Additional file 1: of The impact of the health care workforce on under-five mortality in rural China

Appendix 1: Table A1: Information on missing data. Appendix 2: Imputation approach for female illiteracy rate. Appendix 3: Table A2: Additional sensitivity analyses for the association between the density of health professionals andthe under-five mortality rate in rural China, 2008-2014. (DOCX 37 kb

sj-docx-1-jet-10.1177_15266028231209236 – Supplemental material for A Systematic Review and Meta-analysis of Atherectomy Plus Balloon Angioplasty Versus Balloon Angioplasty Alone for Infrapopliteal Arterial Disease

Supplemental material, sj-docx-1-jet-10.1177_15266028231209236 for A Systematic Review and Meta-analysis of Atherectomy Plus Balloon Angioplasty Versus Balloon Angioplasty Alone for Infrapopliteal Arterial Disease by Haichao Wu, Dandan Zheng, Long Zhou, Qiang Wang, Tao Wang and Siyuan Liang in Journal of Endovascular Therapy</p

DataSheet1_Variations, sources, and effects on ozone formation of VOCs during ozone episodes in 13 cities in China.docx

In recent years, ozone (O3) pollution has worsened in China and contributes frequently to air pollution problems. To support the implementation of coordinated control for ozone and fine particulate matter, it is essential to study the chemical compositions and sources of volatile organic compounds (VOCs), which are the crucial precursor of both ozone and fine particulate matter. In this study, 117 volatile organic compounds were monitored in 13 cities in Beijing-Tianjin-Hebei Urban Agglomeration and Fenwei plain. Concentrations of total volatile organic compounds ranged from 42 to 279 μg/m3 during the monitoring episode. In all 13 cities, alkanes, halogenated hydrocarbons, aromatics and oxygenated volatile organic compounds (OVOCs) were the dominant volatile organic compounds. Contributions of alkanes, halogenated hydrocarbons, aromatics and oxygenated volatile organic compounds to total ozone formation potential (OFP) were 21.7%–32.6%, 21.0%–27.9%, 24.3%–50.8% and 28.6%–52.3%, respectively. Furthermore, the results of source apportionment by positive matrix factorization (PMF) model indicated that solvent usage, gasoline evaporation, vehicle emissions, petrochemical industry and combustion were essential volatile organic compounds sources in 13 cities. Moreover, the sensitivity of ozone production was studied using an Empirical Kinetic Modeling Approach (EKMA) model, and it was found that ozone formation was volatile organic compounds limited in all 13 cities.</p

Additional file 1: of Breast cancer in postmenopausal women is associated with an altered gut metagenome

Table S1. Generated data of the four groups. Table S2. Distribution of the samples of the four groups in the two enterotypes. Table S3. Relative abundance of the different species between premenopausal breast cancer patients and premenopausal healthy controls. Table S4. Relative abundance of the different species between postmenopausal breast cancer patients and postmenopausal healthy controls. Table S5. PERMANOVA analysis was performed to assess effects of different phenotypes on gene profile. Table S6. The optimal species markers in the classification of postmenopausal breast cancer patients and postmenopausal healthy controls. Table S7. The abundance of Pathogen-Host Interactions (PHI) gene coding for diseases in postmenopausal breast cancer patients and postmenopausal healthy controls. Table S8. The virulence factor in samples of postmenopausal breast cancer patients and postmenopausal healthy controls. Table S9. Relative abundance of the different KEGG modules between premenopausal breast cancer patients and premenopausal healthy controls. Table S10. Relative abundance of the different KEGG modules between postmenopausal breast cancer patients and postmenopausal healthy controls. Table S11. Differentially enriched genes which annotated to butanoate metabolism pathways between postmenopausal breast cancer patients and postmenopausal healthy controls. Table S12. Relative abundance of the species of all the samples. Table S13. The species counts of all the samples. (XLS 2530 kb

Additional file 2: of Breast cancer in postmenopausal women is associated with an altered gut metagenome

Figure S1. Rarefaction for gut microbial gene in premenopausal breast cancer patients (n = 18), premenopausal healthy controls (n = 25), postmenopausal breast cancer patients (n = 44), and postmenopausal healthy controls (n = 46). Group 1 indicates premenopausal healthy controls, group 2 indicates premenopausal breast cancer patients, group 3 indicates postmenopausal healthy controls, and group 4 indicates postmenopausal breast cancer patients. Figure S2. The enterotypes of gut microbiota in breast cancer patients and healthy controls. (a) The optimal number of enterotypes was two of the four groups as indicated by Calinski-Harabasz (CH) index. The maximum CH index at two clusters (enterotypes) indicated the optimal enterotype number. (b) The gut microbiota of the four cohorts are clustered into two enterotypes at the genus level, dominated by either Bacteroides (enterotype 1) or Prevotella (enterotype 2). (c) Relative abundances of the top genera in the two enterotypes. (d) Distribution of the samples of the four groups in the two enterotypes. Figure S3. Relative abundance of the gut microbiota in the four groups at the phylum level. Figure S4. Relative abundance of the gut microbiota in the four groups at the genus level. Figure S5. Abundance distribution of the gut microbiota differed significantly between postmenopausal breast cancer patients and postmenopausal healthy controls at the genus level. Figure S6. Distribution of five trials of tenfold cross-validation error in random forest classification of postmenopausal breast cancer patients. The model was trained using the relative species abundances in patients and controls. The black line marks the average of the five trials (gray lines). The red line indicates the number of optimal species markers. Figure S7. Scatter plots for correlations between gut microbiota species and clinical indices. (DOCX 810 kb