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There is a currently a lack of large-area plasma sterilization devices that can intelligently identify the shape of a wound for automatic sterilization. For this reason, in this work, a plasma sterilization device with wound-edge recognition was developed using a field-programmable gate array (FPGA) and a high-performance image-processing platform to realize intelligent and precise sterilization of wounds. SOLIDWORKS was used to design the mechanical structure of the device, and it was manufactured using 3D printing. The device used an improvement of the traditional Sobel detection algorithm, which extends the detection of edges in only the x and y directions to eight directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°), completing the wound-edge detection by adaptive thresholding. The device can be controlled according to different shapes of sterilization area to adjust the positioning of a single plasma-jet tube in the horizontal plane for two-dimensional movement; the distance between the plasma-jet tube and the surface of the object to be sterilized can be also adjusted in the vertical direction. In this way, motors are used to move the plasma jet and achieve automatic, efficient, and accurate plasma sterilization. It was found that a good sterilization effect could be achieved at both the culture-medium level and the biological-tissue level. The ideal sterilization parameters at the culture-medium level were a speed of 2 mm/s and a flow rate of 0.6 slm, while at the biological-tissue level, these values were 1 mm/s and 0.6 slm, respectively.

The self-aligning capability of an exoskeleton is important to ensure wearing comfort, and the delicate motion ability of the exoskeleton is essential for motion assistance. Designing a self-aligning exoskeleton that offers improved wearing comfort and enhanced motion-assistance functions remains a challenge. This paper proposes a novel spatial self-aligning mechanism for a knee exoskeleton to enable simultaneous assistance in the flexion and extension (FE) of the knee joint and the internal and external rotation (IER) of the hip joint. Additionally, considering the misalignment of the human-robot joint axes, a kinematic model of the knee exoskeleton is established and analyzed to demonstrate the kinematic compatibility of the exoskeleton. Furthermore, a global torque manipulability (GTM) index is proposed to evaluate the effects of dimensional parameters on the exoskeleton’s performance, and then the knee exoskeleton is optimized according to the GTM index. Finally, experiments are conducted to validate the performance of the proposed exoskeleton. The experimental results show that during knee FE and hip IER, the proposed exoskeleton exhibits lower interaction forces and torques than existing exoskeletons.

Objective To systematically evaluate the effect of applied muscle tension (AMT) exercises on reducing vasovagal reactions(VVRs) among blood donors by meta-analysis. Methods Nine related databases including MEDLINE, Web of Science and CINAHL were searched using "applied muscle tension" and "vasovagal reactions" as keywords to collect clinical studies on the effects of AMT exercise on VVRs during blood donation. Two researchers independently screened, evaluated the quality and extracted data from the retrieved literature according to the inclusion and exclusion criteria. RevMan 5.4 was used to conduct meta-analysis on the extracted data. Results A total of 7 articles were finally included, 5 in English and 2 in Chinese, covering 6 808 blood donors. The experimental group performed AMT during blood donation, while the control group did not. Compared with the control group, the difference in reducing the incidence of VVRs [ RR = 0.60, 95%CI (0.42, 0.87), P

Modified polyimide (MPI) flexible printed circuits (FPCs) are used as chip carrier boards. The quality of the FPC directly affects the reliability of the integrated circuit. Furthermore, micro-holes are critical components of FPCs. In this study, an ultraviolet (UV) femtosecond laser is used to drill micro-holes in double-layer flexible circuit boards with MPI as the substrate. The morphology of the micro-hole wall in the copper foil and MPI layer is observed, and the effects of the laser processing parameters on the diameter and depth of the micro-holes are analyzed. The drilling process and mechanism of micro-holes obtained using a UV femtosecond laser in MPI FPCs are discussed. The results show that the morphology of femtosecond laser-machined copper is closely related to the laser energy, and a periodic structure is observed during the machining process. Copper, MPI, and copper oxides are the most common molten deposits in micro-holes during drilling. The depth of the micro-holes increases with an increase in the energy of a single pulse, scanning time, and scanning overlap rate of the laser beam. However, the diameter exhibits no discernible alteration. The material removal rate increased significantly when laser processing was applied to the MPI resin layer.

