Analysis of groundwater ion abnormality and its cause of centralized drinking water sources in Jieshou City, China

Groundwater provides drinking water to city and rural residents; which is also one of the chief water sources for commercial and agricultural activities in Jieshou City. We collected and analyzed the samples of 18 underground water source wells in Jieshou. We investigated whether the water was of acceptable quality and had characteristics that exceeded the standard. This study was conducted to determine the chemical characteristics of groundwater and abnormally high super-standard ions found in groundwater. The hydrogeological conditions of the study area were analyzed through data collection; through sample collection and sample testing, the characteristics and types of water chemistry were analyzed by means of mathematical statistics analysis and the Piper chart. The genesis of water chemistry was discussed using the Gibbs chart and correlation analysis; the proportional coefficient of ion molar concentration was used to judge the source, origin, and forming process of groundwater chemical composition. The results show that the groundwater is classified as marginally alkaline water, with a composition of Na-HCO3. The cations are mainly Na+, and the anions are mainly HCO3−. According to the Ⅲ water standard of groundwater quality standard and comparing the content of each ion, Na+ and F− are the primary abnormal super-standard ions, and ions and compounds are the main occurrence states. The concentrations of Na+ and F− exceed the standard for class Ⅲ water. There was a positive correlation between the abnormal Na+ and F−, and the concentration of F− increased with the increase in monitoring depth. The causes of abnormal ions were mainly determined by the lithology of the aquifer in the study area, and most of them are fluorine-containing rocks, which are transferred into groundwater through leaching or hydration. The enrichment of Na+ and F− is influenced by the local primary geological setting, hydrochemical type, hydrogeological conditions, pH and artificial activities, and the primary geological setting is the main influencing factor

Pretreatment of lignocellulosic wheat straw in ethanolwater co-solvents

Pretreatment is the key process for lignocellulosic biomass conversion, which is necessary to alter the structure of biomass to make cellulose and hemicellulose more accessible to the enzymes that convert the carbohydrate polymers into fermentable sugars. The present study reports the use of 15 ml ethanol-water co-solvents (1:1, v/v) for the pretreatment of lignocellulosic biomass (1.5 g) to produce cellulosic residual solid under varying conditions of temperature (220-310 °C) and time (20-100 min). Kinetic analysis was performed to examine the decomposition behavior of biomass in the co-solvents. The results showed that the optimal conditions for the pretreatment were 250 °C and 40 min. The maximum yield of residual solid under the optimized pretreatment conditions was 49.6% (0.744 g), which consisted of 91.4% holocellulose (cellulose and hemicellulose). Microstructure analysis showed that the compact monolithic structure of biomass had decomposed into a loose filamentous structure

Considering Genetic Heterogeneity in the Association Analysis Finds Genes Associated With Nicotine Dependence

While substantial progress has been made in finding genetic variants associated with nicotine dependence (ND), a large proportion of the genetic variants remain undiscovered. The current research focuses have shifted toward uncovering rare variants, gene-gene/gene-environment interactions, and structural variations predisposing to ND, the impact of genetic heterogeneity in ND has been nevertheless paid less attention. The study of genetic heterogeneity in ND not only could enhance the power of detecting genetic variants with heterogeneous effects in the population but also improve our understanding of genetic etiology of ND. As an initial step to understand genetic heterogeneity in ND, we applied a newly developed heterogeneity weighted U (HWU) method to 26 ND-related genes, investigating heterogeneous effects of these 26 genes in ND. We found no strong evidence of genetic heterogeneity in genes such as CHRNA5. However, results from our analysis suggest heterogeneous effects of CHRNA6 and CHRNB3 on nicotine dependence in males and females. Following the gene-based analysis, we further conduct a joint association analysis of two gene clusters, CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6. While both CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6 clusters are significantly associated with ND, there is a much stronger association of CHRNB3-CHRNA6 with ND when considering heterogeneous effects in gender (p-value = 2.11E-07)

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

The Association Rules Algorithm Based on Clustering in Mining Research in Corn Yield

International audienceWith the popularization of agricultural information technology, the use of data mining techniques to analyze the impact of different types of soil nutrient content and yield of corn has become a hot topic in the field of agriculture. Association rule mining is an important part of the field in Data mining, association rules can be found associated with agricultural data attributes. This article will use cluster analysis and association rule to analysis correlation between corn yield and soil nutrient. Firstly compare different clustering algorithm to chooses the optimal algorithm, make data collected in scientific classification, and based on expert knowledge of the collected data into different levels; then determine the type and content of different soil by association rules corn yield and soil nutrient; final inspection algorithm is correct. The results showed that: comparing K-means, hierarchical clustering analysis, and PAM, K-means algorithm to determine the optimal clustering; K value can be determined at selected intervals. K is equal to 3, 4 or 6, clustering effect is good according to Sil value when K from 3 to 10. Based on the principle of association rules, clustering algorithm to select a K value associated with the combination of rule 6; After clustering algorithm of association rules, support and credibility and improve degree of accuracy is better than not clustering; by mining association rules after clustering, a great influence on the different levels of soil nutrients in corn yield. The results for the corn yield provides intelligent decision support data

A Nonlinear Model for Gene-Based Gene-Environment Interaction

A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction