DataSheet_2_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

DataSheet_1_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

DataSheet_3_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

Violation behavior in vertical restraint: Empirical analyses in the case of retail price maintenance

Violation behavior in vertical restraint: Empirical analyses in the case of retail price maintenanc

Chen_et al_Supplmentary_meta_data

Data of tree growth, tree size, species, neighborhood crowding index, neighborhood dissimilarity, species abundance and species shade tolerance inde

ELECTRONIC SUPPLEMENTARY MATERIALS from Abundance-dependent effects of neighbourhood dissimilarity and growth rank reversal in a neotropical forest

SUPPLEMENTARY METHODS，JAGS CODE FOR FITTING THE BAYESIAN MODELS，SUPPLMENTARY TABLES AND FIGURE

Bioinformatics analysis of the potential mechanisms of Alzheimer’s disease induced by exposure to combined triazine herbicides

The development of Alzheimer’s disease (AD) is promoted by a combination of genetic and environmental factors. Notably, combined exposure to triazine herbicides atrazine (ATR), simazine (SIM), and propazine (PRO) may promote the development of AD, but the mechanism is unknown. To study the molecular mechanism of AD induced by triazine herbicides. Differentially expressed genes (DEGs) of AD patients and controls were identified. The intersectional targets of ATR, SIM, and PRO for possible associations with AD were screened through network pharmacology and used for gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis. The binding potentials between the core targets and herbicides were validated by molecular docking and molecular dynamics. A total of 1,062 DEGs were screened between the AD patients and controls, which identified 148 intersectional targets of herbicides causing AD that were screened by network pharmacology analysis. GO and KEGG enrichment analysis revealed that cell cycling and cellular senescence were important signalling pathways. Finally, the core targets EGFR, FN1, and TYMS were screened and validated by molecular docking and molecular dynamics. Our results suggest that combined exposure to triazine herbicides might promote the development of AD, thereby providing new insights for the prevention of AD.</p

Identification of the Intrinsic Active Site in Phase-Pure M1 Catalysts for Oxidation Dehydrogenation of Ethane by Density Functional Theory Calculations

Heterogeneous catalysts for alkane conversion reactions are required to possess both high activity for C–H bond cleavage and selectivity to target products. This work employed an atomic substitution strategy to investigate the active site of the phase-pure M1 MoVNbTeOx catalyst for the oxidation dehydrogenation of ethane (ODHE) reaction. Density of states and crystal orbital Hamilton population (COHP) based on density functional theory calculations indicated that the transition metal–O (M–O) bonds were weakened after H adsorption. Both integrated COHP and Bader charge were useful descriptors to correlate the electronic structure with catalytic performance. The results showed that the content of V in phase-pure M1 catalysts had a linear relationship with ethane conversion. Synergetic interactions between Te–O and V–O sites were accordingly considered as the intrinsic active sites for the ODHE reaction

Membrane permeability changes of BGC-823 cells by monitoring PI and LDH.

<p>The cells were incubated with increasing peptide concentrations for 1 h at 37 oC. (A) Quantitative comparisons of fluorescence intensity (in Geomean) at various concentrations were analyzed by flow cytometry. (B) LDH in the supernatant was measured with a microplate reader at 450 nm. Cells without treatment or lysed with triton X-100 was used as negative and positive controls, respectively. LDH activity was calculated as the percentage of experimental group and positive control, after subtraction of negative control respectively (*P<0.005). Data are the mean ± SD of three independent experiments.</p

Cell viability and combination index of BGC-823 and SGC-7901 treated with a drug combination.

<p>Panel (A, B and C) represents growth inhibition in BGC-823 and SGC-7901 cells with a combination of HPRP-A2 (6 μM) and DOX (1.6 μg/ml) after incubation for 4, 24 and 48 hours, respectively. Results are expressed as the percentage of the control ± SD of three independent experiments. Panel D shows combination index (Q) of the combination treatment of HPRP-A2 and DOX, where Q<0.85, Q>1.15 and 0.85</p