Fig 1 - Fusing multisensory signals across channels and time by Swathi Anil (17382903)

The clinical characteristic of patients. by Hai-bo Ai (20790405)

The results of threshold effect analysis. by Hai-bo Ai (20790405)

The forest plot of subgroup analysis. by Hai-bo Ai (20790405)

Supplementary Tables 1–8. by Juliane F. Tampé (19976538)

Cross-sectional dependence results. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Autocorrelation test results. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Pesaran’s CADF test results for Model I. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Descriptive statistics of related variables. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Age-related correlation of PBMCs percentages. by Juliane F. Tampé (19976538)

Age-dependent expression of <i>ANXA1</i> gene in monocytes and their subtypes. by Juliane F. Tampé (19976538)

Age-related correlation of PBMCs percentages in comparison between males and females. by Juliane F. Tampé (19976538)

“…<p>Only cells/groups with significant p-values are shown. Additional data are presented in S1 Fig 5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0300946#pone.0300946.s001" target="_blank">S1 File</a>. …”

The results of curve fitting analysis. by Hai-bo Ai (20790405)

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

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). …”

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

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. …”

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. …”

S1 File - 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. …”