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In this article, we investigate the problem of social link inference in a target location-aware social network (LSN), which aims at predicting the unobserved links between users within the network. This problem is critical for downstream applications, including network completion and friend recommendation. In addition to the network structures commonly used in general link prediction, the studies tailored for social link inference in an LSN leverage user trajectories from the spatial aspect. However, the temporal factor lying in user trajectories is largely overlooked by most of the prior studies, limiting the capabilities of capturing the temporal relevance between users. Moreover, effective user matching by fusing different views, i.e., social, spatial, and temporal factors, remains unresolved, which hinders the potential improvement of link inference. To this end, this article devises a novel multiview matching network (MVMN) by regarding each of the three factors as one view of any target user pair. MVMN enjoys the flexibility and completeness of modeling each factor by developing its suitable matching module: 1) location matching module; 2) time-series matching module; and 3) relation matching module. Each module learns a view-specific representation for matching, and MVMN fuses them for final link inference. Extensive experiments on two real-world data sets demonstrate the superiority of our approach against several competitive baselines for link prediction and sequence matching, validating the contribution of its key components.

Metal–organic frameworks (MOFs) containing open metal sites are important materials for acetylene (C2H2) adsorption. However, it is inefficient or even impossible to search suitable MOFs by molecular simulation method in nearly infinite MOFs space. Therefore, machine learning (ML) methods are adopted in the material screening and prediction of high-performance MOFs. In this paper, architecture, chemical and structural features are used to analyze the C2H2 adsorption performance of the MOFs. Different ML algorithms are applied to perform classification and regression analysis to the factors affecting material adsorption. By decision tree (DT) algorithm, it is found that only PV, GSA, and Cu-OMS are sufficient to determine the high adsorption of the MOFs. Furthermore, the influence of topology on the performance of MOFs is obtained. Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN), are introduced to analyze the quantitative structure–property relationship (QSPR) between C2H2 adsorption and the features of MOFs. The prediction of the GBDT model is found to have the highest accuracy, with R2 as 0.93 and RMSE as 11.58. In addition, the GBDT model is used for feature analysis, and the contribution of each feature to the performance is obtained, which is of great significance for the design and analysis of MOFs. The successful application of ML to MOFs screening greatly reduce the calculation time and provides important reference for the design and synthesis of new MOFs.

Background: An early trend in the mean age of pubertal onset appears in adolescents, but the association between body fat percentage (BF%) of children and precocious puberty is unclear. The aim of ...

To solve the problem of difficult face detection in a low illumination vehicle environment, a novel multi-scale retinex color restoration (MSRCR) approach exploiting the RGB three-channel decomposition and guided filtering (MSRCR-3CGF) is proposed. The MSRCR algorithm is employed to remove the artifacts and interference of low-light in the image based on the face detector using a multi-task cascaded convolutional neural network (MTCNN). The enhanced face image is decomposed into RGB, and GF is applied to each channel. The proposed method is tested on three widely used datasets: Dark Face, large-scale CelebFaces attributes (CelebA) and WIDER FACE, and an actual low-light scene in vehicles. The experimental results show that the proposed method suppresses the high-frequency noise of MSRCR, whilst improving the image enhancement and accuracy in the face detection in a low-light vehicle environment.

Objectives In this study, we evaluated the concordance between the ultrasonographic stage of breast (US B) and Tanner stage of breast (TS B) for overweight and obese girls based on a school population study. Methods We conducted multistage, stratified cluster, and random-proportional sampling and ultimately included 221 girls (aged 6–10 years). Results This study revealed that the concordance was poor (accuracy=0.19 (95% confidence interval: 0.14, 0.25)) between US B and TS B among the 221 participants. When our subjects were stratified by weight, we observed a weak association between US B and TS B in the thin/normal weight group (r=0.34, p=0.001) but not in the overweight (r=0.097, p=0.38) or obese groups (r=–0.19, p=0.206), and as the body mass index (BMI) z-score increased, the overestimation ratio of TS B increased. US B manifested a positive correlation with breast bud diameter (BD) (r=0.885, p Conclusions As the BMI z-score increased, the correlation between the two methods declined, and the overestimation ratio of TS B increased. US B is an objective and quantitative method used to evaluate breast development, and whether BD might replace US B as a routine diagnostic method to evaluate breast development in clinical practice needs to be confirmed in larger-sample studies.

To investigate the prevalence of precocious puberty in school-based population in Zhongshan, Guangdong, China. We recruited a total of 4058 students in grades 1–3 using a multistage stratified cluster random sampling method in a population-based cross-sectional study. We evaluated height, weight, and development of breast and pubic hair in girls, breast development was evaluated by inspection and palpation combined with ultrasound detection for overweight and obese girls, and testicular volume and development of pubic hair in boys. We estimated overall, sex-, age, and district-specific prevalence of precocious puberty and examined the association between prevalence of precocious puberty and body mass index (BMI). The unadjusted and adjusted prevalence of precocious puberty by Tanner stage was 4.74% (girls:8.78%, boys:2.58%) and 6.19% (girls:11.47%, boys:3.26%), respectively. In both urban and suburban areas, the prevalence of precocious puberty was higher in the overweight and obese group than in the normal-weight group (p

With the continuous development of social economy, private cars have become more and more, and the traffic pressure is also increasing. To more accurately recognize vehicles violating traffic rules, the problem of recognition and optimization of license plate targets has become an urgent task with certain practical significance and guiding significance. In this paper, the license plate recognition (LPR) system is taken as the main research object, which is improved and optimized. Firstly, an improved neural network model is established by applying the improved convolutional neural network (CNN) algorithm to the license plate recognition system and based on the improved activation function, and the relevant experimental results are obtained; meanwhile compared with the traditional CNN model, the corresponding experimental results are obtained; finally, the experimental conclusions are obtained, i.e., the LPR system using improved CNN algorithm has better performance in recognizing license plate target and can more accurately recognize licence plates which are shielded. Therefore, the improved neural network model has great development potential in the application of the LPR system.

