C. Zhang

The past two decades has witnessed a remarkable increase in the number of microbial genomes retrieved from marine systems1,2. However, it has remained challenging to translate this marine genomic diversity into biotechnological and biomedical applications3,4. Here we recovered 43,191 bacterial and archaeal genomes from publicly available marine metagenomes, encompassing a wide range of diversity with 138 distinct phyla, redefining the upper limit of marine bacterial genome size and revealing complex trade-offs between the occurrence of CRISPR–Cas systems and antibiotic resistance genes. In silico bioprospecting of these marine genomes led to the discovery of a novel CRISPR–Cas9 system, ten antimicrobial peptides, and three enzymes that degrade polyethylene terephthalate. In vitro experiments confirmed their effectiveness and efficacy. This work provides evidence that global-scale sequencing initiatives advance our understanding of how microbial diversity has evolved in the oceans and is maintained, and demonstrates how such initiatives can be sustainably exploited to advance biotechnology and biomedicine. Analysis of 43,191 genomes obtained from publicly available marine bacterial and archaeal metagenome data provides insights into marine bacterial evolution, CRISPR–Cas defence and antibiotic resistance genes, and demonstrates the potential of marine metagenomes for biotechnological applications.

With the development of railway transportation and the advancement of deep learning, object detection algorithms are increasingly replacing manual inspection of track fasteners. However, current algorithms struggle with low accuracy in complex weather conditions or low-contrast backgrounds. To address this, we propose a track fastener defect detection algorithm based on YOLOv11 (You Only Look Once).First, we incorporate the DHSA (Dynamic-range Histogram Self-Attention) module into the backbone network of YOLOv11 to enhance noise robustness. Second, we introduce the BRA (Bi-Level Routing Attention) sparse attention mechanism into the neck network for improved efficiency. Finally, we add the PPA (Parallelized Patch-Aware Attention) module to the original neck network to enhance multi-scale feature extraction, specifically for small object detection.To validate the model, we created a dataset and conducted experiments. The experimental results show that YOLO-DRPA achieves a mAP@0.5 of 94.6% and a mAP@0.5:0.95 of 80.7%, marking improvements of 1.8% and 4.0% over YOLOv11n, respectively. The model also demonstrates competitive performance compared to other popular object detection algorithms, highlighting its potential to improve both detection accuracy and efficiency.

Background: Primary aldosteronism (PA) is a common cause of secondary hypertension. Despite considerable advances in medical sciences, the underlying molecular mechanisms of PA remain inadequately investigated. Therefore, this study aims to identify potential therapeutic targets for PA by using transcriptome sequencing, providing promising insights for early diagnosis and precise management of this condition. Methods: Transcriptomic sequencing was performed on blood samples from PA patients and healthy controls. DESeq2 identified differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) of DEGs identified core module genes, which underwent functional enrichment for significant pathways and target detection. Subsequently, aldosterone-producing adenoma (APA) rat models were established. Potential targets (e.g., tuberous sclerosis complex 2 (<i>TSC2</i>)) were modulated via overexpression or shRNA interference, with effects validated physiologically, cellularly, and molecularly. Results: Our analysis identified 277 DEGs and 8 functional modules. Eigengenes in the pink module exhibited the strongest association with disease clinical phenotypes. The genes within this module were significantly enriched in the mammalian target of rapamycin (mTOR) signaling pathway, with <i>TSC2</i> identified as a central hub gene, indicating its potential as a promising therapeutic target for PA. In APA rats, TSC2 overexpression significantly inhibited plasma aldosterone (ALD) increase (<i>p</i> < 0.0001) and elevated plasma renin activity (PRA) (<i>p</i> < 0.001). Furthermore, it inhibited adrenal cell proliferation (<i>p</i> < 0.0001), reduced S-phase fraction (<i>p</i> < 0.0001), decreased Ki67 (<i>p</i> < 0.0001), and increased p21 expression (<i>p</i> < 0.01). Western blot analysis revealed that TSC2 overexpression reduced the adrenal p-mTOR/mTOR ratio (<i>p</i> < 0.0001), while increasing p-eukaryotic translation initiation factor 4E-binding protein (p-4EBP)/4EBP (<i>p</i> < 0.001) in APA rats. Conversely, sh-TSC2 knockdown produced opposing patterns. Conclusions: Transcriptome analysis identified <i>TSC2</i> as a promising therapeutic target for PA, which was later confirmed in rat models. <i>TSC2</i> likely alleviates PA by inhibiting mTOR pathway activation, thereby reducing abnormal adrenal cell proliferation and aldosterone secretion.