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• The Gated Recursive Unit (GRU) is used for motion prediction of Point of Interest (POI) on a beating heart surface by learning the spatio-temporal correlation of the POI and their auxiliary points (APs) from their past trajectories. • Two deep learning models that exploit only spatial and temporal correlations, respectively, are also developed for comparison with the models that exploit joint spatio-temporal correlations. • In addition to prediction accuracy, our work also focuses on the analysis and validation of the prediction robustness of the models involved. • The proposed model was validated and compared with the state-of-the-art methods based on phantom and in vivo datasets, and the source codes of the proposed models were shared on Github. This work aims to * predict * the * 3D * coordinates of the * point * of * interest * (POI) on * the surface of beating heart in dynamic minimally invasive surgery, which can improve the manoeuvrability of cardiac surgical robots and expand their functions. For accurate and robust POI motion prediction, a deep learning technique, the gated recurrent unit (GRU), is employed to learn the spatio-temporal (ST) correlation of the POI and its auxiliary points (APs) from their past trajectories. For reference, two neural network models that exploit only spatial and temporal correlations, respectively, are also investigated. The prediction accuracy and robustness of the above three models are verified based on the motion datasets of phantom and in vivo hearts collected by da Vinci robots. Source codes and motion data are shared on GitHub (https://github.com/oww-file/ST-GRU). The experimental results show that the proposed ST-GRU model is significantly better than the other two reference models, and it outperforms the state-of-the-art deep learning method on the same datasets. [ABSTRACT FROM AUTHOR]

• Focuses on how to reduce the radiation dose of CT and ensure CT's imaging quality. • Proposes a discriminative sparse transform iterative reconstruction algorithm inspired by the prior image compressed sensing reconstruction and the differential feature representation model. • Data are low-dose 840 × 840 CT images obtained by simulating the fan beam scanning scene from the heart trunk phantom CT data. • The results show that the discriminative sparse transform constraints can effectively introduce a priori image and reconstruct a better image effect and avoid the registration and matching problem of the reconstructed image caused by the difference of the prior image source. The commonly used method to reduce the dose is to reduce the tube current. The number of photons received by the detector decreases, making the CT image obtained by analytical reconstruction full of speckle noise and strip artifacts. It interferes with the diagnosis and analysis of the disease. Therefore, how to reduce the radiation dose of CT and ensuring CT's imaging quality is an important research topic in the field of low-dose CT. This paper proposes a discriminative sparse transform iterative reconstruction algorithm inspired by the previous image compressed sensing reconstruction and the differential feature representation model. The global constraint term is used to constrain the consistency between the projected data to be reconstructed and the real projection data. The prior information constraint term constrains the reconstructed image close to the preceding image. This paper adds low-dose CT images obtained from image post-processing based on learning sparse transform to the prior information. Compared with the global constraints constructed only by learning sparse transform, the discriminative sparse transform constraints can effectively introduce a priori image and reconstruct a better image effect. Also, the improved algorithm's prior image avoids the dependence of the classical prior image compression sensing reconstruction and the differential feature representation model on the prior image and avoids the registration and matching problem of the reconstructed image caused by the difference of the prior image source. [ABSTRACT FROM AUTHOR]

