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Long-term exposure to major constituents of fine particulate matter and sleep disorders among children and adolescents: A population-based survey in Guangdong province, China
No full textLong-term exposure to PM2.5 is associated with sleep health in adults, but its impact on children and adolescents remains unclear. This cross-sectional study analyzed 64,203 children and adolescents (6–18 years) from four cities in Guangdong, China, to assess the impact of PM2.5 and its major constituents on sleep disorders. Sleep disorders were assessed using the Sleep Disturbance Scale for Children. Generalized linear mixed models and weighted quantile sum regression were applied to assess the joint effects of PM2.5 constituents. The results indicated a positive association between PM2.5 and sleep disorders. For example, the odds of sleep disorder increased with per interquartile range (IQR) increase in PM2.5 concentrations [odds ratio (OR): 1.15, 95 % confidence interval (CI):1.06, 1.26). Black carbon (BC) and organic matter (OM) increase the odds by 16 % (95 % CI: 9, 24 %) and 23 % (95 % CI: 9, 39 %), respectively. Notably, combustion-related BC (44.3 %) and OM (24.3 %) contributed the key contributors to the mixture risk. Children and adolescents with limited outdoor activity (< 2 hours/day) were more vulnerable to PM2.5 exposure (P for interaction = 0.028). These findings highlight that stricter air pollution regulations and targeted measures to reduce children’s exposure to combustion-derived pollutants are essential for protecting sleep health and overall well-being
The Nexus between Intellectual Property and Trade : Advancing West Africa's Economic (Partnership) Objectives through Optimally Harmonized Intellectual Property Regulation
No full text[PART 2] Design, Synthesis, and Development of dual CatL-Main Pro Inhibitors of SARS-CoV-2
No full textMD simulations of Mpro with respective inhibitors with different mutation backgrounds. For more details please check the respective JMC publication. 1291- Mpro 7TE0_WT_nirma 13A3 OPLS41292- Mpro 8H82_E166V_nirma 13A3 OPLS41293- Mpro 8H7W_S144M_nirma 13A3 OPLS4 (originally 8H7W is S144A)1294- Mpro WT FP237-xtal bound to 8n/FP238 non cov 13A3 OPLS41295- Mpro WT FP237-xtal bound to 8n/FP238 covbound 13A3 OPLS41296- Mpro E166V FP237-xtal bound to 8n/FP238 non cov 13A3 OPLS41297- Mpro E166V FP237-xtal bound to 8n/FP238 covbound 13A3 OPLS41298- Mpro S144M FP237-xtal bound to 8n/FP238 non cov 13A3 OPLS41299- Mpro S144M FP237-xtal bound to 8n/FP238 covbound 13A3 OPLS41300- Mpro 7TE0_WT_nirma (NON COV) 13A3 OPLS4 (200ns/day) - 10x200ns (gpu48_cov
Hallucinations in function calls of large language models: a case study of updating substation and transmission line asset data to an external system
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Behind the Climate Actions - The Influence of Values on Climate Action in the Municipalities of Global North
No full textSurvival of patients with mantle cell lymphoma in the rituximab era: retrospective binational analysis between 2000 and 2020
No full textDataset for "Environmental drivers of increased ecosystem respiration in a warming tundra"
No full textData forNature manuscript titled “Environmental drivers of increased ecosystem respirationin a warming tundra” Corresponding author Dr. Sybryn Maes – [email protected] Github contains all R scripts onhttps://github.com/mjalava/tundraflux Part A. Meta-analysis Thebold names refer to scripts (see the Github repository https://github.com/mjalava/tundraflux)and names in italics refer to files in this repository df_0 -Study design Figure 1 and Extended Fig. 1 from main text df_1a -Effect size calculations of response (ER) -Links to df_1.csv file with raw flux and environmental data -Only the experiments that state ‘Open Access’ in the excel file Authors_Datasets (sheet 2). For experiments stating ‘Available Upon Request’, you need to contact the authors for the -raw flux data. df_1b -Effect size calculations of environmental drivers -Links to df_1.csv file with raw flux data data (see above) and Dataset_ID.csv (this file includes all dataset IDs to merge the drivers into one dataframe) df_2a-f -Meta-analysis (2a) and meta-regression models (2b-f) (ER, N=136) -Links to df_2.csv file with effect size data and context-dependencies and Forestplot_horiz_weights_fig.csv (this file includes the mean pooled Hedges SMD as well as the individual dataset Hedges SMD to plot figure 2) -Contains code for Figs. 2-4 and Extended Figs 2-3 df_3 -Meta-regression for experimental warming duration -Contains code for Fig. 5 df_4a -Effect size calculations of autotrophic-heterotrophic respiration partitioning (Ra, Rh, N=9) -Links to df_3.csv file with raw partitioning data of subset experiments (output file df_4.csv) df_4b -Sub-meta-analysis models (ER, Ra, Rh) -Links to df_4.csv (input file) NOTES · All additional input files for the meta-analysis R-scripts are included within the folders. · ER, Ra, Rh = ecosystem, autotrophic, and heterotrophic respiration · N = sample size (number of datasets) Part B. Upscaling results For upscaling, the input data is described in the code files (see the Github repository) and the accompanying Readme.txt. percentageChangeResp_tundraAlpine.tif: modelled change in respiration baseResp_tundraAlpine.tif: baseline respiration (calculated from the data from literature) modResp_tundraAlpine.tif: modelled respiration after warming (our calculations: (percentageChangeResp_tundraAlpine+1) * baseResp_tundraAlpine) changeResp_tundraAlpine.tif: modResp-baseResp standError_tundraAlpine.tif: standard error of modelled respiration ( standError_tundraAlpine_onlyDataUncertainty.tif: standard error of modelled respiration where only data uncertainty is taken into accoun
