Publications by authors named "T Y N Tong"

Article Synopsis
  • Age is the biggest risk factor for late-onset Alzheimer's Disease (LOAD), with over 80 genetic loci linked to it, but only the APOE region's age dependencies are well-studied.
  • A study conducted on diverse populations (34,833 non-Hispanic Whites, 7,264 African Americans, 3,232 East Asians, and 2,024 Caribbean Hispanics) examined the interaction between SNPs and age to identify significant genetic associations with LOAD.
  • The analysis revealed 6 genome-wide significant loci, with 4 showing preliminary evidence of age-dependent associations with LOAD, including CD2AP, PICALM, APOE, and LILRA5.
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Background: Some subjects exhibit AD pathology but remain cognitively intact. This resilience has been associated with cell-type abundance changes, particularly in neurons. We investigated the molecular basis of cognitive resilience by deconvoluting bulk RNA sequencing (RNA-seq) data into multiple brain cell types derived from three brain regions.

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Photosynthesis harvests solar energy to convert CO into chemicals, offering a potential solution to reduce atmospheric CO. However, integrating photosynthesis into non-photosynthetic microbes to utilize one-carbon substrates is challenging. Here, a photosynthesis system is reconstructed in E.

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Mitochondrial dysfunction is a key factor in exacerbating pressure overload-induced cardiac hypertrophy and is linked to increased morbidity and mortality. ECSIT, a crucial adaptor for inflammation and mitochondrial function, has been reported to express multiple transcripts in various species and tissues, leading to distinct protein isoforms with diverse subcellular localizations and functions. However, whether an unknown ECSIT isoform exists in cardiac cells and its potential role in regulating mitochondrial function and pathological cardiac hypertrophy has remained unclear.

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Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education.

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