Parity has been shown to inversely associate with cardiovascular disease (CVD) mortality, but the evidence of epidemiological studies is still controversial. Therefore, we quantitatively assessed the relationship between parity and CVD mortality by summarizing the evidence from prospective studies. We searched MEDLINE (PubMed), EMBASE and ISI Web of Science databases for relevant prospective studies of parity and CVD mortality through the end of March 2015. Fixed- or random-effects models were used to estimate summary relative risks (RRs) and 95% confidence intervals (CIs). Heterogeneity among studies was assessed using the I2 statistics. All statistical tests were two-sided. Ten prospective studies were included with a total of 994,810 participants and 16,601 CVD events. A borderline significant inverse association was observed while comparing parity with nulliparous, with summarized RR = 0.79 (95% CI: 0.60–1.06; I2 = 90.9%, P < 0.001). In dose-response analysis, we observed a significant nonlinear association between parity number and CVD mortality. The greatest risk reduction appeared when the parity number reached four. The findings of this meta-analysis suggests that ever parity is inversely related to CVD mortality. Furthermore, there is a statistically significant nonlinear inverse association between parity number and CVD mortality.

Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis. INDEX TERMS Cross-domain, domain adaptation, fault diagnosis, review, transfer learning.

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