This study aimed to investigate the intake of dairy products during pregnancy in women with gestational diabetes mellitus (GDM), and its impacts on neonatal birth weight and pregnancy outcomes. A total of 386 women with GDM during the second trimester pregnancy participated in this prospective cohort study. We evaluated dairy products intake through Food Frequency Questionnaire. Pregnancy outcomes were obtained from the delivery data. Participants were divided into insufficient and sufficient intake of milk and dairy products group (<300 g/day and ≥300 g/day, respectively). The average intake of dairy products during the second trimester pregnancy in women with GDM was 317.8±179.5 g/day, and the total energy intake was 1635.4±708.7 kcal/day. However, 76.68% of them did not meet the recommended total energy intake of women with GDM. After adjusting for confounding factors, women with GDM who consumed ≥300 g/day of dairy products had an average reduction in birth weight of 93.1 g compared to women who consumed <300 g/day of dairy products (95% CI: -171.343, -14.927). Women with GDM in sufficient intake group was also associated with lower risk of macrosomia (95% CI: 0.043, 0.695) and cesarean section (95% CI: 0.387, 0.933), and not related to low birth weight infant (95% CI: 0.617, 14.502) and preterm birth (95% CI: 0.186, 1.510) when compared with participants in insufficient intake group. Under the premise of insufficient total energy intake, the intake of dairy products during the second trimester pregnancy in women with GDM might be related to the decrease of neonatal birth weight.

It is a challenging task to discover information from a large amount of data in an open domain.1 In this paper, an event network framework is proposed to address this challenge. It is in fact an empirical construct for exploring open information, composed of three steps: document event detection, event network construction and event network analysis. First, documents are clustered into document events for reducing the impact of noisy and heterogeneous resources. Secondly, linguistic units (e.g., named entities or entity relations) are extracted from each document event and combined into an event network, which enables content-oriented retrieval. Then, in the final step, techniques such as social network or complex network can be applied to analyze the event network for exploring open information. In the implementation section, we provide examples of exploring open information via event network.

In this paper, a Legendre-collocation spectral method is developed for the second order Volterra integro-differential equation with pantograph delay. We provide a rigorous error analysis for the proposed method. The spectral rate of convergence for the proposed method is established in both L2-norm and L∞-norm.

In this paper, we investigate the error estimates and superconvergence property of mixed finite element methods for elliptic optimal control problems. The state and co-state are approximated by the lowest order Raviart-Thomas mixed finite element spaces and the control variable is approximated by piecewise constant functions. We derive L2 and L∞-error estimates for the control variable. Moreover, using a recovery operator, we also derive some superconvergence results for the control variable. Finally, a numerical example is given to demonstrate the theoretical results.

A Raviart-Thomas mixed finite element discretization for general bilinear optimal control problems is discussed. The state and co-state are approximated by lowest order Raviart-Thomas mixed finite element spaces, and the control is discretized by piecewise constant functions. A posteriori error estimates are derived for both the coupled state and the control solutions, and the error estimators can be used to construct more efficient adaptive finite element approximations for bilinear optimal control problems. An adaptive algorithm to guide the mesh refinement is also provided. Finally, we present a numerical example to demonstrate our theoretical results.

The reliability and validity of the Chinese version of the 17-item Hamilton Depression Rating Scale (CHDS) was assessed. Interrater reliability was excellent, the item total-score correlations were good, and the internal reliability was satisfactory. The concurrent validity was tested by correlating the CHDS score with the Global Assessment Scale score; the strong negative correlation found indicated that the CHDS reflects the overall level of disability. Five distinct factors were generated by principle-component analysis; these factors account for 52.4% of the total variance. Rigorous evaluation of the numerous translated scales being used in clinical and research settings of non-Western countries is important.