Background The potential effects of pre-pregnancy body mass (BMI) and gestational weight gain (GWG) on pregnancy outcomes remain unclear. Thus, we investigated socio-demographic characteristics that affect pre-pregnancy BMIs and GWG and the effects of pre-pregnancy BMI and GWG on Chinese maternal and infant complications. Methods 3172 women were enrolled in the Chinese Pregnant Women Cohort Study-Peking Union Medical College from July 25, 2017 to July 24, 2018, whose babies were delivered before December 31, 2018. Regression analysis was employed to evaluate the socio-demographic characteristics affecting pre-pregnancy BMI and GWG values and their effects on adverse maternal and infant complications. Results Multivariate logistic regression analysis revealed that age groups < 20 years (OR: 1.97), 25–30 years (OR: 1.66), 30–35 years (OR: 2.24), 35–40 years (OR: 3.90) and ≥ 40 years (OR: 3.33) as well as elementary school or education below (OR: 3.53), middle school (OR: 1.53), high school (OR: 1.40), and living in the north (OR: 1.37) were risk factors in maintaining a normal pre-pregnancy BMI. An age range of 30–35 years (OR: 0.76), living in the north (OR: 1.32) and race of ethnic minorities (OR: 1.51) were factors affecting GWG. Overweight (OR: 2.01) and inadequate GWG (OR: 1.60) were risk factors for gestational diabetes mellitus (GDM). Overweight (OR: 2.80) and obesity (OR: 5.42) were risk factors for gestational hypertension (GHp). Overweight (OR: 1.92), obesity (OR: 2.48) and excessive GWG (OR: 1.95) were risk factors for macrosomia. Overweight and excessive GWG were risk factors for a large gestational age (LGA) and inadequate GWG was a risk factor for low birth weights. Conclusions Overweight and obesity before pregnancy and an excessive GWG are associated with a greater risk of developing GDM, GHp, macrosomia and LGA. The control of body weight before and during the course of pregnancy is recommended to decrease adverse pregnancy outcomes, especially in pregnant women aged < 20 or > 25 years old educated below university and college levels, for ethnic minorities and those women who live in the north of China. Trial registration Registered at Clinical Trials (NCT03403543), September 29, 2017.

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Aim: To investigate the association of dietary patterns with gestational depression and sleep disturbance. Methods: Women in early pregnancy were recruited from the Chinese Pregnant Women Cohort Study (CPWCS) through July 25th, 2017 to November 26th, 2018, and eventually 7615 participants were included in this study. The qualitative food frequency questionnaire (Q-FFQ), Edinburgh Postnatal Depression Scale (EPDS), and the Pittsburgh Sleep Quality Index (PSQI) were used to assess dietary, depression and sleep quality during pregnancy, respectively. Dietary patterns were derived by factor analysis. Logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (95% CI) of each outcome according to quartiles of each dietary pattern. Results: Five dietary patterns were identified. Participants with the highest quartile in plant-based pattern had a significantly lower likelihood of mental problems (OR: 95% CI for depression: 0.66, 0.55-0.79; sleep disturbance: 0.80, 0.68-0.93); Similar results were observed in vitamin-rich pattern (OR: 95% CI for depression: 0.46, 0.38-0.55; sleep disturbance: 0.76, 0.65-0.89); However, contrary results were found in high-fat pattern (OR: 95% CI for depression: 2.15, 1.25-1.85; sleep disturbance: 1.43, 1.22-1.67); In animal protein-rich pattern, participants with the highest quartile had a decreased likelihood of depression (OR: 0.80, 95% CI: 0.67-0.96). As for bean products pattern, participants with the highest quartile had an increased risk of depression (OR: 1.28, 95% CI:1.06-1.53). Interactions of dietary patterns and lifestyles on mental disorders were observed. Conclusion: Dietary patterns were associated with gestational depression and sleep disturbance. Relevant departments and maternal and child health personnel should conduct health education for pregnant women and guide them to eat properly.

In ad hoc networks, CSMA/CA is a widely used MAC layer protocol. There has been considerable work on the performance evaluation of this protocol. But most work is confined to saturation performance of single-hop ad hoc networks. In this paper, we employ a linear feedback model to evaluate the performance for CSMA/CA according to the Poisson distributed traffic in both single-hop and multi-hop ad hoc networks. To the best of our knowledge, this is the first attempt to analytically evaluate the performance for CSMA/CA protocol under a general assumption about the traffic. This paper also gives analytical results, showing the impact of RTS/CTS. Although RTS/CTS do add the overhead to the system, they become essential when either the hidden terminal problem is dominant, or the traffic is heavy, or the packet length is very large. This paper also shows that the performance degrades dramatically in multi-hop ad hoc networks when the number of competing nodes increases, which implies that the scalability is still a major problem in ad hoc networks. To validate our analytical results, we have done extensive simulations, and it is observed that the simulation results match the analytical results very well.