Despite evidence that genetic factors contribute to the duration of gestation and the risk of preterm birth, robust associations with genetic variants have not been identified. We used large data ...sets that included the gestational duration to determine possible genetic associations.
We performed a genomewide association study in a discovery set of samples obtained from 43,568 women of European ancestry using gestational duration as a continuous trait and term or preterm (<37 weeks) birth as a dichotomous outcome. We used samples from three Nordic data sets (involving a total of 8643 women) to test for replication of genomic loci that had significant genomewide association (P<5.0×10
) or an association with suggestive significance (P<1.0×10
) in the discovery set.
In the discovery and replication data sets, four loci (EBF1, EEFSEC, AGTR2, and WNT4) were significantly associated with gestational duration. Functional analysis showed that an implicated variant in WNT4 alters the binding of the estrogen receptor. The association between variants in ADCY5 and RAP2C and gestational duration had suggestive significance in the discovery set and significant evidence of association in the replication sets; these variants also showed genomewide significance in a joint analysis. Common variants in EBF1, EEFSEC, and AGTR2 showed association with preterm birth with genomewide significance. An analysis of mother-infant dyads suggested that these variants act at the level of the maternal genome.
In this genomewide association study, we found that variants at the EBF1, EEFSEC, AGTR2, WNT4, ADCY5, and RAP2C loci were associated with gestational duration and variants at the EBF1, EEFSEC, and AGTR2 loci with preterm birth. Previously established roles of these genes in uterine development, maternal nutrition, and vascular control support their mechanistic involvement. (Funded by the March of Dimes and others.).
This paper presents a Lyapunov–Krasovskii methodology for studying the input-to-state stability and the integral input-to-state stability of nonlinear time-delay systems. An integral input-state ...estimate which takes into account non-zero initial conditions is also proposed.
Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of ...FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south–north) × 6 km (east–west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha−1 to 8841.3 kg ha−1, with an average of 1786.7 kg ha−1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.
This paper reports probably the first systematic experimental investigation of the influences of lateral inertia confinement, end friction confinement and aggregates in high-speed impact tests on ...dynamic compressive properties of concrete. The influences of the concerned factors on the dynamic increase factor (DIF) are discussed. The proposed DIF relations in Hao and Hao 36 are verified by the experimental data in this study. The results confirm the observations made before based on numerical simulations that lateral inertia confinement effect is dependent on the specimen diameter and strain rate. The influences of aggregate size and volume to DIF are considerable, and aggregates cannot be neglected in experimental or numerical studies. It also confirms that the end friction confinement effect is sensitive to the specimen geometry and strain rate. The equation proposed in Hao et al. 40 based on numerical simulations satisfactorily removes the friction confinement effect on the DIF of concrete material strength obtained in impact test.
•A model-driven learning method is proposed to trace fluctuations dynamically.•The fluctuation forecasting under uncertainties is made possible using the HMM.•Case studies of different scenarios are ...presented to show the validity of the HMM.•Two forecasting formulas are proposed to cope with two scenarios separately.
Waste generation forecasting is a complex process that is found to be influenced by some latent influencing parameters and their uncertainties, such as economic growth, demography, individual behaviors, activities and events, and management policies. These hidden features play an important role in forecasting the fluctuations of waste generation. We therefore focus on revealing the trend of waste generation in megacities which face significant influences of social and economic changes to achieve urban sustainable development. To dynamically trace fluctuations caused by these uncertainties, we propose a probability model-driven statistical learning approach which hybridizes a wavelet de-noising, a Gaussian mixture model, and a hidden Markov model. First, to gain the actual underlying trend, wavelet de-noising is used to eliminate the noise of data. Next, the Expectation–Maximization and the Viterbi algorithms are employed for learning parameters and discerning the most probable sequence of hidden states, respectively. Subsequently, the state transition matrix is updated by fractional predictable changes of influencing parameters to perform non-periodic fluctuation problem forecasting, and the forward algorithm is utilized to search the most similar data pattern for the current pattern from historical data in order to forecast the future trend of the periodic fluctuation problem. Finally, we apply the approaches into two kinds of case studies that test both a small dataset and a large dataset. How uncertainty factors influence forecasted results is analyzed in the subsection of results and discussion. The computational results demonstrate that the proposed approaches are effective in solving the municipal waste generation forecasting problem.
Obesity among pregnant women may adversely affect both maternal iron status throughout pregnancy and placental transfer of iron. The objective of this study was to determine the association of ...maternal body mass index (BMI) with (1) maternal iron status and inflammation in mid and late pregnancy, (2) the change in maternal iron status throughout pregnancy and (3) neonatal iron status.
We examined longitudinal data from 1613 participants in a pregnancy iron supplementation trial in rural China. Women with uncomplicated singleton pregnancies were enrolled in the early second trimester of pregnancy and followed through parturition. Maternal blood samples obtained at enrollment and in the third trimester and cord blood samples were analyzed for a range of hematological and iron biomarkers.
There was a negative association between maternal BMI and iron status at enrollment (transferrin receptor (sTfR): r=0.20, P<0.001; body iron (BI): r=-0.05; P=0.03). This association was markedly stronger among obese women. Maternal BMI was positively associated with maternal inflammation (C-reactive protein: r=0.33, P<0.001). In multiple linear regression models, maternal BMI was negatively associated with neonatal iron status (cord serum ferritin: -0.01, P=0.008; BI: -0.06, P=0.006) and associated with a lower decrease in iron status throughout pregnancy (sTfR: -4.6, P<0.001; BI: 1.1, P=0.004).
Maternal obesity during pregnancy may adversely affect both maternal and neonatal iron status, potentially through inflammatory pathways.
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