Life Sciences

The authors propose a Delaunay-weighted two-sample test: under a low-dimensional manifold assumption they define a Delaunay weight from the Delaunay triangulation that captures both geodesic distance and relative direction, use the average within-group Delaunay weight as the test statistic with a permutation p-value, prove asymptotic normality under the null and consistency under the alternative, and show in simulations substantially higher power than k-NN, k-MST, kernel, e-distance, covariance, and regression tests when the two distributions differ in the principal directions of their covariance matrices, while detecting a treatment-group difference with p=0.011 in a mice protein expression dataset.

Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.

Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.

This study maps the in-context learning operating regime of Evo 2, a nucleotide-level foundation genomic language model, across five binary classification tasks spanning biological and artificial sequences, finding robust performance on shorter natural sequences (F1=0.902 for miRNA, 0.785 for Toxins), degradation with sequence length and collapse at kilobase scale, no benefit from model scaling (the 7B model systematically outperforms the 40B variant), poor prediction of accuracy by perplexity, and mechanistic interpretability via logit-lens and Jacobian Scope suggesting a prediction-generalisation trade-off and that models might track prompt structure rather than signal-carrying content.

In KRAS-mutant and KRAS wild-type pancreatic ductal adenocarcinoma cells (PANC-1, BxPC-3) and a PANC-1 subcutaneous xenograft model, this study shows that the natural polyphenol ellagic acid (EA) combined with the GPX4 inhibitor RSL3 synergistically reduces cell viability and suppresses tumor growth by activating p38 MAPK and upregulating Keap1 to inhibit the Nrf2/HO-1 antioxidant axis, amplifying ferroptotic hallmarks such as iron accumulation, lipid peroxidation, and GPX4 downregulation, with Fer-1 rescuing viability while apoptosis, necroptosis, and autophagy inhibitors do not.

Combining two multimodal qMRI-PET datasets (n = 74, spanning Alzheimer's disease, subjective cognitive decline, and healthy controls), this study used [18F]UCB-H PET distribution volume VT from Logan graphical analysis as the synaptic-density reference, applied ComBat harmonization, and compared classical machine learning (SVR, PLS, Elastic Net, Random Forests) with deep learning (U-Net, ResUNet++, Pix2Pix-like conditional GANs) for predicting PET-like synaptic density images from qMRI maps such as R1, R2*, MTsat, and PD; Elastic Net was best among classical models (R² = 0.50, RMSE = 0.448, MAE = 0.331), deep learning improved accuracy with 3D U-Net most consistent, and gray-matter z-scored evaluation showed strong agreement with reference PET (MSE 0.1294 ± 0.0778, SSIM 0.9832 ± 0.