4656 papers
This study reveals that moderate activation of ghrelin receptors in dorsomedial striosomes regulates conflict-based decision-making by enhancing cost sensitivity through a specific neural circuit involving the lateral habenula and substantia nigra pars compacta.
This paper introduces a transparent, reproducible computational model called the Normalized Head-Impact Dose (HID) that integrates impact frequency, rotational acceleration, and cumulative vulnerability to estimate and compare lifetime brain-injury burdens across six contact sports, revealing that combat sports carry the highest risk while providing a forward-looking framework for assessing neurodegenerative exposure with quantified uncertainty.
This paper introduces the Optic Disc Colour Ratio (ODCR) as a label-free proxy for fundus pigmentation to reveal that diabetic retinopathy screening models systematically assign higher referral probabilities to dark-pigmented fundi due to reduced vessel-background contrast, a disparity driven by sensitivity-calibrated decision rules that remains invisible to standard AUC metrics but is critical for fairness auditing.
This study demonstrates that regenerating forests at La Selva, Costa Rica, can support high densities of mantled howler monkeys, Central American spider monkeys, and Panamanian white-throated capuchins while maintaining their coexistence through distinct activity budgets and vertical space use despite overlapping habitat preferences.
Computational simulations reveal that while molecular crowding minimally affects the average water transport rate through aquaporins, it significantly increases transport variability and membrane instability, suggesting that variance serves as a more sensitive indicator of crowding-induced dynamics than mean rates.
ExposOmix-Fed is a federated, site-invariant protocol that integrates environmental exposome, multi-omics, and abdominal MRI data for colorectal cancer risk stratification within a privacy-preserving framework, demonstrating via in-silico validation on calibrated synthetic data that it effectively recovers injected cross-modal signals and selection-bias-corrected hazard ratios while enabling auditable exposure–molecule interaction maps.
This study utilizes an integrated multi-omics analysis of TCGA-LUAD data combined with machine learning and network analysis to identify and validate five prognostic biomarkers (BIRC3, PSMB1, PSMA4, PSMC4, and TNFRSF12A) that are significantly associated with poor overall survival in lung adenocarcinoma, despite showing limited diagnostic utility.
This study demonstrates that neglecting arterial wall compliance in cardiovascular simulations introduces frequency-dependent errors that progressively increase with heart rate, as rigid-wall models fail to capture the compliance-driven storage and damping mechanisms essential for accurate hemodynamic predictions.
This paper, which originally proposed that exogenous HMGB1 promotes pancreatic cancer progression by facilitating crosstalk between tumor cells and myeloid-derived suppressor cells, has been withdrawn by the authors for corrections and should not be cited.
This paper introduces a scalable geometric framework that utilizes the Off–log metric for correlation matrices and Grassmannian subspace discrimination for eigenvector analysis to enhance the sensitivity and predictive performance of fMRI-based brain network modeling while maintaining compatibility with standard machine learning workflows.