Search

Background: Experimental research suggests that fine particulate matter (PM2.5) exposure might affect embryonic development. However, only few population-based studies have investigated the impact of maternal exposure to PM2.5 on the early pregnancy loss. Objectives: To estimate associations between clinically recognized early pregnancy loss (CREPL) and exposure to ambient PM2.5 at individual residences during peri-conception periods, with the aim to identify susceptible exposure time windows. Methods: CREPL cases and normal early pregnancy controls (of similar age and gravidity presenting within one week, a total of 364 pairs) were recruited between July 2017 and July 2018 among women residing in Tianjin, China. Average ambient PM2.5 concentrations of ten exposure windows (4 weeks, 2 weeks and 1 week before conception; the first, second, third and fourth single week, the first and second 2-week periods, and the entire 4-week period after conception) at the women's residential addresses were estimated using temporally-adjusted land use regression models. Associations between PM2.5 exposures at specific peri-conception time windows and CREPL were examined using conditional logistic regression models, adjusted for covariates. Results: Based on adjusted models, CREPL was significantly associated with a 10 μg/m3 increase in PM2.5 exposure during the second week after conception (OR = 1.15; 95% CI: 1.04, 1.27; p = 0.005), independent of effects at other time windows. There was also an association of CREPL with PM2.5 during the entire 4-week period after conception (OR = 1.22; 95% CI: 1.02, 1.46; p = 0.027). There was little evidence for associations with exposure during pre-conception exposure windows. Conclusions: Maternal exposures to ambient PM2.5 during a critical time window following conception are associated with CREPL, with the second week after conception possibly being the exposure window of most vulnerability. Future studies should focus on replicating these findings and on pathogenic mechanisms. Keywords: Adverse pregnancy outcomes, Air pollution, Land use regression models, Miscarriage, Particulate matter

Evidence for effects of PM

Experimental research suggests that fine particulate matter (PMTo estimate associations between clinically recognized early pregnancy loss (CREPL) and exposure to ambient PMCREPL cases and normal early pregnancy controls (of similar age and gravidity presenting within one week, a total of 364 pairs) were recruited between July 2017 and July 2018 among women residing in Tianjin, China. Average ambient PMBased on adjusted models, CREPL was significantly associated with a 10 μg/mMaternal exposures to ambient PM

Evidence for effects of PM2.5 on systemic oxidative stress in pregnant women is limited, especially in early pregnancy. To estimate the associations between ambient PM2.5 exposures and biomarkers of lipid peroxidation and total antioxidant capacity (T-AOC) in women with normal early pregnancy (NEP) and women with clinically recognized early pregnancy loss (CREPL), 206 early pregnant women who had measurements of serum malondialdehyde (MDA) and T-AOC were recruited from a larger case-control study in Tianjin, China from December 2017 to July 2018. Ambient PM2.5 concentrations of eight single-day lags exposure time windows before blood collection at the women’s residential addresses were estimated using temporally-adjusted land use regression models. Effects of PM2.5 exposures on percentage change in the biomarkers were estimated using multivariable linear regression models adjusted for month, temperature, relative humidity, gestational age and other covariates. Unconstrained distributed lag models were used to estimate net cumulative effects. Increased serum MDA and T-AOC were significantly associated with increases in PM2.5 at several lag exposure time windows in both groups. The net effects of each interquartile range increase in PM2.5 over the preceding 8 days on MDA were significantly higher (p

Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy [Formula: see text]-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concept of degree of belongingness, and PCAs suffer from the “coincident problem” and cannot provide very stable results for some data sets. In this study, we propose a new clustering algorithm, called the credibilistic clustering algorithm (CCA), based on the credibility measure. The credibility measure provides some unique properties which can solve the “coincident problem” and noise issue compared with the probability measure and possibility measure. Based on some randomly generated data sets, experimental results compared with FCM and PCA show that CCA can deal with the “coincident problem” with good clustering results, and it is more robust to noise than PCA.

Credibilistic clustering is a new clustering method using the credibility measure in fuzzy clustering. Zhou et al. (2014) presented the clustering model of credibilistic clustering together with a credibilistic clustering algorithm for solving the optimization model. In this paper, a further investigation on credibilistic clustering is made. Within the solution architecture of alternating cluster estimation, a family of general credibilistic clustering algorithms are designed for solving the credibilistic clustering model. Moreover, a new credibilistic clustering algorithm is recommended for the real applications. Numerical examples based on randomly generated data sets and real data sets are presented to illustrate the performance and effectiveness of the credibilistic clustering algorithms from different aspects. Results comparing with the fuzzy $$c$$c-means algorithm and the possibilistic clustering algorithms show that the proposed credibilistic clustering algorithms can survive from the coincident problem and the noisy environments, and provide the clustering results with high overall accuracy.

An inverse minimum spanning tree problem makes the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, the concept of uncertain α-minimum spanning tree is initiated for minimum spanning tree problem with uncertain edge weights. Using different decision criteria, two uncertain programming models are presented to formulate a specific inverse minimum spanning tree problem with uncertain edge weights involving a sum-type model and a minimax-type model. By means of the operational law of independent uncertain variables, the two uncertain programming models are transformed to their equivalent deterministic models which can be solved by classic optimization methods. Finally, some numerical examples on a traffic network reconstruction problem are put forward to illustrate the effectiveness of the proposed models.

The inverse spanning tree problem is to make a given spanning tree be a minimum spanning tree on a connected graph via a minimum perturbation on its edge weights. In this paper, a chance-constrained programming model is proposed to handle the inverse spanning tree problem where the edge weights are assumed to be uncertain variables. It is shown that such an uncertain minimum spanning tree can be characterized by some constraints on the paths of the graph. Consequently, the proposed model can be reformulated into a deterministic programming model. Furthermore, when the edge weights are linear uncertain variables, the corresponding model reduces to a linear programming problem and can be solved efficiently.

An inverse spanning tree problem is to make the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree. In this paper, based on the notion of fuzzy α-minimum spanning tree, the inverse spanning tree problem with fuzzy edge weights is discussed and formulated as a fuzzy programming model with some chance constraints. It shows that when all the edge weights are assumed to be independent fuzzy variables with regular credibility distributions, the proposed model can be reformulated into a traditional nonlinear programming according to the equivalent condition of fuzzy α-minimum spanning tree characterized by a set of constraints on non-tree edges and their tree paths. Moreover, if all the fuzzy weights are triangular fuzzy numbers, a linear programming problem can be obtained and solved efficiently with the help of some well developed software packages.