Changes in root morphology indexes and root dry weight of different treatments in September 2023 (Second planting). by Hongyi Pan (5706971)

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Relationship between soil three phases and ideal points in different treatments before and after the experiment. by Hongyi Pan (5706971)

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Changes in average BD, PD, and TP of rhizosphere soil (0–60 cm) under different treatments before and after the experiment. by Hongyi Pan (5706971)

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5-HT inhibits <i>P</i>. <i>berghei</i> infection in mosquitoes. by Li Gao (131516)

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TBSS results showing significant AxD and RD changes at baseline and the follow-up. by Sewon Lim (21989785)

“…The decreased diffusivity metrics (AxD and RD) exhibited no statistically significant differences at <i><i>P</i></i> < 0.05 after correcting for multiple comparisons (FWE). …”

Contains R code utilized in the analyses. by Arttu Autio-Kimura (20454681)

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Sex-based differences for combinations of brain regions and cell types, based on Mann-Whitney U-test. by Arttu Autio-Kimura (20454681)

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Bootstrapped spearman correlations from a randomly selected subset of male sample donor originating samples (n = 493). by Arttu Autio-Kimura (20454681)

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Spearman correlations between the age of the sample donor (20–70 years) and estimated proportions of neuronal, glial, and endothelial cells. by Arttu Autio-Kimura (20454681)

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Box plots for subsets of female originating samples, displaying the distributions of the proportions of neuronal, glial, and endothelial cells. by Arttu Autio-Kimura (20454681)

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Box plots for subsets of male originating samples, displaying the distributions of the proportions of neuronal, glial, and endothelial cells. by Arttu Autio-Kimura (20454681)

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Box plot displaying the distributions of the deconvolution derived proportions of cell types. by Arttu Autio-Kimura (20454681)

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Spearman correlation of age with proportions of cell types in men, for each brain region. by Arttu Autio-Kimura (20454681)

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Principal Component Analysis (PCA) to illustrate the distribution of the significant lipid metabolites among the groups (NL, SS and MASH). by Laia Bertran (3685147)

Comparative analysis of macronutrient compositions between baseline and end of intervention (*p < 0.05, ^ns Non-significant results). by Duc Tran Quang (20564967)

Comparative analysis of caloric intake between baseline and end of intervention (*p < 0.05, ^ns Non-significant results). by Duc Tran Quang (20564967)

Results of RF regression model validation and significance of influencing factors (desert area change and spatiotemporal characteristics of ERI). by Mingyue Sun (4638535)

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. …”