK1) in the regulation of L-serine synthesis and podocyte homeostasis. Using in vivo and in vitro models, we examined the effects of angiotensin II (Ang II)-induced metabolic dysregulation on serine metabolism and its impact on podocyte function. The results demonstrate that Ang II downregulates PGK1 expression through the transcription factor FOXA1, leading to reduced L-serine biosynthesis, mitochondrial dysfunction, and increased cellular senescence in podocytes. Supplementing with L-serine or enhancing PGK1 expression in podocytes alleviated these pathological changes, restored mitochondrial function, and reduced senescence-associated phenotypes in CKD mouse models. Moreover, PGK1 was found to interact with keratin, type II cytoskeletal 1 (KRT1), stabilizing the cytoskeletal integrity of podocytes. These findings identify a novel metabolic pathway linking glycolysis, serine metabolism, and podocyte injury with senescence, suggesting that targeting the PGK1-serine axis may offer therapeutic potential for slowing podocyte senescence and CKD progression. Previous studies showed inhibition of glycolytic enzyme pyruvate kinase M2 disrupts podocyte function. The study shows that PGK1-serine axis regulates and drives aging and damage of podocytes.