GRIK3 rs490647 is a Common Genetic Variant between Personality and Subjective Well-being in Chinese Han Population

Personality and subjective well-being (SWB) have been suggested to be strongly related in previous studies. This study was intended to confirm the relationship between personality and SWB and tried to seek out the genetic variants which underlie both personality and SWB. The subjects were 890 participants from Chinese Han population. We evaluated their personality using the Big Five Inventory (BFI) and used the Satisfaction With Life Scale (SWLS) to reflect their SWB. Five single nucleotide polymorphisms (SNPs) were selected from the literature (rs1426371, rs2164273, rs322931, rs3756290, rs490647) and genotyped for genetic association study. We found negative correlations between neuroticism and SWB. On the contrary, extraversion and agreeableness were positively associated with SWB. Three SNPs (rs2164273, rs3756290, rs490647) out of the five were found to connect with personality (extraversion, neuroticism, conscientiousness and openness to experience) and rs490647 variants of GRIK3 was also associated with SWB. Individuals carrying G allele at this site were predisposed to have lower risk to be neuroticism and greater chance to be extraverted, open and satisfied with their life. In summary, our study revealed that rs490647 might be a good candidate genetic variant for personality and SWB in Chinese Han population

Environmental Adaptation: Genomic Analysis of the Piezotolerant and Psychrotolerant Deep-Sea Iron Reducing Bacterium Shewanella piezotolerans WP3

Shewanella species are widespread in various environments. Here, the genome sequence of Shewanella piezotolerans WP3, a piezotolerant and psychrotolerant iron reducing bacterium from deep-sea sediment was determined with related functional analysis to study its environmental adaptation mechanisms. The genome of WP3 consists of 5,396,476 base pairs (bp) with 4,944 open reading frames (ORFs). It possesses numerous genes or gene clusters which help it to cope with extreme living conditions such as genes for two sets of flagellum systems, structural RNA modification, eicosapentaenoic acid (EPA) biosynthesis and osmolyte transport and synthesis. And WP3 contains 55 open reading frames encoding putative c-type cytochromes which are substantial to its wide environmental adaptation ability. The mtr-omc gene cluster involved in the insoluble metal reduction in the Shewanella genus was identified and compared. The two sets of flagellum systems were found to be differentially regulated under low temperature and high pressure; the lateral flagellum system was found essential for its motility and living at low temperature

Rolling Bearing Fault Diagnosis Algorithm Based on Overlapping Group Sparse Model-Deep Complex Convolutional Neural Network

Abstract As the key component of a mechanical system, rolling bearings will cause paralysis of the entire mechanical system once they fail. In recent years, considering the high generalization ability and nonlinear modeling ability of deep learning, a rolling bearing fault diagnosis method based on deep learning has been formed, and good results have been achieved. However, because this kind of method is still in the initial development stage, its main problems are as follows. First, it is difficult to extract the composite fault signal feature of rolling bearing. Second, the existing deep learning rolling bearing fault diagnosis methods cannot well consider the problem of multi-scale information of rolling bearing signals. Therefore, this paper first proposes the overlapping group sparse model. It constructs weight coefficients by analyzing the salient features of the signal. It uses convex optimization techniques to solve the sparse optimization model, and applies the method to the feature extraction of rolling bearing composite faults. For the problem of multi-scale feature information extraction of rolling bearing composite fault signals, this paper proposes a new deep complex convolutional neural network model. This model fully considers the multi-scale information of rolling bearing signals. The complex information in this model not only contains rich representation ability, but also can extract more scale information. Finally, the classifier of this model is used to identify rolling bearing faults. Based on this, this paper proposes a new rolling bearing fault diagnosis algorithm based on overlapping group sparse model-deep complex convolutional neural network. The experimental results show that the method proposed in this paper can not only effectively identify rolling bearing faults under constant operating conditions, but also accurately identify rolling bearing fault signals under changing operating conditions. Additionally, the classification accuracy of the method proposed in this paper is greatly improved compared with traditional machine learning methods. It also has certain advantages over other deep learning methods.</jats:p