Research progress of E3 ubiquitin ligase regulating biological behavior of human placental trophoblast cells.

E3 ubiquitin ligases are important components of the ubiquitin protease system. This family includes many proteins, which can catalyze the ubiquitination of a variety of protein substrates and promote the degradation of them by the proteasome system. Recent studies have shown that E3 ubiquitin ligase plays a key role in the process of fetal development and placental formation. It affects the biological behavior of placental trophoblast cells, leading to a series of pregnancy complications that threaten mothers and babies greatly. This review focuses on the regulation, target and mechanism of E3 ubiquitin ligase on the biological behavior of human placental trophoblast cells.

Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

High-performing, real-time pose detection and tracking in real-time will enable computers to develop a finer-grained and more natural understanding of human behavior. However, the implementation of real-time human pose estimation remains a challenge. On the one hand, the performance of semantic keypoint tracking in live video footage requires high computational resources and large parameters, which limiting the accuracy of pose estimation. On the other hand, some transformer-based models were proposed recently with outstanding performance and much fewer parameters and FLOPs. However, the self-attention module in the transformer is not computationally friendly, which makes it difficult to apply these excellent models to real-time jobs. To overcome the above problems, we propose a transformer-like model, named ShiftPose, which is regression-based approach. The ShiftPose does not contain any self-attention module. Instead, we replace the self-attention module with a non-parameter operation called the shift operator. Meanwhile, we adapt the bridge-branch connection, instead of a fully-branched connection, such as HRNet, as our multi-resolution integration scheme. Specifically, the bottom half of our model adds the previous output, as well as the output from the top half of our model, corresponding to its resolution. Finally, the simple, yet promising, disentangled representation (SimDR) was used in our study to make the training process more stable. The experimental results on the MPII datasets were 86.4 PCKH, 29.1PCKH@0.1. On the COCO dataset, the results were 72.2 mAP and 91.5 AP50, 255 fps on GPU, with 10.2M parameters, and 1.6 GFLOPs. In addition, we tested our model for single-stage 3D human pose estimation and draw several useful and exploratory conclusions. The above results show good performance, and this paper provides a new method for high-performance, real-time attitude detection and tracking.

Effects of placebo-controlled insulin-sensitizing drugs on hormonal parameters in polycystic ovary syndrome patients: A network meta-analysis.

This network meta-analysis was conducted to compare effects of different placebo-controlled insulin-sensitizing drugs, including metformin, pioglitazone, rosiglitazone, and troglitazone on hormonal parameters in polycystic ovary syndrome (PCOS) patients. We searched PubMed, EMBASE, and Cochrane Library databases from their inception to July 2017. Randomized controlled trials (RCTs) met our inclusion criteria were included. We combined direct and indirect evidences to evaluate weighted mean difference (WMD) value and draw surface under the cumulative ranking curve (SUCRA). Totally 28 eligible RCTs were enrolled. The network meta-analysis results indicated that: Compared with placebo, patients treated with pioglitazone had relatively higher sex-hormone-binding globulin (SHBG) (nmol/L) level (WMD = 6.65, 95%CI = 0.57-12.98), patients treated with metformin had comparatively lower total testosterone (TT) (ng/mL) level (WMD = -0.20, 95%CI = -0.39 to -0.02); Compared with rosiglitazone, patients treated with metformin had relatively higher estradiol (E2 ) (pg/mL) level (WMD = 47.91, 95%CI = 11.44-85.55). However, there were no statistical significance among the placebo-controlled insulin-sensitizing drugs in follicle stimulating hormone (FSH) (IU/L), luteinizing hormone (LH) (IU/L), dehydroepiandrostrone-sulphate (DHEAS) (µg/dL), free testosterone (FT) (pg/mL) and androstenedione (ng/mL). The results of cluster analysis showed that rosiglitazone may be the best drug for PCOS patients regarding to DHEAS, TT, FSH, and LH, metformin may be the best drug for PCOS patients as for E2 , FT, and androstenedione. Rosiglitazone had the best effect on PCOS patients in terms of DHEAS, TT, FSH, and LH, metformin had the best effect on PCOS patients for E2 , FT, and androstenedione.

[Comparison and bias estimation of three methods in determination of glycated hemoglobin A1c].