• Minimally invasive surgery has become an essential technic in medical practice and has higher requirements for the operator. • This paper constructs a joint objective function based on the improved SFS and the classical stereo matching method. • The framework's effectiveness is verified by analysis based on the silica-gel-heart and real-heart image datasets. • The results show that the new framework can restore the whole heart surface shape and retain the local details. • The proposed joint reconstruction method has better robustness and reconstruction accuracy. With medical endoscopic equipment development, minimally invasive surgery (MIS) has gradually become an essential technical means in daily medical practice. In recent years, minimally invasive surgery has been widely used because of its small incision and quick recovery. However, at the same time, minimally invasive surgery has put forward higher requirements for the operator. A 3D reconstruction framework combined with stereo vision and Shape from Shading (SFS) was proposed to improve endoscopic imaging accuracy and reduce the difficulty of minimally invasive surgery. This paper constructs a joint objective function based on the improved SFS and the classical stereo matching method. The optimization algorithm of the depth map under the joint objective function is given. Finally, the experimental verification of the joint reconstruction algorithm is carried out. The joint reconstruction framework's effectiveness is verified by qualitative and quantitative comparison and analysis based on the silica-gel-heart model and real-heart image datasets. The experimental results show that the joint reconstruction framework can restore the heart surface shape as a whole and retain the local details. Compared with the classical stereo vision and SFS methods, the proposed joint reconstruction method has better robustness and reconstruction accuracy. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the expression of lipoxin A4, TNFα and IL-1β in maternal peripheral blood, umbilical cord blood and placenta, and to assess their significance in pre-eclampsia. Methods: Ninty pregnant women were divided into three groups: a mild PE group ( n = 30), a severe PE group ( n = 30) and a control group ( n = 30). We measured serum levels of lipoxin A4, TNFα and IL-1β using ELISA. Expression levels of lipoxin receptor (FPR2/ALX) mRNA were compared using quantitative RT-PCR. Results: Mean circulating levels of lipoxin A4, TNFα and IL-1β in the PE groups were significantly increased compared with matched women in the control group. The ratios of lipoxin A4/TNFα and of lipoxin A4/IL-1β in the PE groups were significantly decreased compared with matched women in the control group. No lipoxin A4 was detected in umbilical cord blood. There was a significant increase in FPR2/ALX mRNA expression in placenta obtained from the PE groups. Conclusions: The level of lipoxin A4 in maternal peripheral blood may correlate with the expression of FPR2/ALX in placenta. Lipoxin A4 increased to a lesser degree than either TNFα or IL-1β with progression of PE. This may be one of the reasons why oxidative stress and endothelial dysfunction in severe preeclamptic patients are much more serious that in cases of less severe preeclampsia. Moreover, lipoxin A4 does not have any effect on the fetus through the placenta. We conclude that supplementing with lipoxin A4 to ensure adequate levels may be a novel method for the treatment of pre-eclampsia without any effects on the fetus. [ABSTRACT FROM AUTHOR]

Objective To construct the prokaryotic expression vector for HD-5 and purify the recombinant HD-5 protein then analyze its antifungal activity. Methods The HD-5 gene was cloned by PCR, then was inserted into prokaryotic expression plasmid pQE-30Xa to construct pQE-30Xa/HD-5. After sequencing, pQE-30Xa/HD-5 was transformed into E.coli M15 cells. Its expression was induced by IPTG and confirmed by SDS-PAGE. The recombinant protein was purified through Ni-NTA affinity purification system. The antifungal activity was tested by disk diffusion method. Results HD-5 gene and pQE-30Xa/HD-5 vector were obtained successfully. E.coli M15 strains was used to express HD-5 fusion protein. After purification, the fusion protein was confirmed by Western blot. The disk diffusion test confirmed that the fusion protein can inhibit Candida albicans. Conclusion Expression vector pQE-30Xa/HD-5 was successfully constructed. The HD-5 fusion protein was expressed in E.coli successfully, which showed a certain degree of antifungal activity. [ABSTRACT FROM AUTHOR]

• The key to endoscopic image mosaics success is the accuracy of image registration and fusion. • This paper uses the Gaussian Pyramid to improve the simple ORB-oriented algorithm. • The improved method reduces the calculation and storage requirements and improves the image registration efficiency. • The pyramid ORB algorithm solves the problem that the ORB does not have scale invariance. • The pyramid ORB is more suitable for minimally invasive surgery endoscopic image mosaic. The primary method of endoscopic image mosaics seamlessly mosaics several continuous and overlapping endoscopic images to have a broader vision and clearer images. The key to its success is the accuracy of image registration and fusion. This paper uses the Gaussian Pyramid to improve the simple ORB-oriented algorithm. The experimental results show that the pyramid sphere algorithm has invariability, good robustness in scale change and rotation change, high registration accuracy, and the stitching speed is about 10 times that of SIFT. The improved method has more efficient feature extraction and expression methods, reduces the calculation and storage requirements, and improves the image registration efficiency. The pyramid ORB algorithm solves the problem that the ORB does not have scale invariance. The pyramid ORB is more suitable for minimally invasive surgery endoscopic image mosaic through theoretical analysis and experimental verification. The algorithm has good robustness and real-time performance. [ABSTRACT FROM AUTHOR]

