The carbon components in indoor and outdoor PM2.5 in winter of Tianjin

To study the carbon components in indoor and outdoor PM2.5, the samples of PM2.5 were collected from Nankai University in December 2015. The contents of eight carbon components were analyzed to use the thermo-optical reflection method. The results indicated that organic carbon (OC) mass concentration was 17.01, 19.48 and 18.92 µg/m3 in outdoor, dormitory and laboratory; elemental carbon (EC) mass concentration was 7.97, 3.56 and 3.53 µg/m3 in outdoor, dormitory and laboratory; and the total carbon aerosol was the proportion of more than 23% of PM2.5 samples. Lower wind speed and higher relative humidity were helpful to the accumulation of PM2.5. The ratio of OC/EC was > 2, and the SOC/OC ratio was > 30%, indicating that SOC was a crucial component indoors and outdoors. About 72% and 85% of the outdoor OC entering dormitory and laboratory environment, and about 59% and 71% of the outdoor EC entering dormitory and laboratory environment. Factor analysis of the eight carbon fractions indicated that the sources of OC and EC in outdoor, dormitory and laboratory is different.

SNPxE: SNP-environment interaction pattern identifier

Background Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. Results SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. Conclusions SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).

Critical Success Factors for Leveraging Technology Transfer from Higher Education Institutions to Industry: Indian Context

This study aims to identify and empirically evaluates the critical factors of successful technology transfer (TT) from higher education institutions (HEI) to industry and to develop a TT model in the Indian context. With the help of questionnaire survey, the perception profile of 318 respondents was collected from PAN-India. Utilizing the survey data, factor analysis identified six constructs representing five critical factors of TT and an outcome factor. The results of multiple regression analysis indicate significant effect of all five critical factors on an outcome factor. The findings of the study validate the TT Model and identify supportive and promotive TT environment factor, market and finance factor and HEI factor to be the most important factors of successful TT. This study may help the policymakers in strategizing future initiatives to improve the rate of successful TT.

MWF technology use to reduce environment factor impact upon stability of polymeric composite structures to transverse load effect

The results of three-point bend comparative tests of carbon and glass-fiber samples checked and treated in the MWF electromagnetic field after external environment factor impact in the course of eight months are shown. It is defined that the modification of carbon fiber in a hardened state in the MWF electromagnetic field decreases a negative impact of external environment of strength decrease after eight-month exposure by 44.3…73%, glass-fiber – by 6%. The MWF treatment is offered as a finishing technological operation at manufacturing polymeric composite design elements.

Group Lasso for Identifying Tuberculosis Influenced Factors in West Java

Tuberculosis (TB) is a disease caused by bacteria that can affect serious infection in the lungs. It can spread from one person to another through tiny droplets released into the air via coughs and sneezes. West Java is one of the locations with the largest TB sufferer in Indonesia. This research used the number of TB cases data in West Java 2017 to identify group of factors that can influence TB infections. The group factors applied include health factor, environment factor, health facility factor, and demography factor. The method that used in this research was group lasso. After the group factors have been identified, the ordinary least square applied to determine significant factors to affect TB infections based on these group factors. The selected group factors of this research was health factor, environment factor, and health facility factor significant to influence TB cases. Furthermore, number of diarrhea sufferers, unemployment, and number of malnutrition sufferers were significant to be the variables that important to affect the TB cases in West Java based on ordinary least square.