RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells

Asthma is a chronic inflammatory respiratory disease that affects over 300 million people worldwide. Glucocorticoids are a mainstay therapy for asthma because they exert anti-inflammatory effects in multiple lung tissues, including the airway smooth muscle (ASM). However, the mechanism by which glucocorticoids suppress inflammation in ASM remains poorly understood. Using RNA-Seq, a high-throughput sequencing method, we characterized transcriptomic changes in four primary human ASM cell lines that were treated with dexamethasone—a potent synthetic glucocorticoid (1 µM for 18 hours). Based on a Benjamini-Hochberg corrected p-value <0.05, we identified 316 differentially expressed genes, including both well known (DUSP1, KLF15, PER1, TSC22D3) and less investigated (C7, CCDC69, CRISPLD2) glucocorticoid-responsive genes. CRISPLD2, which encodes a secreted protein previously implicated in lung development and endotoxin regulation, was found to have SNPs that were moderately associated with inhaled corticosteroid resistance and bronchodilator response among asthma patients in two previously conducted genome-wide association studies. Quantitative RT-PCR and Western blotting showed that dexamethasone treatment significantly increased CRISPLD2 mRNA and protein expression in ASM cells. CRISPLD2 expression was also induced by the inflammatory cytokine IL1β, and small interfering RNA-mediated knockdown of CRISPLD2 further increased IL1β-induced expression of IL6 and IL8. Our findings offer a comprehensive view of the effect of a glucocorticoid on the ASM transcriptome and identify CRISPLD2 as an asthma pharmacogenetics candidate gene that regulates anti-inflammatory effects of glucocorticoids in the ASM

Polygenic risk score for atopic dermatitis in the Canadian population

Atopic dermatitis (AD) is characterized by a damaged skin barrier that allows allergens to penetrate the body, leading to sensitization and a higher risk of developing food allergies (relative risk [RR], 33.79), asthma (RR, 7.04), and/or rhinitis (RR, 11.75), all features of the atopic march.1 Recent evidence has shown that the atopic march can be modified in high-risk infants with early interventions directed at reestablishing and/or maintaining skin barrier function with intense use of simple emollients, and introducing food allergens early into the diet.2, 3, 4, 5 Although these constitute examples of low-intensity, high-impact interventions for health care systems, their successful and indiscriminate implementation in the whole population is neither feasible nor realistic. In this context, building a predictive tool to identify children at high risk of developing moderate to severe AD (MSAD) would allow targeted interventions with maximized impact. In this study, a polygenic risk score (PRS) with an area under the curve (AUC) of 88% and explaining 37% of MSAD variance was established for the Canadian population

Pharmacogenomics and genetic risk factors of coronary artery disease

Coronary artery disease (CAD) is the most prevalent disorder and the leading cause of death worldwide. There are a number of CAD medications, which are effective and safe in most patients, but have been associated with adverse reactions such as angioedema induced by angiotensin I-converting enzyme inhibitors (AE-ACEi). In this study, we identified aminopeptidase P (APP) activity as an endophenotype for AE-ACEi, which is a heritable quantitative trait (heritability =0.336 +/- 0.251 SD) and is significantly reduced in a majority of our cases. Although initial mutation screening did not reveal any coding variants in XPNPEP2, which encodes membrane-bound APP, subsequent linkage analysis of APP activity in eight families provided a maximum LOD score (3.75) for this locus. Sequencing of additional cases identified a splice variant (314_431del) and a non-coding polymorphism (rs3788853) in this locus, which cosegregate with low plasma APP activity. The latter accounts for the linkage signal and is associated with AE-ACEi (P = 0.036). In addition, we identified other potential loci for APP activity and demonstrated that certain ACEi (Captopril and Enalapril) non-specifically inhibit APP activity. Furthermore, we detected polymorphisms associated with reduced APP and ACE activities among females with estrogen-dependent inherited angioedema.We also conducted a genetic investigation of depression among CAD patients to identify common susceptibility loci which might explain the correlation between these diseases. Our candidate gene association study identified a polymorphism (rs216873) in the von Willebrand factor gene that was significantly associated (P = 7.4 x 10-5) with elevated depressive symptoms in our CAD cohort. These results suggest that risk factors for atherosclerosis also underlie susceptibility to depression among CAD patients.This dissertation contributes to the field of genetics and pharmacogenomics of CAD. A better understanding of the toxic effects of CAD drugs will assist in the development of safer and more effective treatments. In addition, our results may facilitate clinical assays to identify individuals who are susceptible to angioedema prior to ACEi or estrogen therapy. Finally, our genetic investigation of depression in CAD patients reveals a novel drug target (VWF) for treatment of depression in cardiac cases

A Feature Selection Method for Multi-Label Text Based on Feature Importance

Multi-label text classification refers to a text divided into multiple categories simultaneously, which corresponds to a text associated with multiple topics in the real world. The feature space generated by text data has the characteristics of high dimensionality and sparsity. Feature selection is an efficient technology that removes useless and redundant features, reduces the dimension of the feature space, and avoids dimension disaster. A feature selection method for multi-label text based on feature importance is proposed in this paper. Firstly, multi-label texts are transformed into single-label texts using the label assignment method. Secondly, the importance of each feature is calculated using the method based on Category Contribution (CC). Finally, features with higher importance are selected to construct the feature space. In the proposed method, the feature importance is calculated from the perspective of the category, which ensures the selected features have strong category discrimination ability. Specifically, the contributions of the features to each category from two aspects of inter-category and intra-category are calculated, then the importance of the features is obtained with the combination of them. The proposed method is tested on six public data sets and the experimental results are good, which demonstrates the effectiveness of the proposed method

Healthy Breeding Service System of Vannamei Based on TD-SCDMA

Vannamei is recognized as one of the excellent variety of prawns in the world and has sanguine development foreground. However, the popularization of healthy farming technology in China is limited because of the weak agricultural information infrastructure. This paper takes full advantages of mobile network infrastructure and the features of surfing the Internet with smart phone, designs and implements the healthy breeding service system of vannamei based on TD-SCDMA, WAP and sentence similarity algorithm. Farmers can obtain real-time knowledge of healthy breeding, download training videos and receive early-warning information from the system. Furthermore, farmers can get SMS messages which contain video download addresses or early-warning information URLs chosen by users’ customized interests. This system proposes a better way for farmers to get the breeding knowledge and it can provide service whenever and wherever users want

Development of Early-Warning Model for Intensive Pig Breeding

International audienceFollowing the rapid development of intensive pig breeding in China, the impact of environmental factors on the production and health of pigs has become increasingly apparent, and the monitoring of these environmental factors recognized as critical for improved breeding productivity. Based on the effects of environmental factors on pig growth, this paper established an early-warning model of the piggery environment. Using the model and the environmental factors, which were obtained in real time from a piggery, it was possible to obtain timely warning information, conducive to both creating an appropriate breeding environment for pigs and reducing the incidence of disease. In this article, we established the environmental early-warning indicators relating to pig breeding and then demonstrated the method based on single-factor and fuzzy comprehensive multi-factor models of the piggery environment. Finally, the two models were analyzed based on the experimental results, which showed that the fuzzy comprehensive early-warning model performed better than the single-factor model, and that it could be applied in an intensive farming environment to provide timely warning of environmental deterioration, to maintain the safety of the pig-breeding environment

Advances on Water Quality Detection by UV-Vis Spectroscopy

Water resources are closely linked to human productivity and life. Owing to the deteriorating water resources environment, accurate and rapid determination of the main water quality parameters has become a current research hotspot. Ultraviolet-visible (UV-Vis) spectroscopy offers an effective tool for qualitative analysis and quantitative detection of contaminants in a water environment. In this review, the principle and application of UV-Vis technology in water quality detection were studied. The principle of UV-Vis spectroscopy for detecting water quality parameters and the method of modeling and analysis of spectral data were presented. Various UV-Vis technologies for water quality detection were reviewed according to the types of pollutants, such as chemical oxygen demand, heavy metal ions, nitrate nitrogen, and dissolved organic carbon. Finally, the future development of UV-Vis spectroscopy for the determination of water quality was discussed

Modeling and Analysis of Pollution-free Agricultural Regulatory Based on Petri-net

Abstract: To carry out pollution-free agricultural products certification work is the important issue of people&apos;s daily lives. It is of great significance to construct the pollution-free agricultural regulatory system (PFARS) and achieve the office automation of the certification business. However, the PFARS contains much more steps in dispersion areas that it is very complex for business processes of E-PFAPC. In this paper, we not only provide the The results show that the PFARS system constructed by the workflow model can implement the business run automatically and own prefect performance

Modeling and Analysis of Pollution-Free Agricultural Regulatory Based on Petri-Net

International audienceTo carry out pollution-free agricultural products certification work is the important issue of people’s daily lives. It is of great significance to construct the pollution-free agricultural regulatory system (PFARS) and achieve the office automation of the certification business. However, the PFARS contains much more steps in dispersion areas that it is very complex for business processes of E-PFAPC. In this paper, we not only provide the method of modeling the PFARS with Petri Net, but also provide the improved method to analysis the Performance of the constructed workflow model. The model for the PFARS provides a more simply process management than original work method. The provided workflow model analysis method can effectively verify the performance of building model. The results show that the PFARS system constructed by the workflow model can implement the business run automatically and own prefect performance

Preliminary Design of a Recognition System for Infected Fish Species Using Computer Vision

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceFor the purpose of classification of fish species, a recognition system was preliminary designed using computer vision. In the first place, pictures were pre-processed by developed programs, dividing into rectangle pieces. Secondly, color and texture features are extracted for those selected texture rectangle fish skin images. Finally, all the images were classified by multi-class classifier named SVMs. The experiment showed that color and texture are the appropriate features for fish species classification. The multi-class classifier based on SVM will be developed for further work