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BackgroundIn European and US studies, patients with major depressive disorder (MDD) report more stressful life events (SLEs) than controls, but this relationship has rarely been studied in Chinese populations.MethodsSixteen lifetime SLEs were assessed at interview in two groups of Han Chinese women: 1970 clinically ascertained with recurrent MDD and 2597 matched controls. Diagnostic and other risk factor information was assessed at personal interview. Odds ratios (ORs) were calculated by logistic regression.Results60% of controls and 72% of cases reported at least one lifetime SLE. Fourteen of the sixteen SLEs occurred significantly more frequently in those with MDD (median odds ratio of 1.6). The three SLEs most strongly associated with risk for MDD (OR > 3.0) preceded the onset of MDD the majority of the time: rape (82%), physical abuse (100%) and serious neglect (99%).LimitationsOur results may apply to females only. SLEs were rated retrospectively and are subject to biases in recollection. We did not assess contextual information for each life event.ConclusionsMore severe SLEs are more strongly associated with MDD. These results support the involvement of psychosocial adversity in the etiology of MDD in China.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents inIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

BackgroundAlthough the diagnosis of melancholia has had a long history, the validity of the current DSM-IV definition remains contentious. We report here the first detailed comparison of melancholic and nonmelancholic major depression (MD) in a Chinese population examining in particular whether these two forms of MD differ quantitatively or qualitatively.MethodsDSM-IV criteria for melancholia were applied to 1,970 Han Chinese women with recurrent MD recruited from 53 provincial mental health centers and psychiatric departments of general medical hospitals in 41 cities. Statistical analyses, utilizing Student's t-tests and Pearson's χ2, were calculated using SPSS 13.0.ResultsMelancholic patients with MD were distinguished from nonmelancholic by being older, having a later age at onset, more episodes of illness and meeting more A criteria. They also had higher levels of neuroticism and rates of lifetime generalized anxiety disorder, panic disorder, and social and agoraphobia. They had significantly lower rates of childhood sexual abuse but did not differ on other stressful life events or rates of MD in their families.DiscussionConsistent with most prior findings in European and US populations, we find that melancholia is a more clinically severe syndrome than nonmelancholic depression with higher rates of comorbidity. The evidence that it is a more “biological” or qualitatively distinct syndrome, however, is mixed.

Automatic grammatical error detection for Chinese has been a big challenge for NLP researchers for a long time, mostly due to the flexible and irregular ways in the expressing of this language. Strictly speaking, there is no evidence of a series of formal and strict grammar rules for Chinese, especially for the spoken Chinese, making it hard for foreigners to master this language. The CFL shared task provides a platform for the researchers to develop automatic engines to detect grammatical errors based on a number of manually annotated Chinese spoken sentences. This paper introduces HITSZ's system for this year's Chinese grammatical error diagnosis (CGED) task. Similar to the last year's task, we put our emphasis mostly on the error detection level and error type identification level but did little for the position level. For all our models, we simply use supervised machine learning methods constrained to the given training corpus, with neither any heuristic rules nor any other referenced materials (except for the last years' data). Among the three runs of results we submitted, the one using the ensemble classifier Random Feature Subspace (HITSZ_Run1) gained the best performance, with an optimal F1 of 0.6648 for the detection level and 0.2675 for the identification level.

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On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, fault tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a fault tree diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation.