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The prevalence of smartphones has promoted the popularity of mobile apps in recent years. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a Pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. The inter-diversity of app-category and individual app usage exhibits opposing trends: app-category usage assimilates while individual app usage diversifies. Nevertheless, the intra-diversity of both app-category and app usage declines over time. Also, we demonstrate the country barriers of app category usage. We further investigate how different demographics affect the evolutionary processes of app usage. Our study provides useful implications for app developers, market intermediaries, and service providers

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed., Comment: Accepted by IEEE Transactions on Big Data

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals' cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app usage. We first segment app usage traces into small time windows and then design a probabilistic topic model to infer users' cyber activities of each window. By exploring the coherence of users' activity sequences, the daily patterns of individuals are identified. Next, we recognize the common patterns across diverse groups of individuals using a hierarchical clustering algorithm. We then apply our framework on a large-scale and real-world dataset, consisting of 653,092 users with 971,818,946 usage records of 2,000 popular mobile apps. Our analysis shows that people usually obey yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. We also discover five common daily cyber activity patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. Our findings have profound implications on identifying the demographics of users and their lifestyles, habits, service requirements, and further detecting other disrupting trends such as working overtime and addiction to the game and social media.

An ultra-wide-angle frequency beam scanning leaky wave antenna (LWA) with high scanning rate based on spoof surface plasmon polaritons (SSPPs) waveguide is proposed in this communication. A row of elliptical patches is placed periodically near the SSPPs waveguide, which can shift the fundamental mode of SSPPs waveguide to the fast-wave region and radiate electromagnetic (EM) waves. The simulated results show that the proposed LWA achieves an ultra-wide scanning angle range of 172° over a narrow operation bandwidth of 9.8-12.28GHz, which implies a high scanning rate. The simulated average gain of the proposed LWA is 10.13dBi and its average total efficiency is 75.35%. The proposed LWA is fabricated and measured. The measured scanning angle range is 167° in the frequency band of 9.9-12.25GHz, and the measured average gain is 9.73dBi. Good agreements between the simulated and measured results are obtained, validating the proposed design.

A 41-year-old male patient attended the hospital suffering from chest tightness and intermittent chest pain. Through cardiac ultrasonography, a metallic foreign body was found in the right ventricle, accompanied by tricuspid insufficiency. Chest computed tomography scan further confirmed a foreign body stuck at the opening of the tricuspid valve. The 14 × 40mm stent was removed from the right ventricular through thoracotomy. One month ago, the patient received left renal venous stenting for treatment of Nutcracker Syndrome. Thus, for patients who have received left renal venous stenting for treatment of Nutcracker Syndrome, clinicians should pay due attention to stent-graft migration.

Unmanned aerial vehicle (UAV) multi-hop communication networks are foreseen to be widely employed in both military and civilian scenarios. However, in ultra-dense scenarios with swarm UAVs, nodes are highly dynamic mobile, ultra-dense deployment and non-centralized distribution. These characteristics make the centralized resource management policy not apply. Meanwhile, existing routing protocols can't meet the performance challenges of high dynamic, topology and link frequency changes of ultra-dense scenarios with swarm UAVs. To solve the above challenges of resource management and routing protocol, a cross-layer optimization method is presented with a novel mean field game (MFG) in this paper. It is based on the cross-layer design method of the MFG theory and jointly considers the power resources in the physical layer, frequency resources in the medium access control (MAC) layer, and routing resources in the network layer. By dividing into subproblems, the original problem is solved. Meanwhile, the optimal data transmission path can be selected through the management and allocation of frequency resources and power resources. A crosslayer resource management dynamic source routing (CLRMDSR) protocol is designed based on that which adds link quality measurement. The simulation results show that the presented CLRM-DSR with the proposed resource management scheme can improve the data packet transmission rate, reduce end-toend delay, and lower routing overhead for the multi-hop swarm UAV communication network.

To investigate if positive changes in the clinical performance of radiologists are associated with reading mammographic test sets.This study investigated the clinical audit history for a cohort of 39 participants in the BreastScreen Reader Assessment Strategy who have read for BreastScreen New South Wales in the period between 2010 and 2018, inclusively. Based on the year in which each radiologist completed his or her first test set, data of multiple clinical audit metrics from two calendar years before test-set reading were compared against similar data from three different periods after test-set completion. The same process was repeated after dividing radiologists into two subgroups based on their median screen-reading volume (3,688), to test if experience is a determinant of post-test set performance.On average, radiologists showed significant improvements (p0.05) in the recall rate for subsequent screening rounds, in positive predictive value 1 (PPV1), and in specificity. When dividing radiologists based on their average annual reading volume, radiologists with higher reading numbers demonstrated similar significant improvements in the recall rate and in PPV1. In addition, they showed significant improvements in the detection rates of invasive breast cancer and ductal carcinoma in situ (DCIS). In contrast, the radiologists with lower reading volume indicated significant changes in the recall rate and in PPV1, both accruing in one of the three compared periods.Mammographic test-set participants improve over time in identifying normal breast screens and detecting breast cancer in association with reading higher volumes of breast screening cases.

Hypersharpening (namely, hyperspectral (HS) and multispectral (MS) image fusion) aims at enhancing the spatial resolution of HS image via an auxiliary higher resolution MS image. Currently, numerous hypersharpening methods are proposed successively, among which the unmixing-based approaches have been widely researched and demonstrated their effectiveness in the spectral fidelity aspect. However, existing unmixing-based fusion methods substantially employ mathematical techniques to solve the spectral mixture model, without taking full advantage of the collaborative spatial-spectral information that is usually helpful for abundance estimation improvement. To overcome this drawback, in this article, a novel unmixing-based HS and MS image fusion method, via a convolutional neural network (CNN), is proposed to promote spectral fidelity. The main idea of this work is to use CNN to fully explore the spatial information and the spectral information of both HS and MS images simultaneously, thereby enhancing the accuracy of estimating the abundance maps. Experiments on four simulated and real remote sensing data sets demonstrate that the proposed method is beneficial to the spectral fidelity of the fused images compared with some state-of-the-art algorithms. Meanwhile, it is also easy to implement and has a certain advantage in running time.

