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The clinical application of doxorubicin (DOX) in cancer chemotherapy is limited by its life-threatening cardiotoxic effects. Chrysophanol (CHR), an anthraquinone compound isolated from the rhizome of Rheum palmatum L., is considered to play a broad role in a variety of biological processes. However, the effects of CHR׳s cardioprotection in DOX-induced cardiomyopathy is poorly understood. In this study, we found that the cardiac apoptosis, mitochondrial injury and cellular PARylation levels were significantly increased in H9C2 cells treated by Dox, while these effects were suppressed by CHR. Similar results were observed when PARP1 activity was suppressed by its inhibitors 3-aminobenzamide (3AB) and ABT888. Ectopic expression of PARP1 effectively blocked this CHR׳s cardioprotection against DOX-induced cardiomyocyte injury in H9C2 cells. Furthermore, pre-administration with both CHR and 3AB relieved DOX-induced cardiac apoptosis, mitochondrial impairment and heart dysfunction in Sprague–Dawley rat model. These results revealed that CHR protects against DOX-induced cardiotoxicity by suppressing cellular PARylation and provided critical evidence that PARylation may be a novel target for DOX-induced cardiomyopathy. CHR protected against doxorubicin (DOX)-induced cardiomyocytes apoptosis, mitochondrial injury and heart dysfunction by suppressing cellular PARylation in vitro and in vivo. fx1 • CHR protected against DOX-induced apoptosis, mitochondrial injury and heart dysfunction in vivo and in vitro. • PARP1 inhibition significantly suppressed DOX-induced cardiomyocyte injury. • Ectopic expression of PARP1 blocked the CHR's cardioprotection. [ABSTRACT FROM AUTHOR]

To improve the performance of ensemble techniques for temporal data clustering, we propose a novel bi-weighted ensemble in this paper to solve the initialization and automated model selection problems encountered by all HMM-based clustering techniques and their applications. Our proposed ensemble features in a bi-weighting scheme in the process of examining each partition and optimizing consensus function on these input partitions in accordance with their level of importance. Within our proposed scheme, the multiple partitions, generated by HMM-based K-models under different initializations, are optimally re-consolidated into a representation of bi-weighted hypergraph, and the final consensus partition is generated and optimized via the agglomerative clustering algorithm in association with a dendrogram-based similarity partitioning (DSPA). In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive survey of privacy-preserving schemes for smart grid communications. Specifically, we select and in-detail examine thirty-two privacy preserving schemes developed for or applied in the context of smart grids. Based on both communication and system models, we classify these schemes that are published between 2013 and 2017, in five categories, including, (1) smart grid with advanced metering infrastructure, (2) data aggregation, (3) smart grid marketing architecture, (4) smart community of home gateways, and (5) vehicle-to grid architecture. For each scheme, we survey attacks of leaking privacy, countermeasures, and game theoretic approaches. In addition, we review survey articles published in the recent years that deal with smart grids communications, applications, standardization, and security. Based on the current survey, several recommendations for further research are discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

Modern Supervisory Control and Data Acquisition (SCADA) systems used by the electric utility industry to monitor and control electric power generation, transmission and distribution are recognized today as critical components of the electric power delivery infrastructure. SCADA systems are large, complex and incorporate increasing numbers of widely distributed components. The presence of a real time intrusion detection mechanism, which can cope with different types of attacks, is of great importance in order to defend a system against cyber attacks. This defense mechanism must be distributed, cheap and above all accurate, since false positive alarms or mistakes regarding the origin of the intrusion mean severe costs for the system. Recently an integrated detection mechanism, namely IT-OCSVM , was proposed, which is distributed in a SCADA network as a part of a distributed intrusion detection system (DIDS), providing accurate data about the origin and the time of an intrusion. In this paper we also analyze the architecture of the integrated detection mechanism and we perform extensive simulations based on real cyber attacks in a small SCADA testbed in order to evaluate the performance of the proposed mechanism. [ABSTRACT FROM AUTHOR]