Filling mining is an important way to realize the green development of mines. High-water filling materials have been widely used because of their short setting time. In this material, 40% of the raw material is natural anhydrite, and the main component is anhydrous calcium sulfate (CaSO4). Fluorogypsum is an industrial solid waste produced in the production of hydrofluoric acid. Its main composition is type II CaSO4. In order to explore the influence of fluorogypsum (F1, lime content of 3%; F2, lime content of 5%) on the performance of high water filling materials, the influence of fluorogypsum content on the setting time and 1, 7 and 28 d compressive strength of high-water filling materials at different experimental water temperatures and water binder ratio of 3∶1 was studied. At the same time, the hydration mechanism was studied by XRD, SEM, and TG-DTA. The results show that the setting time of high-water filling material increases gradually with the increase of fluorogypsum content at different experimental water temperatures. With the increase of F1 content, the low content has little effect on the compressive strength of high-water filling materials at different ages. When its content is higher than 20%, the compressive strength of high-water filling materials at 1, 7, and 28 days decrease gradually; with the increase of F2 content, the compressive strength of the high water filling material increases gradually at 1, 7, and 28 d. At different ages, compared with the blank group, the F2 content is 80%, and the maximum growth rate of its compressive strength is 29.7%. Microscopic analysis shows that the hydration products of high-water filling materials mainly include AFt, Al (OH) 3 (gel), and CaSO4 · 2H2O. After hydration for 1 d, the hydration rate of the F2 group (80% of F2) is fast and the amount of AFt is the largest, while the hydration rate of the blank group is the smallest and the amount of AFt is the smallest; After 28 d of hydration, the amount of aft produced in the three groups was the same. At the same time, based on the hydration kinetics of high-water filling material, the influence of fluorogypsum on the hydration process of high-water filling material is analyzed. It is concluded that fluorogypsum can replace natural anhydrite in high-water filling material systems and be applied to high-water filling material systems.

Abstract The edge-cloud computing architecture has been introduced to industrial circles to ensure the time constraints for industrial computing tasks. Besides the central cloud, various numbers of edge servers (ESes) are deployed in a distributed manner. In the meantime, most large factories currently use auto guided vehicles (AGVs). They usually travel along a given route and can help offload tasks to ESes. An ES maybe accessed by multiple AGVs, thus incurring offloading and processing delays due to resource competition. In this paper, we investigate the offloading scheduling issue for cyclical tasks and put forth the Multi-AGV Cyclical Offloading Optimization (MCOO) algorithm to reduce conflicts. The solution divides the offloading optimization problem into two parts. Firstly, the load balancing algorithm and greedy algorithm are utilized to find the optimal allocation of tasks for a single AGV under limited conditions. Then, multiple AGVs are asynchronously trained by applying the Reinforcement Learning-based A3C algorithm to optimize the offloading scheme. The simulation results show that the MCOO algorithm improves the global offloading performance both in task volume and adaptability compared with the baseline algorithms.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Introduction The relationship between tea consumption and glucose metabolism remains controversial. This study investigated the associations of tea consumption with impaired glucose regulation, insulin secretion and sensitivity in Shanghai High-risk Diabetic Screen project.Research design and methods A total of 2337 Chinese subjects were enrolled in the study from 2014 to 2019. Each participant conducted a 75 g oral glucose tolerance test (OGTT) with five-point glucose and insulin level examined. They also completed a nurse-administered standard questionnaire including tea, coffee, and alcohol consumption, smoking habit, physical activity, education, sleep quality, etc.Results The result showed that tea consumption was positively associated with plasma glucose levels during OGTT after adjusting for confounder (Ps 10 years) had an increased risk of glucose intolerance (all p