OBJECTIVE: To evaluate the bias of three different methods in determination of glycated hemoglobin A1c (HbA1c). METHODS: According to Clinical and Laboratory Standards Institute (CLSI)EP9-A3 document, 40 serum specimens among linear range were tested by immunoturbidimetry assay (Roche Tina-quant), capillary electrophoresis (Sebia Minicap FP), high performance liquid chromatography system (HPLC, Trinity Biotech Premier Hb 9210™). RESULTS were analyzed by pairwise comparision. The difference plots and scatter plots were analyzed. The results of outliers were examined by extreme studentized deviate (ESD) method. The best regression model was chosen to fit the regression equation and the biases were calculated at the level of medical decision and judged the comparability according to the half of Tea acceptance standard. RESULTS: Scatter plots and ESD results showed good correlation among test results of 40 samples, no abnormal values were found. Based on proportional difference deviation plots, proportional difference rank order deviation plots, numerical deviation plots and ranked order difference plots, weighted least squares (WLS) (Roche(X) vs sebia(Y1)), ordinary linear regression (OLR) (Roche(X) vs Trinity (Y2)) and Deming (Sebia(Y1) vs Trinity(Y2)) were chosen to fit regression analysis model. The regression equations were Y1 =-0.420 3+ 1.052 7X, Y2 =-0.174 0+ 1.027 0X, Y2=0.423 1+ 0.959 0Y1, respectively. Assuming the HbA1c medical decision points of 10%, 16% to regression equation, the proportional biases were 1.06% and 2.61% at Roche(X) and sebia(Y1) system, 0.96% and 1.60% at Roche(X) and Trinity (Y2) system, 0.13% and -1.47% at Sebia(Y1) and Trinity(Y2) system respectively, which were all lower than the acceptable values. CONCLUSION: The measurement values of HbA1c by capillary electrophoresis (Sebia Minicap FP), HPLC (Trinity Biotech Premier Hb 9210™) and immunoturbidimetry assay (Roche Tina-quant), are comparable.

Micromechanical Modeling of Fiber-Reinforced Composites with Statistically Equivalent Random Fiber Distribution.

Modeling the random fiber distribution of a fiber-reinforced composite is of great importance for studying the progressive failure behavior of the material on the micro scale. In this paper, we develop a new algorithm for generating random representative volume elements (RVEs) with statistical equivalent fiber distribution against the actual material microstructure. The realistic statistical data is utilized as inputs of the new method, which is archived through implementation of the probability equations. Extensive statistical analysis is conducted to examine the capability of the proposed method and to compare it with existing methods. It is found that the proposed method presents a good match with experimental results in all aspects including the nearest neighbor distance, nearest neighbor orientation, Ripley's K function, and the radial distribution function. Finite element analysis is presented to predict the effective elastic properties of a carbon/epoxy composite, to validate the generated random representative volume elements, and to provide insights of the effect of fiber distribution on the elastic properties. The present algorithm is shown to be highly accurate and can be used to generate statistically equivalent RVEs for not only fiber-reinforced composites but also other materials such as foam materials and particle-reinforced composites.

Emotion Analysis of Telephone Complaints from Customer Based on Affective Computing.

Customer complaint has been the important feedback for modern enterprises to improve their product and service quality as well as the customer's loyalty. As one of the commonly used manners in customer complaint, telephone communication carries rich emotional information of speeches, which provides valuable resources for perceiving the customer's satisfaction and studying the complaint handling skills. This paper studies the characteristics of telephone complaint speeches and proposes an analysis method based on affective computing technology, which can recognize the dynamic changes of customer emotions from the conversations between the service staff and the customer. The recognition process includes speaker recognition, emotional feature parameter extraction, and dynamic emotion recognition. Experimental results show that this method is effective and can reach high recognition rates of happy and angry states. It has been successfully applied to the operation quality and service administration in telecom and Internet service company.

Explore Awareness of Information Security: Insights from Cognitive Neuromechanism.

With the rapid development of the internet and information technology, the increasingly diversified portable mobile terminals, online shopping, and social media have facilitated information exchange, social communication, and financial payment for people more and more than ever before. In the meantime, information security and privacy protection have been meeting with new severe challenges. Although we have taken a variety of information security measures in both management and technology, the actual effectiveness depends firstly on people's awareness of information security and the cognition of potential risks. In order to explore the new technology for the objective assessment of people's awareness and cognition on information security, this paper takes the online financial payment as example and conducts an experimental study based on the analysis of electrophysiological signals. Results indicate that left hemisphere and beta rhythms of electroencephalogram (EEG) signal are sensitive to the cognitive degree of risks in the awareness of information security, which may be probably considered as the sign to assess people's cognition of potential risks in online financial payment.

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs.

In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of "C programming language" are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.

Modeling and computing of stock index forecasting based on neural network and Markov chain.

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

Information spread of emergency events: path searching on social networks.

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.