GWAS analysis using Cluster 2 data (shown in S1 Fig). by Ke Li (106849)

GWAS analysis using only Cluster 1 data (shown in S1 Fig). by Ke Li (106849)

GWAS analysis using Cluster 3 data (shown in S1 Fig). by Ke Li (106849)

GWAS analysis using Cluster 4 data (shown in S1 Fig). by Ke Li (106849)

Clustering analysis of total and active RNAP II at genes during the transition from G1 to G2. by Douglas Maya-Miles (7024967)

Sequential viral infection modulates the intestinal bacterial microbiome. by Yuhao Li (1559569)

Mice in the NTC and MBC-005 treatment groups were evaluated for bioluminescent signaling at 6-, 13-, and 21-days after tumor inoculation. by Bryan S. Margulies (21492667)

“…<p>(A) Malthusian non-linear regression analysis demonstrated that the whole-body bioluminescent signal was significantly different between the different treatment groups (p < 0.0001). …”

A Locally Linear Dynamic Strategy for Manifold Learning. by Weifan Wang (4669081)

“…For 10-30% noise, where the Hebbian network employs a local linear transform, learning selectively increases signal direction alignment (blue) while simultaneously decreasing noise direction alignment (orange). …”

Study design. by Moazzam Tanveer (20775814)

“…<div><p>Background</p><p>Childhood obesity poses a significant public health challenge, yet effective school-based physical activity (PA) interventions remain scarce, especially in Pakistan. …”

Multiple bacterial subpopulations are present within splenic microcolonies. by Justin Greene (306625)

Droplet-based microfluidics can be used to model clustered bacterial growth. by Justin Greene (306625)

T3SS induction reduces gentamicin susceptibility, but S10 expression predicts survival. by Justin Greene (306625)

S10 and T3SS reporter signals do not correlate in the absence of MgOx treatment. by Justin Greene (306625)

T3SS induction slows growth, but cells can support high levels of S10 expression. by Justin Greene (306625)

Data Sheet 1_Unlocking the adaptation mechanisms of the oleaginous microalga Scenedesmus sp. BHU1 under elevated salt stress: a physiochemical, lipidomics and transcriptomics appro... by Rahul Prasad Singh (20283954)

“…<p>Microalgae are vital for their photosynthetic abilities, contributing significantly to global oxygen production, serving as a key trophic level in aquatic ecosystems, aiding in biofuel production, assisting in wastewater treatment, and facilitating the synthesis of valuable biochemicals. …”

The performance of eigenvectors of the covariance matrix in a model with more complex population structure. by Joshua G. Schraiber (8027060)

Results of longitudinal analysis of mental health in HCWs. by A. H. Ayesha Lavell (16734002)

Mean scores and 95% CI of mental health symptoms, COVID-19 admissions and effect size during follow-up. by A. H. Ayesha Lavell (16734002)

Benchmark regression results. by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”

Heterogeneity test. by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”