Post-translational histone modifications play important roles in regulating chromatin structure and transcriptional regulation. Histone 3 lysine 4 trimethylation (H3K4me3) is a prominent histone modification mainly associated with gene activation. Here we showed that a histone demethylase, JMJ15, belonging to KDM5/JARID group, is involved in salt stress response in Arabidopsis thaliana. Jmj15 loss-of-function mutants displayed increased sensitivity to salt stress. Moreover, knockout of JMJ15 impaired the salt responsive gene expression program and affected H3K4me3 levels of many stress-related genes under salt-stressed condition. Importantly, we demonstrated that JMJ15 regulated the expression level of two WRKY transcription factors, WRKY46 and WRKY70, which were negatively involved in abiotic stress tolerance. Furthermore, JMJ15 directly bound to and demethylated H3K4me3 mark in the promoter and coding regions of WRKY46 and WRKY70, thereby repressing these two WRKY gene expression under salt stress. Overall, our study revealed a novel molecular function of the histone demethylase JMJ15 under salt stress in plants.

Abstract Background Complement factor H (CFH) has been found to be associated with insulin resistance. This study assessed the correlation between CFH and other clinical parameters, and determined whether CFH played a role in gestational diabetes mellitus (GDM) and adverse pregnancy outcomes. Methods A total of 397 pregnant women were included for analysis in this nested case-control study. Clinical parameters and serum were collected within the 11-17th gestational age at the first prenatal visit. At 24–28 weeks of gestation, a 75 g oral glucose tolerance test was performed and subjects were divided into a GDM (n = 80) and a non-GDM control group (n = 317). The delivery data were also followed. The serum CFH level was assayed by ELISA. Results CFH was higher in GDM than in non-GDM controls (280.02 [58.60] vs. 264.20 [68.77]; P = 0.014). CFH level was moderately associated with pre-pregnancy body mass index (BMI), BMI and total triglycerides (TG), and slightly associated with gestational age, low density lipoprotein cholesterol (LDL-C), total cholesterol (TC) in GDM and non-GDM (all P

Measuring the space area of obstacles is one of the important problems in obstacle localizing fields. Most of the existing research works on the localization of obstacles focus on where the obstacles are, and few of them measure both the positions and the areas of the obstacles. In this paper, we propose a Minimum convex bounding Polygon localizing algorithm based on Visible light Tracking (MPVT) in order to rapidly and accurately locate the position and area of a 2D obstacle in the environment of sparsely-deployed sensors. MPVT first determines the initial localization light by Visible Light Tracing method (VLT). Second, it searches for the first side of the Minimum Convex Bounding Polygon (MCBP) of the obstacle. Third, MPVT calculates the subsequent other sides and the vertexes of MCBP until the next side coincides with the first side. In order to evaluate the approximation degree between the actual values and the localization values in terms of areas, positions and shapes, we propose two performance evaluation indexes, i.e., the area ratio and the ratio of equivalent radius. We conducted experiments on the influence of obstacle orientation and sparseness of sensor deployment, the accuracy comparison with the existing methods, and the time complexity. Experiment results show that MPVT can accurately locate the position and area of the obstacle in the environment of sparsely-deployed sensors with low time overhead, and is suitable for low-cost obstacle localization applications.

In smart cities, intelligent learning environment is an important application scenario, and class attendance checking is an important measure to urge students to attend on time and ensure the quality of learning. Aiming at the existing problems in class attendance checking, such as low efficiency and easy to cheat, this paper proposes a student attendance management method named AMMoC (Attendance Management Method based on Crowdsensing). AMMoC includes two phases, i.e., the initialization phase and the authentication phase. In the initialization phase, a teacher sends an attendance checking request to the server. After receiving the request, the server sends a request to tell students to submit their location information, and then forms the student location map once the server receives all the response from students. In the authentication phase, the server verifies the truth of the location information by sending requests to several students to count the number of students. The authentication phase includes two modules, i.e., the task assignment module and the attendance verification module. In the task assignment module, AMMoC first finds the optimized sequence of subregions and verifiers by using the Monte Carlo algorithm, and then requires the verifiers to count the number of students in the subregion. Finally, the statistics results will be verified in the attendance verification module. Experiment comparisons and analyses show that AMMoC has the advantages of good anti-cheating performance, fast speed, and little disturbance to class, and is suitable for attendance checking applications in classroom environment.

Chronic wounds are a major health problem with increasing global prevalence, which endangers the physical and mental health of those affected and is a heavy burden to healthcare providers. Artemisia argyi extract (AE) has excellent antibacterial and anti-inflammatory properties. In this research, we developed AE loaded composite hydrogel scaffold based on methacrylate gelatin (GelMA)/methacrylate hyaluronic acid (HAMA) and mesoporous silica nanoparticle (MSN) as sustained-release drug carrier vehicles for the treatment of chronic wounds. The presented GelMA/1%HAMA hydrogel possessed stable rheological properties, suitable mechanical properties, appropriate biodegradability, swelling, sustained-release AE capacity. In vitro antibacterial and cell experiments showed that the GelMA/HAMA/MSN@AE hydrogel had excellent antibacterial activity and biocompatibility and induced macrophages to differentiate into M2 phenotype. In vivo wound healing of rat full-thickness cutaneous wounds further demonstrated that the prepared GelMA/HAMA/MSN@AE hydrogel could significantly promote chronic wound healing by upregulating the expression of IL-4, TGF-β1, CD31, and α-SMA but downregulating the expression of TNF-α and IFN-γ and promoting M1-M2 macrophages polarization. Altogether, we believe that the GelMA/HAMA/MSN@AE hydrogel will have wide application prospects in healing chronic wounds.

Abstract Background Mesenchymal stem cell (MSC)-derived exosomes emerge as promising candidates for treating delayed wound healing in diabetes due to the promotion of angiogenesis. Preconditioned MSC with chemical or biological factors could possibly enhance the biological activities of MSC-derived exosomes. The purpose of this research focused on whether exosomes derived from the bone marrow MSC (BMSC) pretreated with atorvastatin (ATV), could exhibit better pro-angiogenic ability in diabetic wound healing or not and its underlying molecular mechanism. Methods We isolated exosomes from non-pretreated BMSC (Exos) and ATV pretreated BMSC (ATV-Exos) and evaluated their characterization by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA) and Western blotting. In vivo, we made full-thickness skin defects in streptozotocin (STZ)-induced diabetic rats and the defects received multiple-point injection with PBS, Exos, or ATV-Exos. Two weeks later, histological analysis was conducted to evaluate the impact of different treatments on wound healing and the neovascularization was measured by micro-CT. In vitro, cell proliferation, migration, tube formation, and vascular endothelial growth factor (VEGF) secretion were measured in human umbilical vein endothelial cells (HUVEC). The role of miRNAs and AKT/eNOS signaling pathway in the promoted angiogenesis of ATV-Exos were assessed with their inhibitors. Results No significant difference in morphology, structure, and concentration was observed between ATV-Exos and Exos. In STZ-induced diabetic rats, ATV-Exos exhibited excellent abilities in facilitating the wound regeneration by promoting the formation of blood vessels compared with Exos without influencing liver and kidney function. Meanwhile, ATV-Exos promoted the proliferation, migration, tube formation, and VEGF level of endothelial cells in vitro. And AKT/eNOS pathway was activated by ATV-Exos and the pro-angiogenic effects of ATV-Exo were attenuated after the pathway being blocked. MiR-221-3p was upregulated by ATV-Exos stimulation, and miR-221-3p inhibitor suppressed the pro-angiogenesis effect of ATV-Exos. Conclusions Exosomes originated from ATV-pretreated MSCs might serve as a potential strategy for the treatment of diabetic skin defects through enhancing the biological function of endothelial cells via AKT/eNOS pathway by upregulating the miR-221-3p.

The vehicle information in used-car transactions is always asymmetric and disputes always happen in China. In order to reduce such disputes caused by the lack of transparency in a transaction, Blockchain technology is adopted to construct a trust mechanism for vehicle information storage and sharing in a transparent manner. In this work, a Blockchain-based vehicle history storing and tracking service, named BCVehis, is proposed. BCVehis allows vehicle owners, vehicle authority, mechanic workshops, insurance brokers and other related individuals/organizations to upload vehicle historical records via simple manners (i.e. mobile app or application programming interfaces). The system provides trustworthy vehicle history to used-car dealers, potential buyers, and other business-related parties. The design rationale and functional implementation of the BCVehis are introduced, and the increased deal volume in a local used-car dealer X is presented, which integrated BCVehis to its online dealing system.