Dam construction brings both benefits and harms to the ecological environment. This paper focuses on the impact of the construction of mega-dams on regional climate. Based on the national precipitation and temperature data from 1990 to 2012 from more than 2400 national stations of the National Meteorological Information Center, this paper analyzes Xiaolangdi Dam's influence on the Yellow River's climate and the Three Gorges Dam of the Yangtze River with geostatistics knowledge. The following results are obtained. (1) According to the analysis of the climate time and space distribution in the Yellow River Basin and the Yangtze River Basin, the dam's construction caused a drop in temperature in the reservoir area, which decreased by about 0.12 °C. On the other hand, the dam's construction also caused the temperature in the downstream area to rise by about 0.6 °C. (2) According to the analysis of regional climate correlation, the rise of the dam's water level will cause the temperature of the reservoir area to drop to a certain extent. The correlation between the temperature in the Three Gorges and the water level changes in the Three Gorges reaches −0.578. (3) According to regional climate statistics analysis, the dam's construction has reduced the temperature difference in the reservoir area. • Based on the national precipitation and temperature data from 1990 to 2012 from more than 2400 national stations of the National Meteorological Information Center. • Focuses on the impact of the construction of mega-dams, Xiaolangdi Dam and Three Gorges Dam, and analyzes their influence on the Yellow River's climate. • According to the analysis, dams' construction leads to decreased temperature in the reservoir area. The construction of dams also leads to an increase in temperature in downstream areas. • According to the correlation analysis of regional climate, the increase of dam water level will lead to a decrease in temperature within the reservoir area. • According to the regional climate's statistical analysis, the Dam's construction reduces the reservoir area's temperature difference. [ABSTRACT FROM AUTHOR]

Smog pollution is becoming a significant problem for people worldwide, becoming an essential threat to the global environment. Many studies on haze already exist, which still need to continue in-depth research to better deal with haze problems. Due to its unique geographical environment, Sichuan has become one of the areas with severe smog pollution. Therefore, the research and prediction of smog pollution in Sichuan has become an urgent need. This paper proposes a deep learning technology based on a Deep Belief-Back Propagation neural network. It makes in-depth prediction research by using the air pollution data of PM2.5, PM10, O 3 , CO NO 2, and SO 2 in Sichuan smog to provide a decision-making basis for predicting and preventing smog polluted weather. According to the prediction results of the model, the concentrations of PM2.5 and PM10 in Chengdu were predicted. The analysis shows that the larger the number of hidden layers in the belief network, the higher the prediction accuracy. Under the same network, the prediction accuracy of PM2.5 is significantly higher than that of PM10. Compared with the traditional Back Propagation neural network, the prediction effect of the Deep Belief-Back Propagation neural network is better. • Based on the air pollution data of PM2.5, PM10, O3, Co, NO2 and SO2 in Sichuan smog, this paper makes an prediction research for predicting and preventing smog polluted weather. • Aiming at the problem of PM2.5 and PM10 concentration prediction, this paper constructs a prediction model based on Deep Belief -Back Propagation neural network. • The results show that the prediction effect of Deep Belief - BP neural network is better than BP neural network, and can quickly extract the concentration characteristics in the first 24 times. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence and frequency of haze have constantly been increasing, bringing severe threats to people's daily lives. To this end, this paper discusses the spatial and temporal distribution characteristics of severe haze in China, analyzes the interaction between haze pollution and the influence of economy and energy structure on haze in 31 provinces of China. It provides references for the treatment of haze weather and the prevention and control of air pollution in China. This paper mainly adopts the spatial autocorrelation method. The data processed mainly includes API (Air Pollution Index) and meteorological station data. Combined with the statistical yearbook data, this paper conducts multi-aspect research and exploration. By using statistical methods to study the haze distribution in China, we found that the haze and PM 2.5 concentrations were mainly distributed in Beijing-Tianjin-Hebei, Shandong Province, the northern northwest, southeastern Sichuan, and Chongqing. Haze distribution has obvious seasonality, more in winter and less in summer. There are also regional differences in the concentration distribution of urban pollutants. The concentration of SO 2 and absorbable particles are relatively high in northern cities. In contrast, that of southern cities is relatively low and changes with seasonal changes. • Focuses on the spatial and temporal distribution characteristics of severe haze in China based on pollution-related data from the Ministry of Environmental Protection. • Analyzes the interaction between haze and the economy and energy structure in 31 provinces of China, and provides reference for the treatment and the prevention and control of air pollution in China. • The haze were concentrated in Beijing-Tianjin-Hebei region, Shandong Province, the northern northwest region, southeastern Sichuan province, and Chongqing. • According to the spatial autocorrelation, haze distribution has obvious seasonality, more in winter and less in summer. • The concentration of SO 2 and absorbable particles is relatively high in northern cities, while that of southern cities is relatively low, and changes with seasonal changes [ABSTRACT FROM AUTHOR]

