Basic Science and Pathogenesis.

Background: Age is the largest risk factor for late-onset Alzheimer's Disease (LOAD). Although >80 genetic loci have been associated with LOAD, little is known about the age dependencies of these associations except the APOE region.

Method: We performed cross-ancestry and ancestry-specific genome-wide gene-age interaction and age-stratified association study using TOPMed-imputed genome-wide association study (GWAS) data from Alzheimer's Disease Genetics Consortium (ADGC) including 34,833 non-Hispanic Whites (NHW), 7,264 African Americans (AA), 3,232 East Asians (EA), and 2,024 Caribbean Hispanics (CH) aged 60 years and older.

Basic Science and Pathogenesis.

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.

A new-to-nature photosynthesis system enhances utilization of one-carbon substrates in Escherichia coli.

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.

ECSIT-X4 is Required for Preventing Pressure Overload-Induced Cardiac Hypertrophy via Regulating Mitochondrial STAT3.

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.

Predicting learning achievement using ensemble learning with result explanation.

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.