Lack of labeled training samples is a big challenge for hyperspectral image (HSI) classification. In recent years, cross-scene classification has become a new research topic. In cross-scene classification, two closely related HSI scenes are considered, one contains adequate labeled samples, namely source scene, while the other one contains only a few labeled samples, namely target scene. The goal of cross-scene classification is utilizing the labeled samples in source scene to benefit the classification in target scene. In most cases, different HSIs are imaged by different sensors, leading to different feature dimensions (numbers of bands) in different scenes. In this situation, heterogeneous transfer learning is demanded. In this article, we propose a heterogeneous transfer learning algorithm namely semisupervised dual-dictionary nonnegative matrix factorization (SS-DDNMF). SS-DDNMF consists of two contributions. 1) Dual-dictionary nonnegative matrix factorization (DDNMF): DDNMF trains two dictionaries for source and target scenes, respectively, aiming at projecting the source and target features to a shared low-dimensional subspace, eliminating the difference between feature spaces. In DDNMF, within-scene and cross-scene graphs are built to maintain the similarities between pixels. 2) Semisupervised learning for target scene: as the limited number of labeled pixels in target scene will affect the graph building of DDNMF, semisupervised learning is adopted in target scene. In details, superpixel segmentation is adopted to generate pseudolabels for some unlabeled pixels, thus more “labeled” pixels can be considered for building better graphs. The effectiveness of SS-DDNMF is verified by experiments on cross-scene HSIs.

Background. Primary membranous nephropathy (MN), sometimes referred to as idiopathic membranous nephropathy (IMN), is a kind of MN whose pathogenesis is yet unclear. According to research reports, the incidence of IMN is about 9.8–26.8%, and it is on the rise. Methods. The computer retrieves eight databases to obtain controlled trials at home and abroad on the rituximab (RTX) actions in IMN management. After a rigorous literature quality evaluation, software called RevMan 5.3 was used for data analysis. Results. This meta-analysis finally contained 8 papers. They were all regarded as controlled trials. Six studies reported serum creatinine (standardized mean difference [SMD]: −6.87; 95% CI: −14.09, 0.35; P=0.062), ALB (SMD: 1.91; 95% CI: −0.31, 4.14; P=0.092), and adverse reactions (OR: 0.56; 95% CI: 0.36, 0.90; P

Abstract Background Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights. Results In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches. Conclusions Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

Background and Aim: With the prolonged survival time of patients with liver cancer, these families may face tremendous pressure and development dilemmas that can easily lead to family adaptation crises. Correspondingly, family adaptation crises adversely affect the quality of life of patients and family members. Basing on McCubbin's resilience model of family stress, adjustment, and adaptation, and considering the key factors affecting family resilience based on a review of literature, this study involved a construction of a family adaptation influencing factors model in Chinese liver cancer patients, which was then verified and revised.Methods: This cross-sectional study was conducted between August and December 2020. Using convenience sampling, we selected 265 liver cancer families from the liver tumor center of a teaching hospital affiliated with a university in Shanghai, China. Data from 252 patients with liver cancer and their caregivers were used to identify the factors and pathways associated with family adaptation. The relationships were modeled using structural equations.Results: A total of 265 liver cancer families participated in the survey, and 252 valid questionnaires were returned, with a response rate of 95.09%. The pathway regression coefficients of six factors (family burden, individual resilience, family problem-solving and coping, inner family support, outer family social support, and family function) in the model were found to be statistically significant (P < 0.05), indicating that all of them were significantly associated with family adaptation. Among them, inner family support, outer family social support, and family function were direct influencing factors, while the others were indirect. The path coefficients of the total effect of the determinants on family adaptation were as follows (from largest to smallest): individual resilience (0.562), family function (0.483), outer family social support (0.345), family burden (−0.300), inner family support (0.293), family problem-solving and coping (0.127).Conclusions: Our findings suggest that clinical nurses should not only pay particular attention to direct influencing factors, develop strategies to strengthen the overall family function, encourage patients and caregivers to utilize inner family and outer family social support, but should also consider indirect influence factors, focus on the vital role of the individual, and promote patients' and caregivers' personal and family coping ability.

The accurate prediction of the highway traffic speed plays an important role in improving the production efficiency and the convenience of lives, and it is also one of the important issues in vehicle social big data analyses. Aiming at the problem of highway traffic speed data preprocessing, this paper proposes the algorithm of Redundant Data Reducing (RDR) that can greatly reduce the amount of data in model training of long short-term memory (LSTM) and improve the training speed under the condition that the influence on prediction accuracy is small and controllable. Aiming at the problem of low prediction accuracy for holiday traffic speeds due to small data volume, this paper proposes the Segment Prediction Algorithm (SPA) based on the speed features, which can effectively improve the prediction accuracy. The experimental results show that RDR can reduce the training data by up to 60% without notably affecting the prediction accuracy, while it improves the training speed by 60%. Compared with the current LSTM algorithm, the prediction accuracy of SPA improves significantly.

Fast development of shared services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or services. However, privacy leakage or disclosure remains a key concern during the sharing service development process. While significant efforts have been undertaken to address various privacy issues in recent years, there is a surprising lack of review for privacy concerns in the cyber-enabled sharing world. To bridge the gap, in this paper, we survey and evaluate existing and emerging privacy issues relating to sharing services from various perspectives. Differing from existing similar works on surveying sharing practices in various fields, our work comprehensively covers six branches of sharing services in the cyber-enabled world and selects solutions mostly from the recent five to six years. We conclude the issues and solutions from three perspectives, namely, from users', platforms' and service providers' perspectives. Hot topics and less discussed (cold) topics are identified, which provides hints to researchers for their future studies.

Aim. To investigate the effects of alcohol on serum glycated albumin (GA) levels in Chinese men. Methods. A total of 2314 male subjects from the Jinuo ethnic group in China were enrolled. Of these, 986 subjects drank alcohol frequently and 404 subjects did not. Lifestyle information was gathered by using a questionnaire, and measurements of blood pressure, body mass index, blood glucose level, liver function, and kidney function were collected. GA was measured by using an enzymatic method. Frequent drinking was defined as a history of drinking ethanol > 80 g/d within the past two weeks. Nondrinking was defined as no alcohol consumption in the past three months. Subjects with an alcohol intake between 0 and 80 g/d in the past two weeks were included in the drinking-occasionally group. Analysis of variance (ANOVA), correlation analysis, and linear regression were used to evaluate the effects of drinking on serum GA levels. Decision tree regression (DTR) algorithm was used to evaluate the effect of features (variables) on GA levels. Results. We found that male subjects who drank frequently had significantly lower serum GA levels than subjects who did not drink (13.0 ± 1.7 vs. 14.1 ± 3.7, p

Abstract Background It has been proved that coffee consumption was associated with a lower risk of type 2 diabetes mellitus. But the benefit effect of coffee on hyperglycemia in Chinese population was largely unknown. Besides, the relationship of coffee intake and diabetic pathogenesis was still unclear. Methods The study population was selected from the Shanghai High-Risk Diabetic Screen (SHiDS) project. A total of 1328 individuals over 18 years of age who have the information of coffee intake were enrolled in the study from 2012 to 2016. Each participant finished a five-point 75 g oral glucose tolerance test and finished a standard questionnaire. Insulin resistance was evaluated by HOMA-IR and insulin secretion was evaluated by HOMA-β, Stumvoll first phase and second phase indexes. Results Coffee consumption group had lower plasma glucose levels at 2-h and 3-h and higher insulin levels at fasting, 30-min and 1-h during OGTT after adjustment with age, fat%, BMI, waist, tea intake, smoking habit, alcohol intake, diabetes family history and educational status (P for PG2h = 0.002; P for PG3h = 0.010; P for FIN = 0.010; P for IN30min = 0.001; P for IN1h = 0.002). Both HOMA-β and Stumvoll formula indexes were positively related to coffee consumption (P for HOMA-β = 0.033; P for Stumvoll first phase = 0.003; P for Stumvoll second phase = 0.001). Logistic regression analysis further confirmed that coffee intake was independently associated with higher levels of HOMA-β and Stumvoll insulin secretion indexes [OR (95% CI) for HOMA-β = 2.270 (1.456–3.538); OR (95% CI) for Stumvoll first phase = 2.071 (1.352–3.173); OR (95% CI) for Stumvoll second phase = 1.914 (1.260–2.906)]. Conclusions Coffee intake is independently and positively related to pancreatic beta cell function in a large high-risk diabetic Chinese population.

Background. The syndrome of maternally inherited diabetes and deafness (MIDD) is typically caused by the m.3243A>G mutation and widely considered maternally inherited. In our study, we aimed to investigate the heredity way of the m.3243A>G among pedigrees with MIDD and discover novel mitochondrial DNA mutations related to atypical clinical phenotypes. Methods. Heteroplasmy levels of the m.3243A>G mutation in peripheral blood, saliva, and urine sediment of 31 individuals from 10 unrelated pedigrees were measured by pyrosequencing. Clinical evaluations including endocrinological, audiological, and magnetic resonance imaging (MRI) examinations, mitochondrial function evaluation in peripheral blood mononuclear cells (PBMCs), and whole mitochondrial DNA (mtDNA) sequencing were performed among the spontaneous mutant pedigrees. Results. Among the 10 unrelated MIDD pedigrees, we found that the de novo m.3243A>G mutation occurred in the family 1957 (F1957). The proband (F1957-II-1) and her son (F1957-III-1) both manifested diabetes with mild bilateral sensorineural hearing loss (SNHL) and abnormal brain MRI, and F1957-III-1 also complained of severe nausea and vomiting. Mitochondrial function evaluation in PBMCs revealed an increased level of ROS generation and decreased levels of ATP and mitochondrial membrane potential (ΔΨm) in the two m.3243A>G carriers. Whole mtDNA sequencing also revealed a de novo heteroplasmic substitution at m.16093T>C in both the proband and her son. Conclusions. Our study showed that de novo m.3243A>G mutation accompanied by other point mutations may occur in the very early embryonic or germ cell stage without maternal inheritance, bringing about both typical and atypical clinical features.

In order to improve the early age strength of ordinary Portland cement-based materials, many early strength agents were applied in different conditions. Different from previous research, the nano calcium silicate hydrate (C-S-H) particles used in this study were synthesized through the chemical reaction of CaO, SiO2, and H2O under 120 °C using the hydrothermal method, and the prepared nano C-S-H particles were highly crystalized. The influences of different amounts of nano C-S-H particles (0%, 0.5%, 1%, 2% and 3% by weight of cement) on the setting time, compressive strength, and hydration heat of cement paste were studied. The hydration products and microstructure of the cement paste with different additions of nano C-S-H particles were investigated through thermogravimetry-differential thermal analysis (TG-DTA), X-ray powder diffraction (XRD), and scanning electron microscope (SEM) tests. The results show that the nano C-S-H particles could be used as an early strength agent, and the early strength of cement paste can be increased by up to 43% through accelerating the hydration of tricalcium silicate (C3S). However, the addition of more than 2% nano C-S-H particles was unfavorable to the later strength development due to more space being left during the initial accelerated hydration process. It is suggested that the suitable content of the nano C-S-H particles is 0.5%−1% by weight of cement.

