Whole Genome Distribution and Ethnic Differentiation of Copy Number Variation in Caucasian and Asian Populations

Although copy number variation (CNV) has recently received much attention as a form of structure variation within the human genome, knowledge is still inadequate on fundamental CNV characteristics such as occurrence rate, genomic distribution and ethnic differentiation. In the present study, we used the Affymetrix GeneChip® Mapping 500K Array to discover and characterize CNVs in the human genome and to study ethnic differences of CNVs between Caucasians and Asians. Three thousand and nineteen CNVs, including 2381 CNVs in autosomes and 638 CNVs in X chromosome, from 985 Caucasian and 692 Asian individuals were identified, with a mean length of 296 kb. Among these CNVs, 190 had frequencies greater than 1% in at least one ethnic group, and 109 showed significant ethnic differences in frequencies (p<0.01). After merging overlapping CNVs, 1135 copy number variation regions (CNVRs), covering approximately 439 Mb (14.3%) of the human genome, were obtained. Our findings of ethnic differentiation of CNVs, along with the newly constructed CNV genomic map, extend our knowledge on the structural variation in the human genome and may furnish a basis for understanding the genomic differentiation of complex traits across ethnic groups

Genome-Wide Analysis of DNA Methylation and Cigarette Smoking in a Chinese Population

Background: Smoking is a risk factor for many human diseases. DNA methylation has been related to smoking, but genome-wide methylation data for smoking in Chinese populations is limited. Objectives: We aimed to investigate epigenome-wide methylation in relation to smoking in a Chinese population. Methods: We measured the methylation levels at > 485,000 CpG sites (CpGs) in DNA from leukocytes using a methylation array and conducted a genome-wide meta-analysis of DNA methylation and smoking in a total of 596 Chinese participants. We further evaluated the associations of smoking-related CpGs with internal polycyclic aromatic hydrocarbon (PAH) biomarkers and their correlations with the expression of corresponding genes. Results: We identified 318 CpGs whose methylation levels were associated with smoking at a genome-wide significance level (false discovery rate < 0.05), among which 161 CpGs annotated to 123 genes were not associated with smoking in recent studies of Europeans and African Americans. Of these smoking-related CpGs, methylation levels at 80 CpGs showed significant correlations with the expression of corresponding genes (including RUNX3, IL6R, PTAFR, ANKRD11, CEP135 and CDH23), and methylation at 15 CpGs was significantly associated with urinary 2-hydroxynaphthalene, the most representative internal monohydroxy-PAH biomarker for smoking. Conclusion: We identified DNA methylation markers associated with smoking in a Chinese population, including some markers that were also correlated with gene expression. Exposure to naphthalene, a byproduct of tobacco smoke, may contribute to smoking-related methylation. Citation: Zhu X, Li J, Deng S, Yu K, Liu X, Deng Q, Sun H, Zhang X, He M, Guo H, Chen W, Yuan J, Zhang B, Kuang D, He X, Bai Y, Han X, Liu B, Li X, Yang L, Jiang H, Zhang Y, Hu J, Cheng L, Luo X, Mei W, Zhou Z, Sun S, Zhang L, Liu C, Guo Y, Zhang Z, Hu FB, Liang L, Wu T. 2016. Genome-wide analysis of DNA methylation and cigarette smoking in Chinese. Environ Health Perspect 124:966–973; http://dx.doi.org/10.1289/ehp.150983

Sensor Fault Diagnosis Based on Fuzzy Neural Petri Net

This study aims to improve the operating stability of the resistance strain weighing sensor and eliminate fuzzy factors in fault diagnosis. Based on fuzzy techniques for fault diagnosis, the proposed fuzzy Petri net model uses the fault logical relationship between a sensor and an improved Petri net model. A formula for confidence-based reasoning is proposed using an algorithm, which combines neural network regulation algorithm with a transition-enabled ignition judgment matrix. This formula can yield an accurate assessment of the operating state of the sensor. Backward inference and the minimum cut set theory are also combined to obtain the priority of faults, which helps avoid blind and ambiguous maintenance. The sensor model was analyzed, and its accuracy and validity were verified through statistical analysis and comparison with other methods of fault diagnosis

Biobased Poly(ethylene 2,5-furancoate): No Longer an Alternative, but an Irreplaceable Polyester in the Polymer Industry

Recently, studies on the synthesis, modification, and functionalization of polyesters derived from 2,5-furandicarboxylic acid (2,5-FDCA) have attracted widespread attention. Among them, poly(ethylene 2,5-furanoate) (PEF) has been in the spotlight due to its greener production process, higher glass transition temperature (T-g), good mechanical performance, and excellent barrier properties, which have qualified it as a new generation of packaging material to be considered for replacement of polyethylene terephthalate (PET). Currently it is a promising rising star in the field of biobased polymers, yet there has been no review entirely focused on it. This article focuses on the research related PEF, discussing its mainstream synthetic methods including optimization of catalysts with solutions of coloration and side reactions, and its comprehensive properties such as thermal, crystallization, mechanical, and barrier performances. Other aspects highlighted are modifications for better properties and the introduction of biodegradability as well as the construction of composites materials. This article aims to reveal the development path of PEF and help us to understand it more deeply and widely in a short time

Advances in the role and mechanism of miRNA in inflammatory pain

Pain is a distressing experience associated with tissue damage or potential tissue damage, and its occurrence is related to sensory, emotional, cognitive and social factors. Inflammatory pain is one of the chronic pains where pain hypersensitivity are functional features of inflammation used to protect tissues from further damage. Pain has a serious impact on people's lives and has become a social problem that cannot be ignored. MiRNAs are small non-coding RNA molecules that exert directing effects on RNA silencing by complementary binding to the 3′UTR of target mRNA. MiRNAs can target a number of protein-coding genes and participate in almost all developmental and pathological processes in animals. Growing studies have suggested that miRNAs have significant implications for inflammatory pain via participating in multiple processes during the occurrence and development, such as affecting the activation of glial cells, regulating pro-inflammatory cytokines and inhibiting central and peripheral sensitization. In this review, the advances in the role of miRNAs in inflammatory pain were discussed. miRNAs as a class of micro-mediators are potential biomarkers and therapeutic targets for inflammatory pain, which provides a better diagnostic and treatment approach for inflammatory pain

Fault diagnosis method of distribution network with distributed generatio

It can change magnitude and direction of fault current of distributed network after distributed generation was connected to power distribution system, which made fault diagnosis of power distribution system complicated. For the above problem, a fault diagnosis method of distribution network based on power direction criterion and Petri net was proposed. The method uses uploading and measured double fault information to realize redundant error correcting, and can accurately and quickly diagnose fault areas, so as to improve fault diagnosis accuracy and fault tolerance of distribution system with distributed generation. At the same time, the method uses unique graphic description and parallel processing ability of Petri nets to ensure universality of fault location model and rapidity of fault diagnosis. The example simulation results verify feasibility and effectiveness of the proposed method

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal &#8220;eating ripe&#8222; stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles