Project description:Exposure to indoor air pollution generated from the combustion of solid fuels is a major risk factor for a spectrum of cardiovascular and respiratory diseases, including lung cancer. In Chinaâs rural counties of Xuanwei and Fuyuan, lung cancer rates are among the highest in the country. While the elevated disease risk in this population has been linked to the widespread usage of bituminous (smoky) coal as compared to anthracite (smokeless) coal, the underlying physiologic mechanism that smoky coal induces in comparison to other fuel types is unclear. As we have previously used airway gene-expression profiling to gain molecular insights into the physiologic effects of cigarette smoke, here we profiled the buccal epithelium of residents exposed to the burning of smoky and smokeless coal in order to understand the physiologic effects of solid fuels. Buccal mucosa scrapings were collected from healthy, non-smoking female residents of Xuanwei and Fuyuan counties who burn coal indoors. RNA was isolated and hybridized onto Affymetrix Human gene 1.0 ST GeneChips, capturing the gene-expression response of (n=26) smoky coal users and (n=9) smokeless coal users. 24-hour indoor personal exposure levels (PM2.5, Polycyclic Aromatic Hydrocarbons) were also captured during this sampling period.
Project description:The dark and humid environment of underground coal mines had a detrimental effect on workers' skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.
Project description:Exposure to indoor air pollution generated from the combustion of solid fuels is a major risk factor for a spectrum of cardiovascular and respiratory diseases, including lung cancer. In China’s rural counties of Xuanwei and Fuyuan, lung cancer rates are among the highest in the country. While the elevated disease risk in this population has been linked to the widespread usage of bituminous (smoky) coal as compared to anthracite (smokeless) coal, the underlying physiologic mechanism that smoky coal induces in comparison to other fuel types is unclear. As we have previously used airway gene-expression profiling to gain molecular insights into the physiologic effects of cigarette smoke, here we profiled the buccal epithelium of residents exposed to the burning of smoky and smokeless coal in order to understand the physiologic effects of solid fuels.
Project description:To effectively monitor microbial populations in acidic environments and bioleaching systems, a comprehensive 50-mer-based oligonucleotide microarray was developed based on most of the known genes associated with the acidophiles. This array contained 1,072 probes in which there were 571 related to 16S rRNA and 501 related to functional genes. Acid mine drainage (AMD) presents numerous problems to the aquatic life and surrounding ecosystems. However, little is known about the geographic distribution, diversity, composition, structure and function of AMD microbial communities. In this study, we analyzed the geographic distribution of AMD microbial communities from twenty sites using restriction fragment length polymorphism (RFLP) analysis of 16S rRNA genes, and the results showed that AMD microbial communities were geographically distributed and had high variations among different sites. Then an AMD-specific microarray was used to further analyze nine AMD microbial communities, and showed that those nine AMD microbial communities had high variations measured by the number of detected genes, overlapping genes between samples, unique genes, and diversity indices. Statistical analyses indicated that the concentrations of Fe, S, Ca, Mg, Zn, Cu and pH had strong impacts on both phylogenetic and functional diversity, composition, and structure of AMD microbial communities. This study provides insights into our understanding of the geographic distribution, diversity, composition, structure and functional potential of AMD microbial communities and key environmental factors shaping them. This study investigated the geographic distribution of Acid Mine Drainages microbial communities using a 16S rRNA gene-based RFLP method and the diversity, composition and structure of AMD microbial communities phylogenetically and functionally using an AMD-specific microarray which contained 1,072 probes ( 571 related to 16S rRNA and 501 related to functional genes). The functional genes in the microarray were involved in carbon metabolism (158), nitrogen metabolism (72), sulfur metabolism (39), iron metabolism (68), DNA replication and repair (97), metal-resistance (27), membrane-relate gene (16), transposon (13) and IST sequence (11).
Project description:Many resources, such as oil, gas, or water, are extracted from porous soils and their exploration is often shared among different companies or nations. We show that the effective shares can be obtained by invading the porous medium simultaneously with various fluids. Partitioning a volume in two parts requires one division surface while the simultaneous boundary between three parts consists of lines. We identify and characterize these lines, showing that they form a fractal set consisting of a single thread spanning the medium and a surrounding cloud of loops. While the spanning thread has fractal dimension 1.55 ± 0.03, the set of all lines has dimension 1.69 ± 0.02. The size distribution of the loops follows a power law and the evolution of the set of lines exhibits a tricritical point described by a crossover with a negative dimension at criticality.