Objective. In this study, we aimed to identify mt3243A > G mutation carriers in a group of Chinese elderly type 2 diabetic patients by a rapid and noninvasive diagnostic system. Methods. DNA was extracted from blood, saliva, and urine sediment samples. The mutation screening and quantitation of heteroplasmy were performed by high-resolution melting (HRM) curve and pyrosequencing, respectively. Patients with mt3243A > G mutation underwent a detailed audiometric, ophthalmologic, neurological, and cardiac examination. Results. Two patients (2/1041) carrying the mt3243A > G mutation were detected among all type 2 diabetic patients. In patient 1, the heteroplasmy was 0.8%, 2.8%, and 14.7% in peripheral blood leukocytes, saliva, and urine sediment, respectively. In patient 2, the heteroplasmy was 5.3%, 8.4%, and 37.7% in peripheral blood leukocytes, saliva, and urine sediment, respectively. Both of the two patients showed hearing impairment. Abnormal ophthalmologic conditions and hyperintensity on T2-weighted magnetic resonance images were showed in patient 1. Conclusion. The occurrence of mt3243 A > G mutation was 0.2% in Chinese elderly type 2 diabetic patients. Moreover, detection of mt3243 A > G mutation in urine sediment with high-resolution melting (HRM) curve and pyrosequencing is feasible in molecular genetic diagnosis.

ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described in patients' medical records with free texts, such as terms, phrases and paraphrases, which differ significantly from those used in ICD-10 classification. This paper presents an improved approach based on the Longest Common Subsequence (LCS) and semantic similarity for automatic Chinese diagnoses, mapping from the disease names given by clinician to the disease names in ICD-10. LCS refers to the longest string that is a subsequence of every member of a given set of strings. The proposed method of improved LCS in this paper can increase the accuracy of processing in Chinese disease mapping.

Microarray technology allows molecular classification of tumors and identification of tumor markers, and it has been used widely in the field of cancer research. Although the problem of binary tumor classification has been addressed extensively, it lacks in-depth research on multi-category tumor classification. In this paper, informative gene selection method, which is a critical step of multi-category tumor classification, was studied. We present a hybrid gene selection strategy aiming to take advantage from the combination of different gene selection algorithms. Top ranked genes of Chi-squared and SVM-RFE algorithms are fused to generate a gene pool, and a genetic algorithm further explores the search space for reduced gene subsets. We tested the proposed model on the multi-category lung cancer microarray gene expression data set. Compared with each individual gene selection algorithm, our hybrid model was able to obtain highest classification performance with much smaller sized subsets of informative genes.

With the large-scale application of gene chip,gene expression data with high dimension which exists a large number of irrelevant and redundant features may reduce classifier performance problem.A maximum mutual information feature extraction method based on cloud platforms was proposed.Hadoop cloud computing platform could be a parallel computing after gene expression data segmentation,features was extracted at the same time combined with the maximum mutual information method and the characteristics of cloud computing platform filter model was realized.Simulation experiments show that the maximum mutual information feature extraction method based on the cloud platform can rapid extraction of features in a higher classification accuracy which save a lot of time resources to make a highly efficient gene feature extraction system.

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

OBJECTIVE:The objective of this study was to evaluate the trend and prevalence of prediabetes and diabetes among high-risk adults in Shanghai from 2002 to 2012. METHODS:From 2002 to 2012, 10043 subjects with known risk factors for diabetes participated in the diabetes-screening project at the Shanghai Sixth People's Hospital of Shanghai Jiao Tong University. All participants were asked to complete a nurse-administered standard questionnaire concerning age, sex, smoking status, and personal and family histories of diabetes, cardiovascular disease, stroke, hypertension and other diseases. The participants' body mass index scores, blood pressures and blood glucose levels at 0, 30, 60, 120 and 180 min were measured in response to a 75 g oral glucose tolerance test. RESULTS:The overall prevalence of diabetes increased from 27.93% to 34.78% between 2002 and 2012 in high-risk subjects. The study also showed that the prevalence increased much faster in male compared to female subjects. Specifically, an increased rate was seen in middle-aged men, with no change observed in middle-aged females over the eleven-year period. CONCLUSION:This study showed that sex, age, parental diabetic history, and being overweight were associated with an increased risk for diabetes in high-risk people. Therefore, as prediabetes and diabetes are highly prevalent in people with multiple diabetes risk factors in Shanghai, screening programs targeting these individuals may be beneficial.