Search

Your search keyword '"Yan Jie"' showing total 14,749 results
Start Over Author "Yan Jie"

Refine your results

more »

more »

more »

more »

more »
14,749 results on '"Yan Jie"'

153. Depression among Chinese university students: prevalence and socio-demographic correlates.

Author

Lu Chen, Lin Wang, Xiao Hui Qiu, Xiu Xian Yang, Zheng Xue Qiao, Yan Jie Yang, and Yuan Liang

Subjects
Medicine, Science
Abstract

The purpose of the present study was to estimate the prevalence of depression in Chinese university students, and to identify the socio-demographic factors associated with depression in this population. A multi-stage stratified sampling procedure was used to select university students (N = 5245) in Harbin (Heilongjiang Province, Northeastern China), who were aged 16-35 years. The Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores of 14 or higher were categorized as depressive for logistic regression analysis. Depression was diagnosed by the Structured Clinical Interview (SCID) for the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV). 11.7% of the participants had a BDI score 14 or higher. Major Depressive Disorder was seen in 4.0% of Chinese university students. There were no statistical differences in the incidence of depression when gender, ethnicity, and university classification were analyzed. Multivariate analysis showed that age, study year, satisfaction with major, family income situation, parental relationship and mother's education were significantly associated with depression. Moderate depression is prevalent in Chinese university students. The students who were older, dissatisfied with their major, had a lower family income, poor parental relationships, and a lower level of mother's education were susceptible to depression.

Published
2013
Full Text
View/download PDF

155. Transcriptome/degradome-wide identification of R. glutinosa miRNAs and their targets: the role of miRNA activity in the replanting disease.

Author

Ming Jie Li, Yan Hui Yang, Xin Jian Chen, Feng Qing Wang, Wen Xiong Lin, Yan Jie Yi, Lei Zeng, Shuo Ye Yang, and Zhong Yi Zhang

Subjects
Medicine, Science
Abstract

Rehmannia glutinosa, a traditional Chinese medicine herb, is unable to grow normally in a soil where the same species has recently been cultivated. The biological basis of this so called "replanting disease" is unknown, but it may involve the action of microRNAs (miRNAs), which are known to be important regulators of plant growth and development. High throughput Solexa/Illumina sequencing was used to generate a transcript library of the R. glutinosa transcriptome and degradome in order to identify possible miRNAs and their targets implicated in the replanting disease. A total of 87,665 unigenes and 589 miRNA families (17 of which have not been identified in plants to date) was identified from the libraries made from a first year (FP) and a second year (SP) crop. A comparison between the FP and SP miRNAs showed that the abundance of eight of the novel and 295 of the known miRNA families differed between the FP and SP plants. Sequencing of the degradome sampled from FP and SP plants led to the identification of 165 transcript targets of 85 of the differentially abundant miRNA families. The interaction of some of these miRNAs with their target(s) is likely to form an important part of the molecular basis of the replanting disease of R. glutinosa.

Published
2013
Full Text
View/download PDF

172. A Surrogate Objective Framework for Prediction+Optimization with Soft Constraints

Author

Yan, Kai, Yan, Jie, Luo, Chuan, Chen, Liting, Lin, Qingwei, and Zhang, Dongmei

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Optimization and Control
Abstract

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem. However, the criteria by which the prediction model is trained are often inconsistent with the goal of the downstream optimization problem. Recently, decision-focused prediction approaches, such as SPO+ and direct optimization, have been proposed to fill this gap. However, they cannot directly handle the soft constraints with the $max$ operator required in many real-world objectives. This paper proposes a novel analytically differentiable surrogate objective framework for real-world linear and semi-definite negative quadratic programming problems with soft linear and non-negative hard constraints. This framework gives the theoretical bounds on constraints' multipliers, and derives the closed-form solution with respect to predictive parameters and thus gradients for any variable in the problem. We evaluate our method in three applications extended with soft constraints: synthetic linear programming, portfolio optimization, and resource provisioning, demonstrating that our method outperforms traditional two-staged methods and other decision-focused approaches., Comment: 32 pages; published as NeurIPS 2021 poster paper

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results