• A knowledge base graph embedding module is constructed to extend the generality of the visual question answering model based on knowledge base in this paper. • This paper constructs two experimental knowledge bases: DBV and DBA through a series of knowledge base preprocessing methods. • The DBV knowledge base constructed in this paper has strong entity correspondence, and its knowledge base embedding effect is very good. In this paper, a knowledge base graph embedding module is constructed to extend the versatility of knowledge-based VQA (Visual Question Answering) models. The knowledge base graph embedding module constructed in this paper extracts core entities from images and text, and maps them as knowledge base entities, then extracts the sub-graphs closely related to the core entities, and converts the sub-graphs into low-dimensional vectors to realize sub-graph embedding. In order to achieve good subgraph embedding, we first extracted two experimental knowledge bases with rich semantics from DBpedia: DBV and DBA. Based on these two knowledge bases, this paper selects several excellent models in knowledge base embedding as test models, including SE (structured embedding),SME(semantic matching energy function), and TransE model to produce link prediction. The results show that there is a clear correspondence between the entities of the DBV, which can achieve excellent node embedding. And the TransE model can achieve a good knowledge base embedding, so we built the knowledge base graph embedding module based on TransE. And then we construct a VQA model (KBSN) based on the knowledge base graph embedding. Experimental results on VQA2.0 and KB-VQA data sets prove that the knowledge base graph embedding module improves the accuracy. [ABSTRACT FROM AUTHOR]

Objective To explore the clinical value of examining HPV E6/E7 mRNA level in assessing cervical lesions infected with high-risk human papillomavirus (HR-HPV). Methods The cervical epithelial cells were collected from 265 patients with HR-HPV infection, including 100 cases of neoplasia free/inflammation group (control group), 88 cases of cervical intraepithelial neoplasia (CIN) I, 33 cases of CINII, 28 cases of CIN III and 16 cases of cervical carcinoma and the transcription of HPV E6/E7 mRNA level was examined using branched DNA (b - DNA) technology. Results The positive rate HPV E6/E7 mRNA were higher in CIN II (81.82%), CIN III (89.29%) and cervical cancer group (100.00%) than tthat in control group (20.00%) and CINI(35.23%) with significant difference, and there were no significant differences between other groups ; The positive rate and transcription level of HPV E6/E7 mRNA in HSIL (high grade squamous intraepithelial lesion)and cancer group were significantly higher than normal, ASC (atypical squamous cell carcinoma) and LSIL (low grade squamous intraepithelial lesion) group (P < 0.05). Conclusion The transcription level of HPV E6/E7 mRNA may reflect the activity of the virus and the progression of disease, and could be use as an effective indicator to screen high grade cervical pathological changes and a complementary method of cervical lesion screening. [ABSTRACT FROM AUTHOR]