Background: Previous studies have shown that postpartum depression negatively affects breastfeeding practices. However, it is not clear whether maternal mood symptoms during pregnancy affect exclusive breastfeeding (EBF) behavior. Our study aimed to quantify the relationship between maternal mood symptoms (depression/anxiety) during pregnancy and EBF.Methods: A cross-sectional study was conducted at Baoan Maternal and Child Health Hospital, Shenzhen, China, from January 1, 2016, to December 31, 2016, among women who had delivered in the hospital and completed the 6-week postpartum visit. A structured questionnaire was designed to collect information on maternal mood symptoms during pregnancy and EBF status at 6 weeks postpartum. Logistic regression models were used to evaluate the associations between maternal mood symptoms (depression/anxiety) during pregnancy and EBF at 6 weeks postpartum.Results: In total, 6324 women were included in this study. In univariate analysis, we observed that depression during pregnancy was associated with a reduced risk for EBF (OR = 0.66; 95% CI: 0.54-0.81; P < 0.001). In the fully adjusted model, depression during pregnancy was still significantly associated with a reduced risk of EBF (OR=0.69; 95% CI: 0.56–0.86; P < 0.001). However, there was no significant difference in the rate of EBF between the anxiety and non-anxiety groups of women (OR=0.88; 95% CI: 0.77–1.01; P > 0.05). Conclusion: We believe that our findings confirm the adverse impact of maternal depression during pregnancy on EBF behavior. Therefore, to improve conditions for breastfeeding of infants, additional attention should be paid to women with depressive symptoms during pregnancy.

In order to meet the demand of constructing salt-cavern underground gas storages (UGSs) in favorable salt mines for the regions that are neither major oil and gas production zones nor major natural gas consumer markets in South China, and solve the problems of unclear conditions for selecting salt-cavern gas storage sites and of inconsistent content and depth in evaluating gas storage conditions, based on investigation and analysis of salt-cavern UGS data in China and other countries, we proposed the principles of selecting gas storage sites in combination with geology and ground surface conditions, and developed a set of technologies in evaluating construction conditions for salt-cavern gas storages. By applying the selection principles of salt-cavern UGSs, we selected and sorted some salt mines in the three major natural gas consumer markets (the Yangtze River Delta, Central–South China, and Southeast Coast of China), and evaluated the conditions of constructing a gas storage in the Huai'an salt mine with better conditions. The following findings were obtained. First, the principles of selecting gas storage sites can be applied to quickly optimize and sort the selected targets, including 6 items of geological factors such as structure, burial depth, salt-bearing layer thickness, NaCl content, top rock, and reserves, and 3 other items such as ground surface conditions, water source and brine, distance from pipeline networks. Second, the evaluation system of UGS construction conditions integrates features of structure and salt-bearing strata, tightness, firmness, dissolubility evaluation, prediction of gas storage scale, and analysis of cavity making condition. In conclusion, the new evaluation technologies can not only effectively guide the site selection and evaluation of salt-cavern UGSs but provide foundation and power support for their scheme designs, and the presented pilot proposal for utilizing thick interlayers has achieved a perfect on-site application effect. This study is of great significance for constructing UGSs in China. Keywords: Salt-cavern, Underground gas storage, Site selection, Storage construction conditions, Strata feature, Appraisal system, Case analysis, Huai'an salt mine (Jiangsu)

Wave-coefficients (WCs) based target recognition can be theoretically applied to any a frequency range, with increased sensitivity to aspect angle at higher frequencies. When it is utilized to a frequency range in resonance region, the WCs can tolerate fairly big aspect variation and contain enough target information, bringing a competitive advantage in multitudinous target features. Four polarization channel WCs are introduced and independently utilized to identify four aerospace targets under noise and noise-free circumstances. To take full advantage of the WCs in different polarization channels, a novel radar aerospace target recognition method based on multi-polarization channel WCs is proposed in this article. The majority vote rule and a maximum discrepancy rule are combined to identify a target when four polarization channel WCs are available at the same time. Simulation results on four aircraft models show that the proposed technique achieves a recognition performance at least comparable to the best single channel performance.

The untunable bandgap is a difficult problem for Bragg structures constructed from corrugated metallic waveguides, and becomes a major barrier for applications. Based on the coupled-mode theory, this paper presents the principle as well as a corresponding theoretical model of bandgap control of two-dimensional coaxial Bragg structures under the condition of a fixed ripple shape. It is shown that such control can be achieved by varying the angular deviation between the outer and inner corrugations, which can be done by rotating one of the conductors relative to the other. The effects of the proposed method on transmission bandgap control of two example structures were investigated by theoretical analysis and electromagnetic simulation. The results confirmed the validity of the proposal, and showed that such a control method can be used to manipulate the number and location of the working bands under multiple bandgap operation. These peculiarities provide potential applications of two-dimensional coaxial Bragg structures in constructing tunable passive and active high-power microwave devices.

To obtain the best pooling effect and higher accuracy in image recognition, an improved method based on optimal search theory for the pooling layer of convolutional neural networks (CNNs) is proposed. The purpose is to solve the problems of the traditional pooling method, namely that it is too simplistic and it is difficult to extract effective features. The basic principle and network structure of CNN are introduced in the study. A new optimum-pooling method is proposed, and the authors study how to obtain the maximum probability to detect the target function under the constrained condition. Comparison experiments of different pooling methods are performed on three widely used datasets: LFW, CIFAR-10, and ImageNet. The experimental results show that the proposed method has the characteristics of more effective feature extraction and wide adaptability, and leads to higher accuracy and lower error rate in image recognition.

Background Nonpharmacological interventions are the first recommendation for cancer-related fatigue, according to current guidelines. There are many forms of nonpharmacological interventions for addressing cancer-related fatigue, but the preferred means remain controversial and are not stated in the guidelines. Therefore, we evaluated the comparative effects and ranks of all major nonpharmacological interventions, according to different assessment methods, in cancer patients with fatigue. Methods Medline, Embase, Cochrane Library, and Allied and Complementary Medicine Database were searched for randomized controlled trials on nonpharmacological treatments for cancer-related fatigue. We assessed the trials' methodological quality using the Cochrane Risk of Bias tool. A Bayesian network meta-analysis and a comparative effects ranking were performed with Aggregate Data Drug Information System software. Results A total of 16,675 items were obtained from the databases, and 182 studies comprising 18,491 participants were included in the analysis. Based on the ranking probabilities, multimodal therapy and qigong ranked best with a Brief Fatigue Inventory; for a Functional Assessment of Cancer Therapy-fatigue scale, combined psychosocial therapies and bright white light therapy ranked best; for the Piper Fatigue Scale, resistance exercise and mindfulness-based stress reduction ranked best; for a multidimensional fatigue inventory, multimodal therapy and cognitive behavioral therapy (CBT) ranked best; for the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), acupuncture and CBT ranked best; and for the Profile of Mood States Fatigue Subscale, multimodal therapy, qigong, aerobic exercise, and CBT ranked best. Comprehensive analysis of the results indicated that multimodal therapy, CBT, and qigong might be the optimum selections for reducing cancer-related fatigue. Most of the included studies had low risk of methodological quality problems; however, 59 studies had low methodological quality. Linking evidence to action Different interventions have their own sets of advantages for addressing cancer-related fatigue. These results can be utilized as evidence-based interventions for healthcare workers and patients to manage cancer-related fatigue.

Few studies have investigated the functional patterns of methamphetamine abstainers. A better understanding of the underlying neurobiological mechanism in the brains of methamphetamine abstainers will help to explain their abnormal behaviors. Forty-two male methamphetamine abstainers, currently in a long-term abstinence status (for at least 14 months), and 32 male healthy controls were recruited. All subjects underwent functional MRI while responding to drug-associated cues. This study proposes to combine a convolutional neural network with a short-time Fourier transform to identify different brain patterns between methamphetamine abstainers and controls. The short-time Fourier transformation provides time-localized frequency information, while the convolutional neural network extracts the structural features of the time–frequency spectrograms. The results showed that the classifier achieved a satisfactory performance (98.9% accuracy) and could extract robust brain voxel information. The highly discriminative power voxels were mainly concentrated in the left inferior orbital frontal gyrus, the bilateral postcentral gyri, and the bilateral paracentral lobules. This study provides a novel insight into the different functional patterns between methamphetamine abstainers and healthy controls. It also elucidates the pathological mechanism of methamphetamine abstainers from the view of time–frequency spectrograms.

Objective: The objective of this study was to evaluate whether the Chinese herbal medicine formula Guilu Erxian Jiao can improve the T-score in osteoporosis patients and even decrease the fracture rate.Design: This is a retrospective study.Subjects and Setting: Osteoporosis participants were selected from the Chang Gung Memorial Hospital (CGMH) database from 2000 to 2019.Interventions: The patients were administered Guilu Erxian Jiao pills.Outcome measures: We analyzed the change of T-score and the associated cumulative incidence of fracture.Results: Eighty-five patients administered GEJ met the inclusion criteria. After propensity score matching (1:5), 425 patients who were not administered GEJ were included in the control group. There were no significant differences about the baseline characteristics between the study and control groups. In the study group, GEJ improved the osteoporosis of the lumbar vertebrate and osteopenia of the hip joint but did not improve osteoporosis of the femoral neck. The cumulative rate of fracture between these two groups was not significantly different. Patients who took at least 600 GEJ pills over the treatment period had a decreased risk for fracture at fracture-prone sites.Conclusion:The use of Guilu Erxian Jiao may improve osteoporosis of the lumbar vertebrate and osteopenia of the hip joint. In addition, it may help decrease fractures of the lumbar spine. Administering at least 600 GEJ pills over the treatment period might result in a decreased risk for fracture at fracture-prone sites.

To solve the problem of errors accumulate over time of strapdown inertial navigation system (SINS), a SINS and Global positioning system (GPS) integrated system is proposed considering their complementary characteristics. We employ loosely-coupled method and Kalman filter (KF) algorithm for data fusion and errors correction to obtain more accurate location information. The process noise and measurement noise in observation process are treated as Gaussian white noise, and their expectations will be given in advance. Compared to SINS, the proposed SINS/GPS integrated system has significantly better navigation accuracy and robustness according to simulation and a field experiment’s results.

Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this paper, we focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. Nevertheless, due to the limited annotations, models may overly rely on the contexts available in the training data, which causes poor generalization to the scenes unseen before. A preferred high-level representation should capture the contextual information while not losing self-awareness. Therefore, we propose to maintain the context-aware consistency between features of the same identity but with different contexts, making the representations robust to the varying environments. Moreover, we present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner, only requiring the feature with lower quality to be aligned towards its counterpart. In addition, to avoid the false-negative samples and filter the uncertain positive samples, we put forward two sampling strategies. Extensive experiments show that our simple yet effective method surpasses current state-of-the-art methods by a large margin and also generalizes well with extra image-level annotations., Accepted to CVPR 2021. Code is available at https://github.com/dvlab-research/Context-Aware-Consistency

A scheme based on bidirectional Michelson interferometers to distribute secret keys through the phase of light wave is proposed and demonstrated. The structure uses two optical cable transmission schemes, lasers of short coherent length, random phase drift, polarization random disturbance, and vibration positioning technology to ensure the safe transmission of the secret key. The feasibility of 80 km secret key transmission is verified by experiments. When the secret key is transmitted at the frequency of 200 kHz, the bit error rate is less than 0.3%, and the disturbance positioning accuracy is within ± 100 m .

Pathologic conditions in which the body׳s own response to an insult is the most damaging aspect of the disease, such as in auto-immunity and sepsis, have seen researchers and clinicians struggle to find therapeutic targets for decades. Large clinical cohorts and advanced molecular biology are key elements in the search for new medications. Methodology with increasing resolution is constantly being developed to identify new subsets of cells embedded in the disease framework and to map their complex gene expression profile, secretome and epigenome. Big data that is thus being generated require advanced computational methods in order to distil knowledge from them, and in order to use them as the basis for modeling approaches. Models need to integrate the spatial, temporal and organizational domains in order to reflect biology with a certain degree of fidelity. The ultimate goal of the combined efforts of clinical studies, molecular biology with cutting-edge technology and comprehensive mathematical modeling is the bench-to-bedside translation of knowledge to reduce the socio-economic burden of disease.

Doxorubicin (DOX) could trigger congestive heart failure, which largely limited the clinical use of DOX. microRNAs (miRNAs) were closely involved in the pathogenesis of DOX-induced cardiomyopathy. Here, we aimed to investigate the effect of miR-152 on DOX-induced cardiotoxicity in mice. To study this, we used an adeno-associated viral vector to overexpress miR-152 in mice 6 weeks before DOX treatment, using a dose mimicking the concentrations used in the clinics. In response to DOX injection, miR-152 was significantly decreased in murine hearts and cardiomyocytes. After DOX treatment, mice with miR-152 overexpression in the hearts developed less cardiac dysfunction, oxidative stress, inflammation, and myocardial apoptosis. Furthermore, we found that miR-152 overexpression attenuated DOX-related oxidative stress, inflammation, and cell loss in cardiomyocytes, whereas miR-152 knockdown resulted in oxidative stress, inflammation, and cell loss in cardiomyocytes. Mechanistically, this effect of miR-152 was dependent on the activation of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) in response to DOX. Notably, Nrf2 deficiency blocked the protective effects of miR-152 against DOX-related cardiac injury in mice. In conclusion, miR-152 protected against DOX-induced cardiotoxicity via the activation of the Nrf2 signaling pathway. These results suggest that miR-152 may be a promising therapeutic target for the treatment of DOX-induced cardiotoxicity.

Postpartum depression (PPD) is a major public health problem worldwide, which adversely affects maternal and infant health. The purpose of this study was to determine the prevalence and related factors of postpartum depression in Chinese puerperium women.A cross-sectional study was conducted in Baoan Maternal and Child Health Hospital located in the west of Shenzhen, China. The Chinese version of the Edinburgh Postpartum Depression Scale (EPDS) was used to screen PPD. A score of ≥10 was used as the threshold of postpartum depression.A total of 4813 puerperal women were included in this study, 11.5% of whom were considered to have PPD. Multivariate logistic regression analysis found that family history of mental illness (OR = 1.94; 95% CI, 1.05-3.57; P = 0.033), living with parents-in-law (OR = 1.41; 95% CI, 1.16-1.72; P0.001), anxiety during pregnancy (OR = 3.66; 95% CI, 2.97-4. 52; P0.001), depression during pregnancy (OR = 4.25; 95% CI, 3.28-5.50; P0.001), and stressful life events (OR = 1.50; 95% CI, 1.03-2.20; P = 0.036) were associated with the high prevalence of PPD.The cross-sectional survey cannot assess direct causality. EPDS is just a screening tool and not a diagnostic one.Postpartum depression is a common disease in Chinese puerperal population. High-risk pregnant women with postpartum depression need routine screening and targeted intervention for PPD.

With the evolution of web technologies, sentiment analysis, especially aspect-based sentiment analysis (ABSA), as a technology to identify and extract user opinions, has attracted widespread attention. According to the characteristics of user comments, we convert ABSA problem into a multi-label classification problem, and propose a classification model based on BERT with modified TF-IDF for ABSA. In the feature extraction phase, a modified TF-IDF method is proposed to better reflect the words' importance in multi-label classification problem by calculating the different weights in individual classes. And a new feature is generated by combining the BERT embedding with modified TF-IDF for classification task. Then the feature is input into a fully connected layer to fine-tune BERT model for the multi-label classification task. The effectiveness of supervised TF-IDF and proposed model is validated by experiments of multilabel classification on a restaurant reviews dataset.

To address the problem of measurement noise interference in the trajectory tracking process of a wall-climbing robot (WCR), a robust Student's t-based extended Kalman filter (STEKF) is proposed. The measurement noise of the extended Kalman filter (EKF) is modeled with a Student's t distribution instead of a Gaussian distribution. Three kinds of filters are selected for comparison. Simulation results show that STEKF algorithm is more robust with accuracy than existing state-of-the-art filters.

Study design This was a Bayesian network meta-analysis. Objective We aimed to assess the comparative efficacies of the 5 most commonly used procedures for spondylolisthesis in a Bayesian network meta-analysis. Summary of background Lumbar fusion procedures are the backbone for the surgical treatment of spondylolisthesis. However, the current evidence has only considered head-to-head comparisons of different fusion procedures and failed to definitively favor one fusion procedure over another. Materials and methods A Bayesian random-effects model was used, and radiographic fusion and complications were assessed by risk ratio with a corresponding 95% credible interval; Oswestry Disability Index (ODI) scores were assessed by mean difference and 95% credible interval. Furthermore, with respect to each endpoint, ranking probabilities for each fusion procedure were evaluated using the value of the surface under the cumulative ranking curve. Results In all, 12 studies were identified. Significant differences in associated complications were found between minimally invasive transforaminal lumbar interbody fusion (MTLIF) and each of the other 4 fusion procedures. Surface under the cumulative ranking curve results suggested that for the most effective radiographic fusion, open transforaminal lumbar interbody fusion (OTLIF) had the maximum probability of being successful (76.6%), followed by MTLIF (70.4%) and open posterior lumbar interbody fusion (OPLIF, 48.7%). In terms of ODI scores, OTLIF might be the best intervention (70.5%), followed by MTLIF (68.8%) and then OPLIF (44.0%). However, MTLIF is ranked the safest (99.8%) regarding associated complications, followed sequentially by OTLIF (57.1%) and posterolateral fusion (30.1%). Conclusions OTLIF for spondylolisthesis was found to be the more efficient procedure in terms of radiographic fusion and ODI scores. Importantly, MTLIF is most likely to have a lower associated complication rate compared with other fusion procedures.

Social Media Popularity (SMP) prediction focuses on predicting the social impact of a given post from a specific user in social media, which is crucial for online advertising, social recommendation, and demand prediction. In this paper, we present HyFea, our winning solution to the Social Media Prediction (SMP) Challenge for multimedia grand challenge of ACM Multimedia 2020. To address the multi-modality and personality issues of this challenge, HyFea carefully considers multiple feature types and adopts a tree-based ensembling method, i.e., CatBoost, which is shown to perform well in prediction. Specifically, HyFea involves the features related to Image, Category, Space-Time, User Profile, Tag, and Others. We conduct several experiments on the Social Media Prediction Dataset (SMPD), verifying the positive contributions of each type of features.

Background: Mild cognitive impairment (MCI) is the early phase of Alzheimer's disease (AD). The aim of early intervention for MCI is to decrease the rate of conversion from MCI to AD. However, the efficacy of multiple interventions in MCI, and the optimal methods of delivery, remain controversial. We aimed to compare and rank the treatment methods for MCI in AD, in order to find an optimal intervention for MCI and a way to prevent or delay the occurrence of AD. Methods: Pair-wise and network meta-analysis were conducted to integrate the treatment effectiveness through direct and indirect evidence. Four English databases and three Chinese databases were searched for international registers of eligible published, single or double blind, randomized controlled trials up to September 31st 2019. We included nine comparative interventions: pharmacological therapies which incorporated cholinesterase inhibitors (ChEI), ginkgo, nimodipine, and Chinese medicine; non-pharmacological therapies comprising of acupuncture, music therapy, exercise therapy, and nutrition therapy; and a placebo group. The primary outcome was the Mini-Mental State Examination (MMSE) score. The secondary outcome was the AD Assessment Scale-cognitive subscale (ADAS-cog). Results: Twenty-eight trials were eligible, including 6,863 participants. In the direct meta-analysis, as for the Mini-Mental State Examination scale, the ChEIs (MD: -0.38; 95% CI: -0.74, -0.01), Chinese medicine (MD: -0.31; 95% CI: -0.75, 0.13), exercise therapy (MD: -0.50; 95% CI: -0.65, -0.35), music therapy (MD: -1.71; 95% CI: -4.49, 1.07), were statistically more efficient than placebo. For AD Assessment Scalecognitive subscale outcome, ChEIs (MD: 1.20; 95% CI: 0.73, 1.68), Acupuncture (MD: 1.36; 95% CI: 1.28, 1.44), Chinese medicine (MD: 0.61; 95% CI: 0.49, 0.73) and exercise (MD: 0.61; 95% CI: 0.49, 0.73) were better than placebo. In the network meta-analysis, the MMSE outcome ranked music therapy (59%) as the best and Acupuncture (26%) as second. Nutrition and Ginkgo treatment had the lowest rank among all interventions. For ADAS-cog outcome, acupuncture (52) ranked the best. Conclusion: Among the nine treatments studied, music therapy appears to be the best treatment for MCI, followed by acupuncture. Our study provides new insights into potential clinical treatments for MCI due to AD, and may aid the development of guidelines for MCI in AD.

COVID-19 has been announced pandemic by WHO and over 17,000,000 people infected (Till April 21st 2020). The disease is currently under control in China, with a curative rate of 86.8%. Chloroquine (CQ) is an old anti-malarial drug with good tolerability, which had proved to be effective in previous SARS-coronavirus, which spread and disappeared between 2002-2003. In vitro studies demonstrated the efficacy of CQ in curing COVID-19. Consequently, via analytical PBPK modeling, a further preliminary clinical trial has proved the efficacy and safety of CQ in China., and multiple clinical trials were registered and approved to investigate the activity of other analogs of CQ against COVID-19. We have listed all the clinical trials and made a meta-analysis of published data of hydroxychloroquine (HCQ). HCQ could increase the CT improvement and adverse reactions (ADRs) significantly though there was considerable heterogeneity among current researches. Actually, CQ and its analogs have unique pharmacokinetic characteristics, which would induce severe side effects in some circumstances. We have then summarized pharmacological considerations for these drugs so as to provide to the busy clinicians to avoid potential side effects when administered CQ or its analogs to COVID-19 patients, especially in the elderly, pediatrics, and pregnancies.

A novel tangential spiral-flow column photobioreactor (TSCP) was developed to strengthen the velocity field and turbulent kinetics for improving CO2 fixation with microalgae. Microalgal solution was injected into a column photobioreactor from four symmetrically arranged nozzles, whose central axes had 10°-included angles with the radius. The injection solution streamline formed a tangential swirling circle, thereby driving the surrounding solution to spirally flow upward. This flow pattern was determined via computational fluid dynamics simulation and a miniature Doppler velocimeter. The optimal structural and operational parameters were determined as follows: relative diameter of imaginary tangential circle = 0.3, nozzles number = 4, and injection solution velocity = 1.33 m/s. The average turbulent kinetic energy, bubble diameter, and actual photochemical efficiency in TSCP were 15% higher, 47% lower, and 19% higher than those in the bubble column photobioreactor (BCP), respectively. Therefore, the micro...

Hepatitis B e antigen (HBeAg) seroconversion is considered to have significantly favourable clinical outcomes for patients with chronic hepatitis B (CHB). However, inconsistent study results suggest that hepatocellular carcinoma (HCC) still occurs in patients with HBeAg seroconversion. We performed a systematic review and meta-analysis to determine the incidence of HCC in patients with CHB after HBeAg seroconversion. Web of Science, PubMed and Embase databases were searched through January 2017. The incidence of HCC in CHB patients after HBeAg seroconversion was pooled using a random-effects model or fix-effects model. Sixteen studies were finally included, involving 4910 patients with HBeAg seroconversion. The overall pooled proportion suggested that 3.33% (95% confidence interval (CI): 2.28%-4.58%) of patients with CHB develop HCC despite HBeAg seroconversion. In patients with HBeAg seroconversion without cirrhosis, the pooled proportion of HCC development was 0.94% (95% CI: 0.15%-2.4%). Moreover, patients with cirrhosis, active hepatitis, or aged greater than 40 years at the time of HBeAg seroconversion were at significantly higher risk for HCC development. HBeAg seroconversion was significantly associated with a reduced risk of HCC compared with persistently positive HBeAg (RR = 0.58, 95% CI: 0.35-0.97, P = .04). Despite the reduced risk with HBeAg seroconversion, HCC can still occur in a proportion of patients with CHB after HBeAg seroconversion. Long-term monitoring is needed for patients with established cirrhosis, active hepatitis or those older than 40 years at the time of HBeAg seroconversion.

For the problems of light source fluctuation noises, zero drift and other problems of H2S gas concentration sensor in optic-fiber evanescent-wave absorption spectroscopy, double-light-path absorption system was designed to theoretically analyze the relationship between evanescent-wave power and gas concentration as well as detection sensitivity. Moreover, the relationship between evanescent-wave power and optical-fiber residual diameter in the optic fiber corrosion process was also studied. The sensing probes with different evanescent-wave power ratios were used to detect experiments of H2S gas concentration. The experimental results show that the sensitivity is up to 0.169 μw/ppm when the evanescent-wave power ratio of sensing probe is 10%, detected minimum H2S gas concentration is 5.1 ppm, the detection repeatability is good and the response time is only 2.03s. Due to low production cost, high sensitivity and short response time, the optic-fiber evanescent-wave sensor can be used for real-time on-line measurement of H2S gas in the area of the hydrocarbon exploration and development.

To avert the degradation information island brought by a single model for state estimation of lithium-ion batteries (LIBs), multi-model integration is well worth being extended for health evaluation. A cloud-edge collaboration strategy that integrates multi-model adaptation and machine learning is proposed for battery capacity prediction in lifespan. In this respect, multiple individual algorithms are promoted relying on online data from battery management system (BMS) and induced ordered weighted average (IOWA) operator is introduced for joint capacity estimation. Taking the massive data storage and computing power into account, the operation of the neural network optimized by genetic algorithm (GA) is elevated to the big data platform for strong robustness and accurate prediction, accompanied by the historical state message from BMS. With the validation of NASA battery aging data, the error of the collaborative prediction strategy is kept within 5%, and the feasibility of cloud-edge collaboration is confirmed for future battery management.

In this work, we have developed a non-ordinary state-based peridynamics model for brittle fracture in ceramics or fracture in quasi-brittle materials in general. The model is firstly validated by three numerical benchmark tests, and then it is applied to simulate the edge-on impact and drop ball test experiments. We have implemented the modified Johnson Holmquist (JH-2) constitutive damage model into the peridynamics framework at finite strain. Furthermore, the contact algorithm between the projectile and target is discussed. It is shown that the numerical results obtained from peridynamics simulations are in general agreement with those from the experiment. The comparison of experimental and numerical results indicates that the proposed peridynamics model has the ability to capture the damage propagation and other features of the brittle fracture.

Background Premature infants are prone to suffer multisystem complications after birth due to the incomplete development of organ tissues and low immunity, and they require a longer period of supervised treatment in the neonatal intensive care unit (NICU). However, due to the specificity of medical care in the NICU, the sleep of preterm infants is highly susceptible that has an impact on the prognosis of preterm infants. Recently, various non-pharmacological interventions have been applied to the sleep of preterm infants in the NICU, which have shown positive outcomes. However, the efficacy and safety of them are unclear. This study aims to evaluate the effects of non-pharmacological interventions on sleep in preterm infants in the NICU through a network meta-analysis. Methods Randomized controlled trials of non-pharmacological interventions on sleep in preterm infants in the NICU published before September 2021 will be searched in online databases, including the Chinese Scientific Journal Database, China National Knowledge Infrastructure Database, Wanfang, China Biomedical Literature Database, PubMed, Cochrane Library, Embase, and Web of Science. Two researchers will be independently responsible for screening and selecting eligible literatures, extracting data and evaluating the risk of bias in the included studies. Stata 14.0 software will be used for data analysis. Results The results of this meta-analysis will be submitted to a peer-reviewed journal for publication. Conclusion This study will provide comprehensive and reliable evidence-based references for the efficacy and safety in different non-pharmacological interventions on sleep in preterm infants in the NICU.

With the propagation of sensor devices applied in smart home, activity recognition has ignited huge interest and most existing works assume that there is only one habitant. While in reality, there are generally multiple residents at home, which brings greater challenge to recognize activities. In addition, many conventional approaches rely on manual time series data segmentation ignoring the inherent characteristics of events and their heuristic hand-crafted feature generation algorithms are difficult to exploit distinctive features to accurately classify different activities. To address these issues, we propose an end-to-end Tree-Structure Convolutional neural network based framework for Multi-Resident Activity Recognition (TSC-MRAR). First, we treat each sample as an event and obtain the current event embedding through the previous sensor readings in the sliding window without splitting the time series data. Then, in order to automatically generate the temporal features, a tree-structure network is designed to derive the temporal dependence of nearby readings. The extracted features are fed into the fully connected layer, which can jointly learn the residents labels and the activity labels simultaneously. Finally, experiments on CASAS datasets demonstrate the high performance in multi-resident activity recognition of our model compared to state-of-the-art techniques.

A compact dual band 2 × 1 multiple input and multiple output (MIMO) antenna with high isolation was proposed. It resonates at 2.4 and 5.25 GHz. The proposed MIMO antenna with a compact size of 0.2λ * 0.27λ (f = 2.4 GHz). The high isolation performance is obtained by loading strips with the ground plane, inserting quasi-pai shaped slots in the ground plane and employing neutral line between the two elements of the MIMO antenna. Results show that the isolation of the MIMO antenna can reach 33 dB at both of the operating frequencies.

As an important member of periodic structures family, Bragg structures are widely employed in the many active and passive microwave devices, such as reflectors, couplers, filters and mode converters. In this paper, the transmission characteristics of a two dimensional coaxial Bragg structure with double frequency selective surfaces are investigated, where both of the inner and outer conductors are corrugated with helical ripples. Numerical results reveal that the transmission bandgap can be controlled by the relative angular position of the inner and outer conductors. This peculiarity may lead to potential applications of these structures in constructing tunable microwave devices.

Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the false-positive calls. The existing filters rely on the whole genome/exome sequencing data, which can provide sufficient samples for training the filters. However, the gene-panel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semi-learning filter for gene-panel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on gene-panel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.90-0.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on gene-panel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for gene-panel sequencing data.

The insufficient accuracy of GPS technology leads to the sampling trajectory data being away from the actual road. In order to improve the accuracy of GPS trajectory data for matching to map, a map matching integration algorithm based on historical trajectory data is proposed. Firstly projection distance and hidden Markov model are used respectively to compare the matching results. Then the difference road segments are found and the DBSCAN algorithm is used to cluster the historical trajectory data to adjust the segments. Our experiment uses the truck trajectory data to test the algorithm. The results show that the map matching integration algorithm effectively improves the accuracy of map matching.

Chemosensory systems play an important role in insect behavior, and some key associated genes have potential as novel targets for pest control. Cacopsylla chinensis is an oligophagous pest and has become one of the main pests of pear trees, but little is known about the molecular-level means by which it locates its hosts. In this study, we assembled the head transcriptome of C. chinensis using Illumina sequencing, and 63,052 Unigenes were identified. A total of 36 candidate chemosensory genes were identified, including five different families: 12 odorant binding proteins (OBPs), 11 chemosensory proteins (CSPs), 7 odorant receptors (ORs), 4 ionotropic receptors (IRs), and 2 gustatory receptors (GRs). The number of chemosensory gene families is consistent with that found in other Hemipteran species, indicating that our approach successfully obtained the chemosensory genes of C. chinensis. The tissue expression of all genes using quantitative real-time PCR (qRT-PCR) found that some genes displayed male head, female head, or nymph-biased specific/expression. Our results enrich the gene inventory of C. chinensis and provide valuable resources for the analysis of the functions of some key genes. This will help in developing molecular targets for disrupting feeding behavior in C. chinensis.

With the rapid development of electric vehicles, the safe and environmentally friendly disposal of retired lithium batteries (LIBs) is becoming a serious issue. Echelon utilization of the retired LIBs is a promising scheme because of its considerable potential for generating economic and environmental value. The most outstanding technical challenge of echelon utilization is how to sort and regroup the large-scale retired LIBs efficiently and accurately. In this paper, the status and challenges of echelon utilization for the retired LIBs are reviewed. First, the criteria, policies, regulations, markets, costs, and values of echelon utilization are summarized comprehensively to illustrate its potential and expose existing problems and pain points. Second, the key technologies related to large-scale echelon utilization of LIBs are detailed; valuable opinions and technical routes are presented for the selection and rapid estimation of sorting indices, the classification and regrouping algorithm, evaluation of the sorting results, and other aspects. In particular, a multilevel and multidimensional fast sorting method is proposed for large-scale echelon utilization of retired LIBs that considers different scenario constraints. Valuable solutions to the key technical problems are given, such as predicting the characteristics of retired LIBs with in-service data and building a fast sorting model from a small number of samples to sort large quantities of LIBs. Finally, the technological prospects of echelon utilization are discussed. Big data and artificial intelligence can be used to promote further development and application of echelon utilization, which may eventually be applied to managing the whole life cycle of LIBs.

Aiming at the current problem that thick sandwiches are commonly developed in domestic salt mines, this study investigated whether thick sandwiches would collapse to allow connection of salt deposits above and below, consequently expand single cavern capacity by laboratory experiments, numerical simulation and field tests. The analysis of water-soluble mechanism and changing patterns of mechanical parameters of sandwich layers after water immersion shows that: after water immersion, sandwiches dissolve into honeycomb structure with cracks that easily flake, and the residues occupy small space; after water immersion, mechanical strength of sandwiches decrease greatly, susceptible for failure and collapse. Based on the calculation model for critical limited span, the collapse timing was predicted. In addition, laws of thick sandwiches' collapse were obtained by numerical simulation, which indicate that the smaller thickness, as well as the larger depth, limited span and storage height of the sandwiches are more likely to cause the sandwich collapse. The design idea of “full immersion and secondary cavity construction” was proposed, and cavern leaching design for a 12 meter thick sandwich in a gas storage was made, and tests on two wells were conducted. The results prove that thick sandwiches could collapse, increasing leaching height and single cavity capacity, and the technology is technologically and economically feasible. Key words: salt-cavern storage, thick sandwich, water-soluble mechanism, mechanical parameter after water immersion, critical span, cavern leaching design, cavern leaching experiment

This paper evaluates the existing policy frameworks for mitigation of diffuse water pollution from agriculture (DWPA) in England and China. With reference to a conceptual model of the process of policy transfer or international lesson drawing, and possible constraints to this, it assesses whether and how China can draw lessons to improve current policy from the supra-national and national provisions of the EU and a member state that by 2016 had comprehensively implemented EU agricultural and environmental policy. DWPA is first analysed as a public policy challenge to inform specification of a generic framework for its mitigation. The current policy frameworks for mitigation of DWPA in England and China are evaluated, and their potential for improvement is assessed. A number of barriers to lesson drawing for regulation, incentive payments schemes and advice provision are diagnosed. These barriers are potentially least in relation to advice provision and its use to promote voluntary action by farmers. Given its structure and capabilities the public agricultural extension system in China is also recognised as a key resource. A focus on three policy approaches to mitigate DWPA in China is recommended: i) targeted regulation to a ‘reference level’ of large intensive livestock, and ultimately other large commercial farms; ii) strategic use of incentive payment schemes to protect water resources from DWPA; and iii) re-orientation of the ethos and modalities of operation of the extension system, informed by international lesson drawing, with the aim of rebalancing farm productivity and environmental protection.

Emergy analysis was performed to evaluate the sustainability of three rice farming systems (rice mono-cropping, rice-fish and rice-duck farming systems) in the Jiangsu province. The results showed that the three systems exhibit unique characteristics on each component. The emergy of rice seeding and the labour input were the most important costs, at 57.18 and 18.20 sej/ha/season, respectively, for the rice mono-cropping system. In the rice-fish and rice-duck systems, the feed input played a more important role, corresponding to values of 63.70 and 79.20 sej/ha/season, respectively. The machinery input was 45.21 sej/ha/season for the rice-fish system due to the construction of fish ponds and ditches. The rice-duck system exhibited lower environmental loading and a higher sustainability index than the rice mono-cropping and rice-fish systems, respectively. The ratios of the economic input to output were 0.41, 0.61 and 0.41 for the rice mono-cropping, rice-fish and rice-duck systems, respectively. The net profit of the rice-duck system was $3264.2 per hectare, which was nearly 40% higher than those of the other systems. Based on the results showing the highest economic efficiency and value on the sustainability index, the rice-duck system may be the optimal rice agriculture system from both ecological and economic perspectives.