Singapore Science News API

{
    "totalArticles": 17016,
    "articles": [
        {
            "id": "70a8242b9225a2a3eb3df91974b5ffa2",
            "title": "Scientists Finally Discover Why Gold Never Rusts",
            "description": "Researchers discovered that gold’s unusual resistance to rust comes from protective atomic surface patterns that block oxygen.",
            "content": "A new study explains why the “noble metal” resists oxidation and how surface modifications could unleash its catalytic power.\nGold has long been prized for its stability, but in the world of chemistry, that stability of gold is precisely what has cau... [2024 chars]",
            "url": "https://www.techexplorist.com/gold-never-rusts/103076/",
            "image": "https://www.techexplorist.com/wp-content/uploads/2026/05/gold.webp",
            "publishedAt": "2026-05-23T16:43:46Z",
            "lang": "en",
            "source": {
                "id": "384f05ddf6ed854b082cdea0d2279b35",
                "name": "Tech Explorist",
                "url": "https://www.techexplorist.com"
            }
        },
        {
            "id": "f589d35ecb70efa3192f38e68e923c79",
            "title": "Reconstitution of protein arginylation pathways in bacteria for robust identification and quantification",
            "description": "ATE1 is a conserved enzyme that catalyzes the covalent addition of arginine to proteins bearing N-terminal or mid-chain Asp and Glu residues. N-terminal (Nt) arginylation can also occur on Cys, Asn, and Gln following enzymatic conversion, often marking proteins for degradation. Essential for development, this pathway contributes to protein quality control and stress responses. Despite growing insight into ATE1 structure and function, the mechanisms governing its substrate selectivity and coordination with upstream oxygenase and deamidase remain poorly defined. Here, we reconstitute the human processing cascades that generate Nt-arginylated proteins in E. coli, enabling step-resolved analysis of arginylation outcomes in a cellular context. By co-expressing human ADO, NTAN1, or NTAQ1 with ATE1 in a modular system, we achieved efficient conversion of Nt-Cys, Asn, and Gln into arginylation-permissive forms, recapitulating key features of upstream processing. Using this platform, we demonstrated that N-terminal processing is efficient and that ATE1 preferentially modifies protein N-termini over internal acidic residues. Mid-chain arginylation of α-synuclein was detectable but occurred at low frequency, with no major differences in site selectivity observed across the ATE1 isoforms tested. Together, this bacterial reconstitution system provides a scalable experimental platform for quantitative, protein-level analysis of ATE1 substrate specificity under defined conditions. A bacterial reconstitution system for human arginylation pathways enables quantitative, step-resolved analysis of ATE1 activity, revealing efficient N-terminal arginylation and low-frequency mid-chain modification.",
            "content": "This work was supported by grants from NIH R35 GM150678 to Y.Z., NIH R21 CA292191 to Z.L., NIH R01 HL177113 to Z.L. and B.A.G., Research Education Component (REC) through an NIA grant P30AG066444 to Z.L., Case Comprehensive Cancer Center (P30CA043703... [1262 chars]",
            "url": "https://www.nature.com/articles/s42003-026-10275-z?error=cookies_not_supported&code=4c96dd69-9744-4593-8c0d-1920cff0f70a",
            "image": "https://www.nature.com/static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-05-23T09:08:21Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        },
        {
            "id": "2227c2653182ac314a99699143df9701",
            "title": "Knowledge distillation and pseudo-labeling for lightweight YOLOv11-based structural crack detection",
            "description": "Structural cracks threaten the safety and long-term durability of civil infrastructure, yet manual inspection remains slow, subjective, and unreliable on complex surfaces. This study presents a training-centric strategy to improve a lightweight YOLOv11-N detector (2.6 M parameters) by transferring knowledge from a high-capacity YOLOv11-L teacher, without modifying the student architecture. Two semi-supervised mechanisms are investigated: pseudo-labeling and knowledge distillation. Using a crack dataset enlarged via systematic data augmentation, pseudo-labeling combines high-confidence teacher predictions with available ground-truth annotations through IoU and NMS filtering, while the distillation approach guides the student using both hard labels and teacher-derived soft signals to strengthen localization behavior. Experimental results show that both strategies enhance the baseline student model, with pseudo-labeling providing more stable training dynamics and stronger overall gains, whereas distillation primarily improves convergence behavior and sample efficiency. Ablation analyses highlight that the benefit of pseudo-labeling is data-dependent and requires a minimum pseudo-label density to achieve consistent improvements. Finally, edge-device (Raspberry 3B + and Nvidia Jetson Nano Kit) benchmarks validate that the resulting lightweight detector is suitable for deployment on resource-constrained platforms, enabling practical UAV- and mobile-oriented crack inspection across diverse edge computing tiers.",
            "content": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give... [797 chars]",
            "url": "https://www.nature.com/articles/s41598-026-52396-9?error=cookies_not_supported&code=57930aea-f285-412c-b50f-f097ea2ff85e",
            "image": "https://www.nature.com/static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-05-23T08:26:12Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        }
    ]
}