Since big data becomes a main impetus to the next generation of IT industry, data privacy has received considerable attention in recent years. To deal with the privacy challenges, differential privacy has been widely discussed and related private mechanisms are proposed as privacy-enhancing techniques. However, with today's differential privacy techniques, it is difficult to generate a sanitized dataset that can suit every machine learning task. In order to adapt to various tasks and budgets, different kinds of privacy mechanisms have to be implemented, which inevitably incur enormous costs for computation and interaction. To this end, in this paper, we propose two novel schemes for outsourcing differential privacy. The first scheme efficiently achieves outsourcing differential privacy by using our preprocessing method and secure building blocks. To support the queries from multiple evaluators, we give the second scheme that employs a trusted execution environment to aggregately implement privacy mechanisms on multiple queries. During data publishing, our proposed schemes allow providers to go off-line after uploading their datasets, so that they achieve a low communication cost which is one of the critical requirements for a practical system. Finally, we report an experimental evaluation on UCI datasets, which confirms the effectiveness of our schemes.

With the rapid development of perovskite solar cells (PSCs), PSCs without electron transport layer (ETL) structure have been reported one after another, but the efficiency is low. In this work, a tin-based ETL-free structure of perovskite solar cell is introduced. In order to explore the internal influencing factors of the device with ETL-free structure, we used SCAPS-1D (Solar Cells Capacitance Simulator) to simulate the device and adjust the appropriate physical parameters to approximate the results reported in the experiment. The simulation results show that the thickness of the absorption layer, defect density, operating temperature and band gap have different effects on the performance of the device, and the best absorbent layer thickness is 600 nm. When the operating temperature exceeds 300 K, the performance parameters will decrease, and the band gap is best controlled at about 1.3 V. It should be pointed out that defect density is the main culprit that seriously affects device performance. When defect density exceeds 1015cm−3, power conversion efficiency (PCE) begins to decline significantly, and we need to pay more attention to defect density. At the same time, the effects of different interface defect layers (IDL) on device performance are simulated. It is found that when the defect density of the interface defect layer is too high, the asymmetry effect will appear. The interface defect layer IDL2 between the absorption layer and the hole transport layer (HTL) has a greater effect on the open circuit voltage (VOC), and the interface defect layer IDL1 has a greater effect on PCE. Therefore, it is necessary to pay attention to these defect layers to improve the VOC and PCE. Finally, based on ETL-free PSCs, we list HTL of different materials for reference, and provide new ideas for perovskite solar cells in the future.

The shared nature of the wireless medium induces contention between data transport and backward signaling, such as acknowledgment. The current way of TCP acknowledgment induces control overhead which is counter-productive for TCP performance especially in wireless local area network (WLAN) scenarios. In this paper, we present a new acknowledgment called TACK (``Tame ACK''), as well as its TCP implementation TCP-TACK. TACK seeks to minimize ACK frequency, which is exactly what is required by transport. TCP-TACK works on top of commodity WLAN, delivering high wireless transport goodput with minimal control overhead in the form of ACKs, without any hardware modification. Evaluation results show that TCP-TACK achieves significant advantages over legacy TCP in WLAN scenarios due to less contention between data packets and ACKs. Specifically, TCP-TACK reduces over 90% of ACKs and also obtains an improvement of up to 28% on goodput. A TACK-based protocol is a good replacement of the legacy TCP to compensate for scenarios where the acknowledgment overhead is non-negligible.

Inertial measurement units (IMUs)-based sensing systems have received extensive attention for wearable gait measurement due to their small size, low price, and high accuracy. However, reconstructing walking dynamics usually requires a large number of sensor units attached to major body segments, which is generally not suitable for daily-life scenarios. Previous studies showed that two shank-mounted IMUs can detect gait events and estimate many important gait parameters. However, it is unclear whether this simple two-IMU system can be used to reconstruct walking dynamics. In this paper, we propose a two-stage method for predicting walking dynamics from measurements of two shank-mounted IMUs. Displacements of ankle joints and shank angles are first estimated from the IMU outputs. Then a model-based whole-step optimization approach is used to solve the gait dynamics by tracking the estimated shank motion. The proposed method is validated with both normal and asymmetric walking data, achieving a root mean square error of 5.3, 6.1, and 6.8 degrees in the hip, knee, and ankle joint angle estimation and 3.9% and 12.2% body weight in the fore-aft and vertical ground reaction force estimation. Comparing with the reported results in literature, the number of IMUs is significantly reduced with similar accuracy. This implies that gait dynamic information may be estimated from very limited measurements and the inherent gait characteristics can be used to reconstruct gait dynamics from the motion of a small number of segments.

Unreinforced masonry (URM) structures exhibit high vulnerability in the moderate or strong earthquakes. The door- or window-type opening would further increase the seismic vulnerability, in which there is a weak area in the structures with stress concentrations. To improve the seismic behavior of perforated URM wall, a reinforcement of engineered cementitious composites (ECC) overlay was used on the both sides of the wall. Two 1/2-scale H-shaped specimens were prepared and tested under cyclic lateral loads. The damage patterns, hysteretic curves, envelope curves, stiffness degradation and energy dissipation of specimens were analyzed and discussed. The ECC effect can be observed as an increase of lateral resistance (maximum lateral force from 87.5 kN to 255.2 kN), and energy dissipation capacity (the cumulative energy dissipation up to ultimate state from 10.6 kJ to 21.5 kJ). Moreover, ECC overlay was able to control the crack initiation and propagation. The wide cracks along mortar joints observed in the spandrel of control specimen became multiple flexural cracks in the masonry piers after ECC reinforcement. The final failure of strengthened wall was dominated by the bending of piers, which was beneficial to improving the collapse resistance of masonry structures in the strong earthquake excitation.

Background. Young patients with type 2 diabetes mellitus (DM) and acute myocardial infarction (AMI) have high long-term all-cause and cardiovascular mortality rates. We aimed to investigate the differentially expressed genes (DEGs) that might be potential targets for DM patients with AMI. Methods. Gene datasets GSE775, GSE19322, and GSE97494 were meta-analyzed to obtain DEGs of the left ventricle myocardium in infarcted mice. Gene datasets including GSE3313, GSE10617, and GSE136948 were meta-analyzed to identify DEGs in diabetes mice. A Venn diagram was used to obtain the overlapping DEGs. KEGG and GO pathway analyses were performed, and hub genes were obtained. Pivotal miRNAs were predicted and validated using the miRNA dataset in GSE114695. To investigate the cardiac function of the screened genes, a MI mouse model was constructed; echocardiogram, qPCR, and ELISA of hub genes were performed; ELISA of hub genes in human blood samples was also utilized. Results. A total of 67 DEGs were identified, which may be potential biomarkers for patients with DM and AMI. GO and KEGG pathway analyses were performed, which were mainly enriched in response to organic cyclic compound and PI3K-Akt signaling pathway. The expression of PIK3R1 and SPNB2 increased in the MI group and was negatively correlated to left ventricular ejection fraction (LVEF), whereas that of CRYAB decreased and was positively correlated to LVEF. Patients with high CRYAB expression demonstrated a short hospital stay and the area under the curves of the three protein levels before and after treatment were 0.964, 0.982, and 0.918, suggesting that PIK3R1, SPNB2, and CRYAB may be diagnostic and prognostic biomarkers for the diabetes patients with AMI. Conclusion. The screened hub genes, PIK3R1, SPNB2, and CRYAB, were validated as credible molecular biomarkers and may provide a novel therapy for diabetic cardiac diseases with increased proteotoxic stress.

Background. Some postmarketing Chinese patent formulas have been widely used to treat atherosclerosis (AS) and play critical roles in Chinese healthcare. However, the usage of these herbs is yet controversial due to unclear effects and lack of understanding of the mechanism of action. With the modernization of traditional Chinese formulas, we are to elucidate the atheroprotective properties of these remedies from successful postmarketing experiments in vivo. Methods. In this systematic review, we critically searched the databases, applied stringent criteria, assessed the methodological quality, and examined the current evidence in vivo. Results. Consequently, 60 studies were included in the present qualitative synthesis. Data on models, high-fat diet, intervention time, outcome measures, efficacy, and mechanisms were collected. Finally, 23 formulas that could alleviate AS were correlated to the amelioration of plaques, improvement of plaque stability, modification of lipid level and lipid metabolism, and the effects of anti-inflammation and antioxidant stress with multiple components and targets. However, the methodological quality was low and incomplete among the included literature. Conclusions. Thus, taken together, the studies on postmarketing Chinese patent formulas would provide a novel approach to improve the treatment of AS, and rigorously designed studies would provide high-quality evidence.

INTRODUCTION In many western countries, there is good evidence documenting the performance of radiologists reading digital breast tomosynthesis (DBT) images. However, the diagnostic efficiency of Chinese radiologists using DBT, particularly type of errors being made and type of cancers being missed, is understudied. This study aims to investigate the pattern of diagnostic errors across different lesion types produced by Chinese radiologists diagnosing from DBT images. Australian radiologists will be used as a benchmark. METHODS Twelve Chinese radiologists read a DBT test set and located each perceived cancer lesion. True positives, false positives (FP), true negatives and false negatives (FN) were generated. The same test set was also read by 14 Australian radiologists. Z-scores and Pearson correlations were used to compare interpretation of lesions and identification of normal appearances between two groups of radiologists. RESULTS Architectural distortions (p

With the rapid development of society, video surveillance has progressively expanded into different areas of life, such as transportation, security inspection, banks. There are a large number of replaced and newly deployed cameras in fields such as safe cities, smart campuses and smart buildings, which leads to a huge amount of video data, slow retrieval speed in video examining, and low efficiency in understanding complete picture of videos. In this paper, we propose SurVizor, a visual analysis system to understand the key content of surveillance videos. We integrate multiple image features and employ time series analysis methods to explore key temporal patterns in the feature. We integrate multiple visualization views from three levels of video, feature, and frame to promote exploration, analysis and understanding of video content. We evaluate the proposed system through a case study based on real-world surveillance videos from multi-camera and a user study. The results demonstrate the usability and effectiveness of our system in analyzing and understanding the key content of surveillance videos.

To develop a deep learning algorithm based on automatic detection of landmarks that can be used to automatically calculate forefoot imaging parameters from radiographs and test its performance. A total of 1023 weight-bearing dorsoplantar (DP) radiographs were included. A total of 776 radiographs were used for training and verification of the model, and 247 radiographs were used for testing the performance of the model. The radiologists manually marked 18 landmarks on each image. By training our model to automatically label these landmarks, 4 imaging parameters commonly used for the diagnosis of hallux valgus could be measured, including the first–second intermetatarsal angle (IMA), hallux valgus angle (HVA), hallux interphalangeal angle (HIA), and distal metatarsal articular angle (DMAA). The reference standard was determined by the radiologists’ measurements. The percentage of correct key points (PCK), intragroup correlation coefficient (ICC), Pearson correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE) between the predicted value of the model and the reference standard were calculated. The Bland–Altman plot shows the mean difference and 95% LoA. The PCK was 84–99% at the 3-mm threshold. The correlation between the observed and predicted values of the four angles was high (ICC: 0.89–0.96, r: 0.81–0.97, RMSE: 3.76–6.77, MAE: 3.22–5.52). However, there was a systematic error between the model predicted value and the reference standard (the mean difference ranged from − 3.00 to − 5.08°, and the standard deviation ranged from 2.25 to 4.47°). Our model can accurately identify landmarks, but there is a certain amount of error in the angle measurement, which needs further improvement.

Background: Event log data generated in the software development process contains historical information and future trends in software development activities. The mining and analysis of event log data contribute to identify and discover software development activities and provide effective support for software development process mining and modeling. Methods: Firstly, a deep learning model (Word2vec) was used for feature extraction and vectorization of software development process event logs. Then, the K-means clustering algorithm and measure of silhouette coefficient and intra-cluster SSE were used for clustering and clustering effect evaluation of vectorized software development process event logs. Results: This paper obtained the mapping relationship between software development activities and events, and realized the identification and discovery of software development activities. Conclusion: Two practical software development projects (jEdit and Argouml) are given to prove the feasibility, rationality and effectiveness of our proposed method. This work provides effective support for software development process mining and software development behavior guidance.

Deep learning-based Automatic Modulation Recognition (AMR) can improve the recognition rate compared with traditional AMR methods. However, in practical applications, as training samples and real scenario signal samples have different distributions in practical applications, the recognition rate for target domain samples can deteriorate significantly. This paper proposed an unsupervised domain adaptation based AMR method, which can enhance the recognition performance by adopting labeled samples from the source domain and unlabeled samples from the target domain. The proposed method is validated through signal samples generated from the open-sourced Software Defined Radio (SDR) GNU Radio. The training dataset is composed of labeled samples in the source domain and unlabeled samples in the target domain. In the testing dataset, the samples are from the target domain to simulate the real scenario. Through the experiment, the proposed method has a recognition rate increase of about 88% under the CNN network structure and 91% under the ResNet network structure.

Background Fine-needle aspiration cytology (FNAC) is a basic diagnostic tool for thyroid nodules. However, 15–30% of nodules are cytologically indeterminate. Midkine (MK), a pleiotropic growth factor, is often upregulated in patients with cancers. This study aimed to evaluate the role of MK and its ratios in fine-needle aspirates (FNA) for predicting thyroid malignancy. Methods This retrospective study included patients with thyroid nodules who underwent preoperative FNA and/or thyroidectomy between April 2017 and September 2017. MK levels in FNA washout were measured by enzyme-linked immunosorbent assay, and thyroglobulin (TG) and free thyroxine (FT4) levels in FNA washout were measured by chemiluminescent immunometric assays. Results A total of 217 patients with 242 nodules were included in this study. The concentrations of TG, FT4, MK/TG, MK/FT4, and FT4/MK were significantly different between papillary thyroid carcinomas and benign thyroid nodules. Both MK/TG and MK/FT4 ratios were positively correlated with maximum tumor diameter, extrathyroidal extension, and T and N stages. The area under the curve for MK/TG was 0.719 with a cutoff value of 55.57 ng/mg, while the area under the curve for MK/FT4 was 0.677 with a cutoff value of 0.11 μg/pmol. FNAC in combination with MK/FT4 had a higher sensitivity (95% vs. 91%) and accuracy (96% vs. 92%) than FNAC alone for cytologically indeterminate specimens, those of unknown significance, or those suspected of malignancy. Conclusions MK/FT4 and MK/TG may have diagnostic utility for evaluation of papillary thyroid carcinomas, particularly for cytologically indeterminate thyroid nodules.

Background Alcohol consumption and smoking are the leading risk factors for laryngeal cancer (LC). Understanding the variations in disease burden of LC attributable to alcohol use and smoking is critical for LC prevention. Methods Disease burden data of LC were retrieved from the Global Burden of Disease Study 2019. We used estimated average percentage change (EAPC) to measure the temporal trends of the age-standardized mortality rate (ASMR) of LC. Results Globally, while the ASMR of LC decreased by 1.49% (95% CI, 1.41–1.57%) per year between 1990 and 2019, the number of deaths from LC has increased 41.0% to 123.4 thousand in 2019. In 2019, 19.4 and 63.5% of total LC-related deaths were attributable to alcohol use and smoking worldwide, respectively. The ASMR of alcohol- and smoking-related LC decreased by 1.78 and 1.93% per year, whereas the corresponding death number has increased 29.2 and 25.1% during this period, respectively. The decreasing trend was more pronounced in developed countries. In some developing countries, such as Guinea and Mongolia, the LC mortality has shown an unfavorable trend. Conclusion The ubiquitous decrease in LC mortality was largely attributed to the smoking control and highlighted the importance of smoking control policies. However, the disease burden of LC remained in increase and more effective strategies are needed to combat the global increase of alcohol consumption.

Purpose The occurrence, progression, invasion and metastasis of tumors depend on a tumor vascular network. Vascular disrupting agents (VDAs) are a new class of drugs targeting the tumor vasculature, by blocking the existing tumor blood vessels. However, there is no clear consensus on the clinical efficacy of tumor vascular disrupting therapy. In this study, we performed the first systematic review and meta-analysis of published clinical trials focused on tumor vascular disrupting therapies. Materials and methods We searched PubMed, EMBASE, and the Cochrane Library to identify clinical trials that used VDAs to treat tumors. After literature screening and data extraction, according to inclusion and exclusion labels, meta-analysis was performed using RevMan5.3 software. Results In this meta-analysis, we included 2659 patients from eight randomized controlled trials involving non-small-cell lung cancer, prostate, epithelial ovarian, fallopian tube, and primary peritoneal carcinoma. Compared with the control arm, the experimental arm exhibited an effective improvement of 0.5-year and 1-year survival, as well as the 6-month progression-free survival rate. There was no significant difference between patients in the experimental compared to the control arm with respect to objective response and disease control rates, and 12-month progression-free survival. Conclusion Vascular disrupting therapy can effectively prolong the survival of cancer patients. However, for indicators of short-term efficacy, such as objective response rate and disease control rate, there is still a lack of high-quality, large-scale clinical trial data to confirm the effectiveness of VDAs.

In the field of artificial intelligence, classification algorithms tend to be biased toward the majority class samples when encountering imbalanced data , resulting in low recognition rates for minority class samples. Undersampling techniques address this issue by decreasing the number of majority class samples to balance the original data distribution before the dataset is learned. However, current clustering-based undersampling methods have limitations that directly affect the original imbalanced dataset and the final classification performance. To address these problems, we propose a novel three-stage undersampling framework with denoising, fuzzy c-means clustering, and representative sample selection (UFFDFR). This framework improves the classification performance on imbalanced data by removing noise and unrepresentative samples from the majority class. Experiments on 15 different imbalanced datasets demonstrate that UFFDFR effectively removed noise and unrepresentative majority class samples and improved classification performance. Furthermore, UFFDFR outperformed three classic and three state-of-the-art clustering-based undersampling methods in terms F-measure, G-mean, and AUC for five classification algorithms, which was confirmed by the Friedman and Nemenyi post-hoc statistical tests.

AIM The aim of the study was to examine the cross-sectional association between serum albumin and hyperuricemia (HU). MATERIALS AND METHODS HU was defined as uric acid ≥ 420 umol/L for the male population and ≥ 357 umol/Lfor the female population. We reviewed the files of 216 consecutive patients with idiopathic membranous nephropathy treated at our hospital between 2010 and 2019. The correlation of serum albumin with hyperuricemia and the association between serum uric acid levels and the clinical of Idiopathic membranous nephropathy were assessed by statistical analysis. A multivariable logistic analysis model was applied to test the association after adjusting for a number of potential confounding factors. RESULTS Triglyceride and serum albumin were higher in the group with hyperuricemia than in the group without hyperuricemia (p = 0.020, p < 0.001, respectively). As serum albumin rose to 1 g/L, the probability for hyperuricemia increased to 17% (adjusted II OR = 1.17, 95% CI (1.02, 1.35), p = 0.0294). A unit increase in serum albumin was associated with an increase of 6.64 g/L in uric acid (adjusted II β = 6.64, p = 0.0135). Using tertile 1 (T1) for reference, the tertile 3 (T3) group was positively associated with both hyperuricemia (adjusted II OR = 44.21, 95% CI (12.76, 75.67), p = 0.0064) and uric acid (adjusted II β = 98.64, p = 0.0116). The interaction test showed significant interactions between serum albumin and the body mass index (BMI) when hyperuricemia or uric acid were used to determine the outcomes. The participants with a BMI ≥ 25 kg/m2 had a higher OR between serum albumin and hyperuricemia and had a higher β between serum albumin and uric acid than those with a BMI ≥ 25 kg/m2 (for hyperuricemia: OR = 1.01 vs. 1.18, p for interaction = 0.0056; for uric acid: β = 0.96 vs. 6.23, p for interaction = 0.0154). The area under the ROC curve (AUC) was 0.7615 in the participants with a BMI ≥ 25 kg/m2. The sensitivity and specificity of this point were 47.37% and 95.83%, respectively. CONCLUSION Our study showed that serum albumin was positively associated with hyperuricemia and uric acid, especially in obese subjects.

Ancient Chinese military castles were equipped with rapid transportation routes for mutual aid, and this was an essential indicator of regional defense capability. However, since the sites of these transportation routes have mostly disappeared, it is not easy to examine the actual distribution of these routes. It is necessary to speculate the trend and position of military routes on the basis of the castle locations. In this study, the geographic features of each castle location were extracted as factors affecting the efficiency of the intercastle transportation system using the ArcGIS cost path function. By analyzing the fit of each factor for screening and weight assignment, a time cost path was established, and a model was generated for calculating the efficiency of this transportation system. The Weihai area, a typical representation of sea defense during the Chinese Ming Dynasty, was taken as an example for simulation. Overall, five ancient military transportation routes were restored. The establishment of the Ming Dynasty Wendengying transformed the linear defense layout of the Weihai region into a longitudinal network layout, and its site selection was of great benefit to the overall defense of the coastal citadel of Weihai. This model breaks the traditional limitations of relying on subjective speculation for ancient road restoration and dramatically improves its accuracy and credibility. Moreover, it makes a significant contribution to judging the road systems of ancient cities in different regions and provides a new idea to quantify the efficiency of ancient castle defenses.

Urban rail transit stations are the key nodes of urban rail transit network. Identifying and analyzing land use characteristics around urban rail transit stations can significantly contribute to urban rail transportation operation and management. Therefore, a visualization method of land use characteristics around urban rail transit stations based on POI is proposed in this paper. In the proposed method, first, the Voronoi diagram is used to determine coverage of urban rail transit stations and each POI is put in a coverage area based on their physical location. Then, topic-oriented hierarchical POIs of each urban rail transit station are extracted based on skyline idea. Finally, the land use characteristics around an urban rail transit station are visualized based on the extracted hierarchical POIs. We carried out two case studies and a quality evaluation. By using realistic data from Beijing rail transit in order to validate the method proposed in this paper. Results show that our method can clarify various situations of land use of urban rail transit stations and may provide support for the application of transportation model technology.

Table-to-Text generation aims to generate descriptions which can be viewed as a set of field-value records for factual tables. Despite the significant progress, the state-of-the-art models suffer from two major issues: Nonfluency and Divergence. Nonfluency means descriptions generated by models are not as fluent as those generated by humans, and thus can be distinguished easily. Divergence refers to the fact that the generated sentences contain information which can not be concluded from factual tables. This could be attributed to that most neural models are trained with the Maximum Likelihood Estimation (MLE) loss and use divergence-contained references as the ground truth, which forces the models to learn what cannot be inferred from the source to some extent. Motivated by the limitations of current models, we propose a novel GAN-based model with adversarial learning mechanism, which simultaneously trains a generative model G and a discriminative model D, to address Nonfluency and Divergence issues in Table-to-Text generation. Specifically, we build the generator G as an agent of reinforcement learning with a sequence-to-sequence architecture, which takes the raw data as input and predicts the generated sentences. Meanwhile, we build the discriminator D with a Convolutional Neural Network (CNN) to calculate rewards to measure the fluency of generations. To judge the fidelity of generations with regard to the original table more accurately, we also calculate the rewards from BLEU-Table. With the fusion rewards from CNN and BLEU-Table, our methods outperform the baselines by a large margin on the WikiBio and Wiki3C benchmarks evaluated with BLEU, ROURGE, and PARENT. Specifically, our models achieve 49.0 (BLEU-4), 37.8 (ROUGE-4) and 45.4 (PARENT) on WikiBio, as well as 12.9 (BLEU-4) and 6.9 (ROUGE-4) on Wiki3C. More importantly, we construct a new Wiki3C dataset that improves the insufficiency of datasets and promote the progress in Table-to-Text generation.

The range query on encrypted databases is usually implemented using the order-preserving encryption (OPE) technique which preserves the order of plaintexts. Since the frequency leakage of plaintexts makes OPE vulnerable to frequency-analyzing attacks, some frequency-hiding order-preserving encryption (FH-OPE) schemes are proposed. However, existing FH-OPE schemes require either the large client storage of size O ( n ) or O (log n ) rounds of interactions for each query, where n is the total number of plaintexts. To this end, we propose a FH-OPE scheme that achieves the small client storage without additional client-server interactions. In detail, our scheme achieves O ( N ) client storage and 1 interaction per query, where N is the number of distinct plaintexts and N ≤ n . Especially, our scheme has a remarkable performance when N ≪ n . Moreover, we design a new coding tree for producing the order-preserving encoding which indicates the order of each ciphertext in the database. The coding strategy of our coding tree ensures that encodings update in the low frequency when inserting new ciphertexts. Experimental results show that the single round interaction and low-frequency encoding updates make our scheme more efficient than previous FH-OPE schemes.

Coronavirus disease 2019 (COVID-19) radically modified the organization of healthcare systems with shutdown of routine activities and outpatient clinics. Herein, we report our institutional experience with a Telemonitoring and Care Program (TC-Program) to monitor and support left ventricular assist device (LVAD) patients during COVID-19 outbreak. This single-arm cohort study analyzed 156 patients who entered the TC-Program at our institution between April and August 2020. The TC-Program was based on routine phone calls to patients and a 24/7 emergency line. In November 2020, patients were asked for feedback on the TC-Program and checked for survival, transplant, or explant. The primary endpoint was the rate of TC-Program-driven interventions. Patients (males: 82.8%) were 61 years old (interquartile range [IQR]: 53.0-67.5) and on LVAD support for 1,266 days (IQR: 475-2,211). Patients were included in the TC-Program for a median time of 99 days (min:15, max:120) and received a median number of six phone calls (min:1, max:14). Twenty-three patients (14.7%) were referred for clinical evaluation after phone contact. Two patients (1.27%) were diagnosed with COVID-19: one of them died after intensive care, and one remained paucisymptomatic and recovered. Three patients asked to exit the program considering it not useful while the others gave high rates in terms of usefulness (median: 9, IQR: 8-10), information (median: 9, IQR: 8-10), good medical care (median: 9, IQR: 8-10), and psychologic support (median: 8, IQR: 7-10). A TC-Program based on the four ICSA principles (Inform, Care, Support, and Adapt) is feasible in LVAD patients and can be rapidly implemented during the COVID-19 pandemic.

Background To research the first-order features of apparent diffusion coefficient (ADC) values on diffusion-weighted magnetic resonance imaging (DWI) in maxillofacial malignant mesenchymal tumours. Methods The clinical data of 12 patients with rare malignant mesenchymal tumours of the maxillofacial region (6 cases of sarcoma and 6 cases of lymphoma) treated in the hospital from May 2018 to June 2020 and were confirmed by postoperative pathology were retrospectively analyzed. The patients were all examined by 1.5T magnetic resonance imaging. PyRadiomics were used to extract radiomics imaging first-order features. Group differences in quantitative variables were examined using independent-samples t-tests. Results The voxels number of ADCmean and ADCmedian of sarcoma tissues were 44.9124 and 44.2064, respectively, significantly higher than those in lymphoma tissues (ADCmean (− 68.8379) and ADCmedian (− 74.0045)), the difference considered statistically significant, so do the ADCkurt and ADCskew. Conclusions The statistical difference of ADCmean and ADCmedian is significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of twelve cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADCkurt combined ADCskew may improve the diagnosis level.

A novel integration strategy of microstrip antenna and metasurface is proposed to achieve low-radar cross section (RCS) and improved radiation performance simultaneously. Aimed at the applications in stealth cylindrical carrier, the antenna has a circular configuration, and the metasurface cells are closely arranged around the radiation patch in a concentric ring-type manner. Moreover, the mode analysis method is fully used to analyze the antenna’s working mechanism and guide the optimization process. Owing to the addition of metallic vias, the original quasi-TM30 mode is altered to three series-TM10 modes and thus enhanced gain is achieved. Interestingly, the addition of vias also generates a new resonance which widen the antenna’s impedance bandwidth. In another aspect, both in-band and out-of-band RCS reductions are obtained simultaneously. Finally, two prototypes are fabricated to validate the proposed design strategy. Good agreements between measurements and simulations are obtained. Measured results show that the gain of the proposed antenna is at least 3 dB higher than the conventional antenna in the entire operating band, and obvious RCS reduction is achieved from 4.0 to 8.5 GHz concurrently.

Software is rapidly evolving and operates in a changing environment; therefore, in addition to software design and testing, it is essential to observe and understand the software execution behavior by modeling data recorded during the execution of the software to improve its reliability. The nested call relationship between methods during the execution of software is common, but most process-mining methods are unable to discover them, only generating flat models with low fitness. Meanwhile, it is easy to generate “spaghetti-like” models with low comprehensibility when dealing with complex software execution data. This paper proposes a component-based hierarchical software behavior model discovery method that can discover hierarchical nested call structures during software runtime, improving the fitness of the model; meanwhile, the proposed method partitions the discovery model into several parts by component information to improve the comprehensibility of the model, which can also reflect the interaction behavior within and between components. The proposed approach was implemented in a process mining toolkit. Using real-life software event logs and public datasets, we demonstrated that compared with other advanced process mining techniques, our approach can visualize actual software execution behavior in a more accurate and easy-to-understand way while balancing time performance.

The present study aimed to construct a novel methylation-related prognostic model based on microsatellite status that may enhance the prognosis of colorectal cancer (CRC) from methylation and microsatellite status perspective. DNA methylation and mRNA expression data with clinical information were downloaded from The Cancer Genome Atlas (TCGA) data set. The samples were divided into microsatellite stability and microsatellite instability group, and CIBERSORT was used to assess the immune cell infiltration characteristics. After identifying the differentially methylated genes and differentially expression genes using R packages, the methylation-driven genes were further identified. Prognostic genes that were used to establish the methylation-related risk score model were generated by the univariate and multivariate Cox regression model. Finally, we established and evaluated the methylation-related prognostic model for CRC patients. A total of 69 MDGs were obtained and three of these genes (MIOX, TH, DKFZP434K028) were selected to construct the prognostic model. Patients in the low-risk score group had a conspicuously better overall survival than those in the high-risk score group (p < .0001). The area under the receiver operating characteristic curve for this model was 0.689 at 3 years, 0.674 at 4 years, and 0.658 at 5 years. The Wilcoxon test showed that higher risk score was associated with higher T stage (p = .01), N stages (p = .0028), metastasis (p = .013), and advanced pathological stage (p = .0013). However, the more instability of microsatellite status, the lower risk score of CRC patients (p = .0048). Our constructed methylation-related prognostic model based on microsatellite status presents potential significance in assessing recurrence risk stratification, tumor staging, and immunotherapy for CRC patients.

Crowd counting aims to count the number of pedestrians in an image or a video. Currently, scale variations in crowd counting are inevitable and challenging in practice. Besides, for the crowd counting task, there is only a small amount of annotated data available. It can be seen from previous methods of crowd counting and object detection that the two tasks have similar attention areas. However, existing methods in crowd counting generally ignore the similarities and specialties between the crowd counting task and the object detection task. In this paper, in order to solve the above challenges, we propose an adversarial scale-adaptive neural network (ASANet), consisting of three branches. First, a private branch for the crowd counting task concentrates on generating high-quality density maps. Second, another private branch for the object detection task aims to correctly detect and recognize objects. Third, we design a common branch to learn the similar attention area of the two tasks and assist crowd counting. Experimental results demonstrate an outstanding performance of the ASANet over state-of-the-art methods on three public datasets (ShanghaiTech, UCF_CC_50, and UCF_QNRF).

The present paper provides insights into the feasibility of using hydrogen and bioethanol blends as energy carriers in the foreseeable future upon discussions on the advantages and the disadvantages. The comprehensive overviews on the production, storage, and transportation of hydrogen and bioethanol have been made; and the current problems and potential solutions for the three stages have been summarized. Finally, the prospections on hydrogen and bioethanol could be expect optimistically.

To explore the treatment of pediatric hepatic hemangioma and the role of the Hisense computer-assisted surgery (Hisense CAS) system in diagnosis and treatment. We collected the clinical and follow-up data of all pediatric hepatic hemangioma cases in our pediatric surgery department from March 2008 to March 2021 for retrospective analysis. The Hisense CAS system was used to create three-dimensional (3D) reconstructions based on computed tomography data. There were 71 patients, mainly infants (prenatal to 39 months). There were more males than females (42 vs. 29), and the alpha-fetoprotein level was significantly increased in 8 cases. 3D reconstruction by the Hisense CAS system showed that hepatic artery tracking was helpful for the differential diagnosis of pediatric hepatic hemangioma. Twenty-three children treated with propranolol showed significant differences in the tumour diameter, volume, and tumour-to-liver volume ratio after treatment (all P

BACKGROUND A critically ill coronavirus disease 2019 (COVID-19) patient complicated by acute respiratory distress syndrome is reported. The patient survived following treatment with awake veno-venous extracorporeal membrane oxygenation (ECMO). CASE SUMMARY A 53-year-old male patient attended our hospital following a cough for 11 d and fever for 9 d. According to his computed tomography (CT) scan and real-time reverse transcription–polymerase chain reaction assay of a throat swab, nucleic acid was positive, confirming that he had COVID-19. He was subsequently transferred to the intensive care unit due to respiratory failure. The patient received antiviral drugs, a small dose of glucocorticoid, and respiratory support, including mechanical ventilation, but the treatment effect was poor. On the 28th day after admission, veno-venous ECMO and prone position ventilation (PPV) were performed, combined with awake ECMO and other comprehensive rehabilitation measures. On the 17th day of ECMO, the patient started to improve and his chest CT and lung compliance improved. ECMO was discontinued after 27 days, and mechanical ventilation was also discontinued after 9 days. The patient was then transferred to the rehabilitation department. CONCLUSION COVID-19 can damage lung tissues and cause evident inflammatory exudation, thus affecting oxygenation function. Awake ECMO, PPV, and comprehensive rehabilitation are effective in patients with critical COVID-19 and respiratory failure.

Necroptosis, a novel programmed cell death, plays a critical role in the development of fibrosis, yet its role in atrial fibrillation (AF) remains elusive. Mounting evidence demonstrates that aerobic exercise improves AF‐related symptoms and quality of life. Therefore, we explored the role of necroptosis in AF pathogenesis and exercise‐conferred cardioprotection. A mouse AF model was established either by calcium chloride and acetylcholine (CaCl2‐Ach) administration for 3 weeks or high‐fat diet (HFD) feeding for 12 weeks, whereas swim training was conducted 60 min/day, for 3‐week duration. AF susceptibility, heart morphology and function and atrial fibrosis were assessed by electrophysiological examinations, echocardiography and Masson's trichrome staining, respectively. Both CaCl2‐Ach administration and HFD feeding significantly enhanced AF susceptibility (including frequency and duration of episodes), left atrial enlargement and fibrosis. Moreover, protein levels of necroptotic signaling (receptor‐interacting protein kinase 1, receptor‐interacting protein kinase 3, mixed lineage kinase domain‐like protein and calcium/calmodulin‐dependent protein kinase II or their phosphorylated forms) were markedly elevated in the atria of AF mice. However, inhibiting necroptosis with necrostatin‐1 partly attenuated CaCl2‐Ach (or HFD)‐induced fibrosis and AF susceptibility, implicating necroptosis as contributing to AF pathogenesis. Finally, we found 3‐week swim training inhibited necroptotic signaling, consequently decreasing CaCl2‐Ach‐induced AF susceptibility and atrial structural remodeling. Our findings identify necroptosis as a novel mechanism in AF pathogenesis and highlight that aerobic exercise may confer benefits on AF via inhibiting cardiac necroptosis.

The demand together with the urgency of phosphogypsum (PG) treatment will pose significant challenges for many countries. This research aims to explore the research progress of PG, including basic status, cooperation situation, research fields, and development trends, based on the Web of Science database through bibliometric analysis of publications (articles and patents) from 1990 to 2020. The results show that academic research on PG originated early, but the number of patents grew quickly. China is a global leader in terms of the number of publications and plays a significant role in international cooperation. The knowledge of PG has remained concentrated in the fields of natural radioactivity, cement paste backfilling, soil, crystal morphology, and synthetic gas. However, academic hotspots focus on the microstructure of chemical processes and various environmental impacts; patents and hot technologies are based on the production of refractory materials, ceramics, surface materials, cement mortar, and composite materials. The academic frontiers of PG will be centered on exploiting the methods of recovering rare earth elements from PG, the conditions of ion solidification/stabilization in PG, the impact of reaction conditions on product quality, and the reaction mechanism at the micro-level. The frontiers of patents need to focus on the improvement of manufacturing equipment, new wall materials, and chemically modified polymer materials. Envisaging the number of articles and patents to be published in the future, architectural research has a large room for improvement. This paper conducts an in-depth analysis of PG and provides information on the technological development prospects and opportunities, which is helpful for researchers engaged in PG management.

BACKGROUND Retroperitoneal hemorrhage (RPH) is a rare and severe complication in patients undergoing extracorporeal membrane oxygenation (ECMO). Clinical diagnosis is difficult. CASE SUMMARY Three cases of RPH patients with corona virus disease-19 (COVID-19) were included in this study. All three suffered from respiratory failure, were treated with veno-venous or veno-arterial-venous ECMO, and experienced RPH during ECMO treatment. Two of the COVID-19 cases were diagnosed after the patients experienced abdominal pain. The other patient exhibited decreases in the ECMO circuit flow rate and hemoglobin level. Two cases were treated by transcatheter arterial embolization, and one was treated conservatively. The hemorrhage in each of the three cases did not deteriorate. Satisfactory treatment results were achieved for the three patients because of prompt diagnosis and treatment. CONCLUSION Although the incidence of RPH during ECMO treatment is low, the risk is increased by anticoagulant use and local mechanical injury. If declines in blood flow velocity and hemoglobin are detected, RPH should be considered, and prompt aggressive therapy should be started.

Background Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. Methods Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset. Results A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion. Conclusions Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.

Background and Objectives: Cardiovascular diseases (CVDs) are associated with a heavy health burden globally. Although there are several studies and reviews with a focus on the effects of ambient particulate matter on CVDs, presently, review of the association between particulate matter components and cardiovascular biomarkers has not been reported. Therefore, in this study, we reviewed the effects of particulate matter exposure on the levels of cardiovascular biomarkers. Methods: PubMed, Embase, and Web of Science databases were searched for related studies published between January 1, 2010, and May 30, 2021, using keywords, including particle, particulate, constituent, component, composition, cardiovascular biomarker, inflammation, oxidative stress, coagulation vasoactivity, and lipid metabolism. Results: Ten studies, which met the inclusion criteria, highlighted the existence of significant associations between particulate matter components and the levels of cardiovascular biomarkers, including lipid, inflammation and coagulation biomarkers, etc. However, multicenter studies evidence regarding the effects of long-term exposure to particulate matter components on cardiovascular biomarkers is still lacking. Further, studies with a focus on proteomics, and metabolomics of cardiovascular biomarkers owing to particulate matter exposure are also scarce. Conclusions: Exposure to particulate matter components was found to be significantly associated with cardiovascular biomarkers. However, in future, it would be necessary to conduct multicenter studies on the effects of long-term exposure to particulate components on the levels of cardiovascular biomarkers.

PurposeThis study aims to examine the influence of task interdependence on team members’ Moqi in virtual teams in China. The authors also aim to identify virtual collaboration as a mediator and distributive justice climate as a moderator in this relationship.Design/methodology/approachThe data were collected from a sample of 87 virtual teams (including 349 individuals) from various Chinese companies through a three-wave survey. Hierarchical regression analysis, path analysis, bootstrapping method and multiple validity tests were used to examine the research model.FindingsIn virtual teams in China, task interdependence has a significantly positive influence on team members’ Moqi; Virtual collaboration mediates the relationship between task interdependence and team members’ Moqi; The distributive justice climate positively moderates the relationship between task interdependence and virtual collaboration, as well as the indirect effect of virtual collaboration on the relationship between task interdependence and team members’ Moqi.Practical implicationsIn virtual teams, leaders can facilitate team members’ Moqi by designing highly interdependent tasks, encouraging team members to engage in virtual collaboration and cultivating a climate of high attention distributive justice.Originality/valueThis is one of the first studies to pay to the Moqi among team members rather than supervisor-subordinate relationships and further examine how team members’ Moqi is predicted by task interdependence via the mediation of virtual collaboration with the distributive justice climate playing a moderating role.

With the improvement of public security awareness, video anomaly detection has become an indispensable demand in surveillance videos. To improve the accuracy of video anomaly detection, this paper proposes a novel two-stream spatial-temporal architecture called Two-Stream Deep Spatial-Temporal Auto-Encoder (Two-Stream DSTAE), which is composed of a spatial stream DSTAE and a temporal stream DSTAE. Firstly, the spatial stream extracts appearance characteristics whereas the temporal stream extracts the motion patterns, respectively. Then, based on the novel policy joint reconstruction error, this model fuses the spatial stream and the temporal stream to extract spatial-temporal characteristics to detect anomalies. Furthermore, since the optical flow is invariant to appearances such as color or light, we introduce optical flow to enhance the capability of extracting continuity between adjacent frames and inter-frame motion information. We demonstrate the accuracy of the proposed method on the publicly available standard datasets: UCSD, Avenue and UMN datasets. Our experiments demonstrate high accuracy, which is superior to the state-of-the-art methods.

An intra-cavitary left ventricular (LV) thrombus is a relative contraindication to LV assist device (LVAD) implantation based on increased thromboembolic risks. Herein, we present our experience with LVAD patients with or without preoperative diagnosis of LV-thrombus. We retrospectively investigated 563 patients who received LVAD implantation between 2004 and 2018. Diagnosis of LV-thrombus was verified with computed tomography scan, magnetic resonance imaging, echocardiography, or intraoperative LV inspection. The primary endpoint was 30-day survival free of stroke and pump thrombosis. Overall, 72 patients (12.8%) had a diagnosis of LV-thrombus. They were younger (51 years; IQR:41-59), affected by severely reduced ejection fraction (15%; IQR:10-20), more often presenting with dilated cardiomyopathy (61.8%) and INTERMACS profile 1 (33.3%). Preoperative atrial fibrillation was frequent in patients without LV-thrombus (38.9%). Conventional sternotomy was the preferred approach in LV-thrombus patients (77.8%), based on more HMII implantations in these patients (41.7%). Survival free of strokes and pump thrombosis at 30 days was comparable (P = .5751) between patients with (83.3%) or without LV-thrombus (80.9%). LVAD implantation in patients with preoperative LV-thrombus is safe and feasible. When managed through correct diagnostic and intraoperative strategies including accurate inspection of the LV cavity, these patients show similar 30-day outcomes compared to patients without LV-thrombus.

Acetyl fentanyl (AF) is a Schedule I fentanyl analog that has been increasingly seen in heroin and fentanyl polydrug toxicity overdoses in Michigan (MI). Drug users are often unaware of the presence of AF in their drugs because it is often sold mixed into or disguised as heroin. High levels of AF in heroin drug products can cause increased incidence of overdose. This article describes data from a longitudinal opioid surveillance program and details 102 decedents in MI who were found to have evidence of heroin in their postmortem blood. A large portion of these decedents were also found to have evidence of fentanyl and AF. Our data further show significant overlap in incidence rates of AF and heroin-related overdose deaths in several MI counties, suggesting that AF is becoming enmeshed in heroin trafficking. Furthermore, we report unprecedented high incidence rates of AF and heroin-related overdose deaths in Calhoun county, and we propose that it is a high-intensity drug trafficking area. Highways US-131 and US-31 are likely used to transport these drugs. More study is needed into the drug trafficking trends in MI to ascertain drug sources and monitor the ever developing and dangerous polydrug heroin combinations.

Background: Numerous therapies have been developed for the treatment of chronic pelvic pain (CPP). Oxygen-ozone therapy is a new method for the treatment of CPP. Objectives: This article evaluated the feasibility of ultrasound-guided peritoneal perfusion with ozone in patients with CPP. Study Design: This is a bicenter retrospective study. Setting: The study was conducted at 2 pain centers of a university hospital. Methods: The medical records of patients with CPP (n = 60) from March 2016 until October 2018 were collected and reviewed. Group A contained 19 patients who were treated with a 1500 mcg dose of ozonated water (10 mcg/mL concentration and 150 mL volume), group B contained 23 patients using the same dose of ozonated water but a 15 mcg/mL concentration and 100 mL volume. Group C included 18 patients using a similar ozone dose but delivered in an oxygen-ozone mixture (15 mcg/mL concentration and 100 mL volume oxygen-ozone mixture). Visual Analog Scale (VAS) scores for pain of the 3 groups were compared at pretreatment, posttreatment, 1, 3, and 6 months posttreatment. The injection pain was evaluated using a 4-point verbal rating scale. Quality of life (QoL), anxiety, and depression were assessed at pretreatment and at 6 months posttreatment. Results: The VAS scores of the 3 groups decreased over time following treatment. Group A showed much higher pain scores compared with groups B and C at 1, 3, and 6 months posttreatment. However, the injection pain for groups B and C was higher than group A, but there was no difference seen between group B and C. At 6 months posttreatment, the QoL for all patients improved compared with pretreatment, whereas the anxiety and depression did not demonstrate differences. Limitations: The main limitations of this study are the retrospective study design, limited case number, and short follow-up period. Conclusions: Ultrasound-guided peritoneal perfusion with ozone is a feasible therapy for patients with CPP. Key words: Chronic pelvic pain, ozone, peritoneal perfusion