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The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expression, 282 lipid species quantification, and 284 clinical, lifestyle, and dietary variables. This concept paper introduces the depth of the data resource, and investigates the extent of ethnic variation at these omics and non-omics biomarkers. It is evident that there are specific biomarkers in each of these platforms to differentiate between the ethnicities, and intra-population analyses suggest that Chinese and Indians are the most biologically homogeneous and heterogeneous, respectively, of the three groups. Consistent patterns of correlations between lipid species also suggest the possibility of lipid tagging to simplify future lipidomics assays. The Singapore Integrative Omics Study is expected to allow the characterization of intra-omic and inter-omic correlations within and across all three ethnic groups through a systems biology approach.The Singapore Genome Variation projects characterized the genetics of Singapore's Chinese, Malay, and Indian populations. The Singapore Integrative Omics Study introduced here goes further in providing multi-omic measurements in individuals from these populations, including genetic, transcriptome, lipidome, and lifestyle data, and will facilitate the study of common diseases in Asian communities.
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http://dx.doi.org/10.1038/s41467-017-00413-x | DOI Listing |
Nanoscale
September 2025
Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore.
A crack-free and residue-free transfer technique for large-area, atomically-thin 2D transition metal dichalcogenides (TMDCs) such as MoS and WS is critical for their integration into next-generation electronic devices, either as channel materials replacing silicon or as back-end-of-line (BEOL) components in 3D-integrated nano-systems on CMOS platforms. However, cracks are frequently observed during the debonding of TMDCs from their growth substrates, and polymer or metal residues are often left behind after the removal of adhesive support layers wet etching. These issues stem from excessive angular strain accumulated during debonding and the incomplete removal of support layers due to their low solubility.
View Article and Find Full Text PDFGlob Ment Health (Camb)
July 2025
School of Social Sciences, College of Humanities, Arts, and Social Sciences, Nanyang Technological University, Singapore.
The global prevalence of mental health disorders among youths aged 15 to 24 is a significant public health concern. This systematic review aimed to explore global strategies for promoting mental well-being and addressing mental health challenges within this demographic, as defined by the World Health Organization. A comprehensive search of electronic scientific databases was conducted on November 1, 2023, yielding 43 studies with a total of 29,581 participants published between 2008 and 2023 that examined mental health interventions targeting youth.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
September 2025
State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Electrochemical Process and Technology of Materials, Beijing University of Chemical Technology, Beijing, 10029, P.R. China.
Lithium metal batteries (LMBs) have emerged as the most promising candidate for next-generation high-energy-density energy storage systems. However, their practical implementation is hindered by the inability of conventional carbonate electrolytes to simultaneously stabilize the lithium metal anode and LiNiCoMnO (NCM811) cathode interfaces, particularly under extreme operating conditions. Herein, we present a transformative molecular design using 3,5-difluorophenylboronic acid neopentyl glycol ester (DNE), which uniquely integrates dual interfacial stabilization mechanisms in a single molecule.
View Article and Find Full Text PDFNat Commun
September 2025
Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, 90033, California, USA.
Brief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
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