The discovery of new therapeutic drugs with the ability of preventing inflammation and joint destruction with less adverse effects is extremely urgent for rheumatoid arthritis (RA). Cryptotanshinone (CTS), an active component isolated from the root of Salvia miltiorrhiza Bunge, has been reported to have antibacterial and antitumor effects. However, its effects on RA have not been clearly elucidated. Here, we investigated the therapeutic effect of CTS on type II collagen-induced arthritis (CIA) in rats and explored the underlying mechanisms. Our results revealed that CTS treatment efficaciously ameliorated inflammation and joint destruction of rats with CIA. Both in vivo and in vitro studies showed that CTS suppressed the production of proinflammatory cytokines including interleukin 1β, tumor necrosis factor alpha, and interleukin 17α production and downregulated the production and activity of matrix metalloproteinase 9. By receptor activator of nuclear factor kappa B (NF-κB) ligand–induced bone marrow macrophages, we observed that CTS could inhibit osteoclast differentiation, which is critic for joint destruction. Further studies on inflammatory signaling revealed that CTS could inhibit the degradation of inhibitor of NF-κB (IκB) α in vivo and in vitro, prevent the nuclear translocation of NF-κB p65 induced by lipopolysaccharide in a time- and dose-dependent manner. By electrophoretic mobility shift assay and luciferase reporter assay, we found that CTS distinctively inhibited the NF-κB DNA binding activity and NF-κB–dependent luciferase activity. These results indicate that the therapeutic effect of CTS on CIA is accomplished mainly through the inhibition of NF-κB signaling. Our findings provide the evidence to develop CTS as a potential therapeutic agent for patients with RA. [ABSTRACT FROM AUTHOR]

Spike (S) protein, a homotrimeric glycoprotein, is the most important antigen target for SARS-CoV-2 vaccines. A complete simulation of the advanced structure of this homotrimer during subunit vaccine development is the most likely method to improve its immunoprotective effects. In this study, preparation strategies for the S protein receptor-binding domain, S1 region, and ectodomain trimer nanoparticles were designed using ferritin nanoparticle self-assembly technology. The Bombyx mori baculovirus expression system was used to prepare three nanoparticle vaccines with high expression levels recorded in silkworms. The results in mice showed that the nanoparticle vaccine prepared using this strategy could induce immune responses when administered via both the subcutaneous administration and oral routes. Given the stability of these ferritin-based nanoparticle vaccines, an easy-to-use and low-cost oral immunization strategy can be employed in vaccine blind areas attributed to shortages of ultralow-temperature equipment and medical resources in underdeveloped areas. Oral vaccines are also promising candidates for limiting the spread of SARS-CoV-2 in domestic and farmed animals, especially in stray and wild animals. [Display omitted] • Nanoparticle Spike antigen expressed in fusion with ferritin has stronger immunogenicity. • Both intramuscular and oral immunizations induce potent immune responses. • Antibodies induced by nanoparticle antigens have neutralizing effect on both origin strain and delta variants of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Abstract: Cardiac fibrosis is involved in a lot of cardiovascular pathological processes. Cardiac fibrosis can block conduction, cause hypoxia, strengthen myocardial stiffness, create electrical heterogeneity, and hamper systolic ejection, which is associated with the development of arrhythmia, heart failure and sudden cardiac death. Besides the initial stimulating factors, the cardiac fibroblasts (CFs) are the principal responsible cells in the fibrogenesis cascade of events. TRPM7, a member of the TRPM (Melastatin) subfamily, is a non-selective cation channel, which permeates both Ca2+ and Mg2+. Here we demonstrated TRPM7 expression in CFs, and 2-APB (TRPM7 inhibitor), inhibited Ang II-induced CTGF, α-SMA expression and CFs proliferation. Besides, knocking down TRPM7 by shRNA, we proved that TRPM7 mediated both calcium and magnesium changes in cardiac fibroblasts which contribute to fibrosis progress. This study suggested that TRPM7 should play a pivotal role in cardiac fibroblast functions associated to cardiac fibrosis development. [Copyright &y& Elsevier]

Abstract: As one of the most important cultural heritages, ink and wash paintings (IWPs) play an important role in the world of traditional Chinese arts. In comparison with western arts, the Chinese IWPs have the unique feature that the art form is primarily populated with limited number of content elements, such as stones, mountains, flowers, and animals etc. and hence most likely different art pieces share similar content, making it difficult to differentiate in terms of content alone. In this paper, we propose to extract histogram-based local feature and global feature to characterize different aspects of art styles, and such features are applied to drive neural networks to complete the classification of IWPs in terms of individual artistic descriptors. We then propose a windowed and entropy balanced fusion scheme to make integrated decisions to optimize the final classification and recognition results. Extensive evaluation via experiments is also reported, which supports that the proposed algorithm achieves good performances, outperforming the existing benchmark techniques and hence providing an excellent potential for computerized analysis and management of traditional Chinese IWPs. [Copyright &y& Elsevier]

Abstract: This study applies the scanning t -test to the tree-ring chronology of Scots pine (Pinus sylvestris L.) from Finnish Lapland for 7638 years for detecting significant changes of subseries means. Analyses are done on multi-centennial scales and the results are demonstrated both as figures and tables. Firstly, the tree-ring data were processed to smooth the high frequency fluctuations by using an 81-year low-pass Gaussian filter following the Maximum Entropy Spectrum analysis, which revealed two significant periodicities on 80.3–87.7-year and 30.8–31.8-year scales. Secondly, the scanning t -test detected 20 change-points in the low-passed series of the tree-ring chronology. The 7638-year tree-ring chronology was partitioned into 21 episodes with comparatively high or low subseries-mean index. The episode durations range from 105 to 845 years. On longer temporal scales, a decreasing tree-ring trend (indicating palaeoclimatic cooling) appeared during the period from 4960BC to 2442BC, an increasing trend (indicating palaeoclimatic warming) occurred during years 2441BC to 449BC, then a roughly normal stage followed between 425BC and 1148AD. The last 850 years contain three episodes of obvious changes: the episode in 1149–1447AD in high index, the episode in 1448AD–1735AD as a low index, and the episode in 1736–2004AD showing back to high index again. Thirdly, all the detected 21 episodes were verified by comparing the results with the previously published studies and their interpretation. This verification was expanded to some works on the ice-core and glacier in the Greenland and the Alps respectively, in the regional view. [Copyright &y& Elsevier]

Abstract: A rainfall index since 1873 was constructed for the middle-lower reaches of Yangtze River basin in the eastern Asia summer monsoon (May–August) based on observations and the 5 grades of dryness/wetness in eastern China. Using data from 4 stations: Shanghai (no missing data), Nanjing, Hangzhou and Wuhan (missing data for years before 1905 and from 1938 to 1945), the sums of rainfall observations for the 4 months May–August at each station for years before 1951 was standardized. Secondly, linear regression analysis using data from Shanghai was used to infer the missing data from the other three stations. Thirdly, regressive results were corrected using the 5 grads of dryness/wetness at each station. Finally, average values were calculated over the 4 stations. For years 1951––2006, standardizations at each of the 4 stations and average over the 4 stations were calculated based on full observations. The homogeneity and change points of mean (the first moment) and of variance (the second moment) in this rainfall index series were detected by applying the scanning t-test and scanning F-test separately. The results show good homogeneity in both of mean and of variance for the two periods 1873–1950 and 1951–2006. Meanwhile, 8 climatic change points in subseries means and in subseries variances were detected on decadal time scales. This index correlates well to the rainfall variation over the middle-lower reaches and south of the Yangtze River basin based on the observational data in 1951–2006. [Copyright &y& Elsevier]

• A deep learning method is proposed using different optimizers and transfer learning to classify Pneumonia patients. • Preparing a dataset of around 5300 X-ray images for pneumonia detection. • The proposed deep transfer learning method is trained on a benchmark open dataset of chest x-ray images. • Presenting the optimization results, precision, recall, accuracy, and F1-score for proposed method. • In proposed method achieve better accuracy in the detection rate than other techniques. Pneumonia is a disease that leads to the death of individuals within a short period since the flow of fluid in the lungs. Hence, initial diagnosis and drugs are very important to avoid the progress of the disease. This paper proposes a novel deep learning approach for automatic detection of pneumonia using deep transfer learning to simplify the detection process with improved accuracy. This work was aimed to preprocess the input chest X-ray images to identify the presence of pneumonia using U-Net architecture based segmentation and classifies the pneumonia as normal and abnormal (Bacteria, viral) using pre-trained on ImageNet dataset models such as ResNet50, InceptionV3, InceptionResNetV2. Besides, to extract the efficient features and improve accuracy of pre-trained models two optimizers, namely, Adam and Stochastic Gradient Descent (SGD) used and its performances are analyzed with batch sizes of 16 and 32. Based on the values obtained, the performances of undertaken pre-trained models are analyzed and compared with other Convolutional Neural Network (CNN) models such as DenseNet-169+SVM, VGG16, RetinaNet + Mask RCNN, VGG16 and Xception, Fully connected RCNN, etc using various measures. From the results observed that the proposed ResNet50 model work achieved 93.06% accuracy, 88.97 % precision rate, 96.78% Recall rate and 92.71% F1-score rate, which than is higher than the other models aforementioned. [ABSTRACT FROM AUTHOR]

Abstract: As most of MPEG and JPEG compression standards are DCT-based, we propose a simple, low-cost and fast algorithm to extract dominant colour features directly in DCT domain without involving full decompression to access the pixel data. As dominant colour is one of the colour features used in constructing MPEG-7 dominant colour descriptors, the proposed algorithm would provide useful technique for those already compressed videos and images, where MPEG-7 dominant colour descriptors are needed. While the proposed algorithm presents advantages in terms of computing efficiency, i.e. eliminating the need of IDCT for compressed videos and images, extensive experiments also support that the proposed algorithm achieves competitive performances in extracting dominant colour features when compared with the pixel domain extraction described in MPEG-7. [Copyright &y& Elsevier]

Abstract: Video temporal segmentation is normally the first and important step for content-based video applications. Many features including the pixel difference, colour histogram, motion, and edge information etc. have been widely used and reported in the literature to detect shot cuts inside videos. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries with one single algorithm. In this paper, we propose a fuzzy logic approach to integrate hybrid features for detecting shot boundaries inside general videos. The fuzzy logic approach contains two processing modes, where one is dedicated to detection of abrupt shot cuts including those short dissolved shots, and the other for detection of gradual shot cuts. These two modes are unified by a mode-selector to decide which mode the scheme should work on in order to achieve the best possible detection performances. By using the publicly available test data set from Carleton University, extensive experiments were carried out and the test results illustrate that the proposed algorithm outperforms the representative existing algorithms in terms of the precision and recall rates. [Copyright &y& Elsevier]

Abstract: In this paper, we describe a combined approach with fuzzy logic and structure tensor towards improved enhancement of possible MCs (microcalcifications) in digital mammograms. The proposed contrast enhancement algorithm has two operational components. One is structure tensor operator and the other is fuzzy enhancement operator, both of which are arranged in parallel to process the input digital mammograms. While the structure tensor operator processes the digital mammograms and produces a corresponding eigen-image to highlight the region-of-interests, the fuzzy enhancement operator fuzzifies the mammogram via the maximum fuzzy entropy principle in fuzzy domain. As a result, the local fuzzy contrast can be extracted and modified adaptively in accordance with the information provided by the eigen-image, and those non-MCs regions are suppressed by being taken as noise. After that, the mammogram is transformed back to the pixel domain and the enhanced mammogram is constructed. Extensive experimental results show that our proposed algorithm outperforms the existing benchmark in terms of cost figures across the whole range of true positive fractions (TPF). [Copyright &y& Elsevier]

To improve efficiency of compressed image retrieval, we propose a novel statistical feature extraction algorithm in this paper to characterize the image content directly in its compressed domain. The statistical feature extracted is mainly through computing a set of moments directly from DCT coefficients without involving full decompression or inverse DCT. Following the algorithm design, a content-based image retrieval system is implemented especially targeting retrieving joint picture expert group compressed images. Theoretical analysis and experimental results support that the system is robust to translation, rotation and scale transform with minor disturbance, and the system achieves good performances in terms of retrieval efficiency and effectiveness. [Copyright &y& Elsevier]

Abstract Video summarization has unprecedented importance to help us overview current ever-growing amount of video collections. In this paper, we propose a novel dynamic video summarization model based on deep learning architecture. We are the first to solve the imbalanced class distribution problem in video summarization. The over-sampling algorithm is used to balance the class distribution on training data. The novel two-stream deep architecture with the cost-sensitive learning is proposed to handle the class imbalance problem in feature learning. In the spatial stream, RGB images are used to represent the appearance of video frames, and in the temporal stream, multi-frame motion vectors with deep learning framework is firstly introduced to represent and extract temporal information of the input video. The proposed method is evaluated on two standard video summarization datasets and a standard emotional dataset. Empirical validations for video summarization demonstrate that our model achieves performance improvement over the existing and state-of-the-art methods. Moreover, the proposed method is able to highlight the video content with the active level of arousal in affective computing task. In addition, the proposed frame-based model has another advantage. It can automatically preserve the connection between consecutive frames. Although the summary is constructed based on the frame level, the final summary is comprised of informative and continuous segments instead of individual separate frames. [ABSTRACT FROM AUTHOR]

Recent developments of virtual reality applications have accelerated the usage of cameras with wide-angle and telephoto/macro lens, which produce nonlinear radial lens distortions, such as barrel distortion and pincushion distortion. However, due to many reasons, the resolution of images with nonlinear lens distortions is often limited. In this paper, we address the image super-resolution (SR) for images with nonlinear lens distortions through the deep convolutional neutral network with residual learning, which can significantly improve the image quality before and after the camera calibration. The proposed deep learning network was trained using hundreds of simulated images and tested on real cameras with fisheye and macro lens. Experimental results show that the proposed image SR method outperforms state-of-the-art SR methods for various degrees of radial-based barrel and pincushion distortions. [ABSTRACT FROM AUTHOR]

• Our method takes the ordering of pairwise distance as the supervisory signal to learn the similarity score function. • Our similarity score function captures both local structure and global structure of the data sample distribution. • We propose a simple but effective strategy to identify the boundary samples from a given dataset. Unsupervised clustering categorizes a sample set into several groups, where the samples in the same group share high-level concepts. As the clustering performances are heavily determined by the metric to assess the similarity between sample pairs, we propose to learn a deep similarity score function and use it to capture the correlations between sample pairs for improved clustering. We formulate the learning procedure in a ranking framework and introduce two new supervisory signals to train our model. Specifically, we train the similarity score function to guarantee 1) a sample should have a higher level of similarity with its nearest neighbors than others in order to achieve correct clustering, and 2) the ordering of the similarity between neighboring sample pairs should be preserved in order to achieve robust clustering. To this end, we not only study the relevance between neighboring sample pairs for local structure learning, but also study the relevance between each sample and the boundary samples for global structure learning. Extensive experiments on seven public available datasets validate the effectiveness of our proposed framework, including face image clustering, object image clustering, and real-world image clustering. [ABSTRACT FROM AUTHOR]

Abstract: Computer-aided diagnosis is one of the most important engineering applications of artificial intelligence. In this paper, early detection of breast cancer through classification of microcalcification clusters from mammograms is emphasized. Although artificial neural network (ANN) has been widely applied in this area, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve A z . This performance may become much worse when the training samples are imbalanced. As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from has been significantly improved. An average improvement of more than 10% in the measurements of F 1 score and A z has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications. [Copyright &y& