Browse Articles

Metapanax delavayi extract as a neurocutaneous modulator via CRHR1/POMC/MC1R signaling

Radar-based observation of a lava tube on Venus

Genetic variability in thrombopoietin receptor and GATA1 influences response to eltrombopag in dengue-induced thrombocytopenia

Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea

Rapid, efficient, and thermal degradation of chlorophenols using polymer-coated or metal-doped magnetic nanoparticles, with and without the application of AMF

High-throughput multi-organ proteomics workflow for drug efficacy and toxicity analysis

Genotype by environment interaction analysis for seed cotton yield stability under normal irrigation and drought stress conditions using numerical stability statistics

Abstract One of the study’s main goals is to find high-yielding and stable genotypes in cotton under normal irrigation conditions (NIC) and drought stress conditions (DSC), as well as the comparison between parametric and non-parametric stability statistics. In order to achieve this objective, 24 cotton genotypes were evaluated under NIC and DSC during the 2019 and 2020 growing seasons (four environments) at the Sakha Agriculture Research Station in the Kafr El-Sheikh Governorate of Egypt. Every trial was set up using a randomized complete block design with three replications. According to the ANOVA, years under NIC and genotypes under DSC had a highly significant impact on the seed cotton yield. The AMMI analysis showed significant effects of environments (P < 0.01), genotypes, and their interaction (GEI) (P < 0.05) on seed cotton yield in four environments (two years and two irrigation conditions). The AMMI model successfully divided the variability by GEI into three principal component axes (PCs), only PC1 explained 87% of the total variability of GEI. When compared to NIC, seed cotton productivity across DSC was found to be significantly reduced, ranging from 8.54% (G5 genotype) to 42.38% (G3 genotype). The stability statistics have the ability to separate, rank, and detect stable genotypes in both irrigation conditions. According to Y i , YS i and TOP stability measures, the genotypes G1, G5, G20 and G19 were determined as the most stable genotypes (dynamically) with the highest yielding performance, while, the genotypes G8 and G4 were the most stable (statically) with moderate seed cotton yield using most other stability statistics. Based on rank, Spearman’s rank correlation, PCA biplot and heatmap data, the stability parameters can be divided into two major groups that corresponded to different dynamic and static concepts of stability under NIC and DSC. The first group includes YS i and TOP , which are strongly correlated with Y i (dynamic). While, the second group has other stability statistics, which negatively correlated with Y i , thus related to the static concept. Generally, the genotypes G5 and G8 were stable coupled with high and moderate yield, respectively. Therefore, these genotypes are used in the cotton breeding programs for the development of improved cotton varieties to address drought conditions in Egypt.

Cellulose nanofibers and limestone filler enable high-performance, sustainable, and cost-efficient printable concrete

Single cell RNA transcriptome response to fentanyl use in persons with HIV infection

STING synergizes with TOX suppressing HO-1 expression to trigger ferroptosis in tumor-infiltrating CD8+ T cell and immunotherapy resistance

Land surface phenometrics and their responses to climatic variables in the semi-arid rangelands of the central Zagros mountains

Author Correction: ID1 expressing macrophages support cancer cell stemness and limit CD8+ T cell infiltration in colorectal cancer

Efficacy of artemether lumefantrine vs chloroquine for the treatment of Plasmodium Vivax infection in Pakistan

iMOE: prediction of second-life battery degradation trajectory using interpretable mixture of experts

Abstract Retired electric vehicle batteries offer immense potential to support energy infrastructure stability in underdeveloped regions through second-life use, but uncertainties in battery degradation behaviors pose major safety concerns. This work proposes an interpretable mixture of experts (iMOE) network that predicts battery degradation trajectories using partial, field-accessible signals in a single cycling operation. iMOE leverages an adaptive multi-degradation prediction module to classify battery degradation modes using expert weight synthesis learned from battery capacity-voltage and relaxation data. The module produces latent degradation trend embeddings, which are input to a use-dependent recurrent network for long-term degradation trajectory prediction. Validated on three typical use patterns (i.e. consistent operating histories, deeply aged batteries with unknown prior use, and uncertain second-life conditions, including 295 batteries, 93 use conditions, and 84,213 cycles), iMOE achieves an average mean absolute percentage errors (MAPE) of 0.95% with a 0.43 ms inference time for life-long battery degradation trajectory prediction. Compared to state-of-the-art Informer and PatchTST, it reduces computational time and MAPE by 50% and 77%, respectively. Compatible with data sampling in random state of charge regions, iMOE supports a 150-cycle time-horizon degradation trajectory prediction with 1.50% and 6.26% MAPE on average and at maximum, respectively. Notably, iMOE can operate effectively even with pruned 5MB training data while retaining 0.95% MAPE. Broadly, this network offers a deployable, history-free solution for battery degradation trajectory prediction at the time of second-life deployment, redefining how second-life energy storage systems are sensed, evaluated, controlled, and integrated for sustainable energy infrastructures at scale.

Comparative evaluation of a multi-functional dust suppressant synthesized from Sapindus mukorossi extract and bentonite clay

Historical depletion and future drought-driven risks to Gulf of Mexico fisheries production

Administration of N-acetylcysteine influence the expression of apoptotic genes in the granulosa cells of infertile women diagnosed with endometriosis

Phase engineering of relaxor ferroelectricity in van der Waals crystal

Artificial neural network modeling and optimization of an electrochemical biosensor for plasma miR-155-based breast cancer detection

Partitioned polygenic scores show mechanistic heterogeneity in type 2 diabetes and hypertension comorbidity

Abstract Type 2 diabetes and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analyse large-scale multiomic data to uncover genetic factors underlying type 2 diabetes and blood pressure comorbidity. We curate 1304 independent single-nucleotide variants associated with type 2 diabetes and blood pressure, grouping them into five clusters related to metabolic syndrome, inverse type 2 diabetes/blood pressure risk, impaired pancreatic beta-cell function, higher adiposity, and vascular dysfunction. Colocalization with tissue-specific gene expression highlights significant enrichment in pathways related to thyroid function and fetal development. Partitioned polygenic scores derived from these clusters improve risk prediction for type 2 diabetes/hypertension comorbidity, identifying individuals with more than twice the usual susceptibility. These results reveal a mechanistically heterogeneous genetic architecture shared between type 2 diabetes and blood pressure, enhancing comorbidity risk prediction. Partitioned polygenic risk scores offer a promising approach for early risk stratification, personalised prevention, and improved management of these interconnected conditions.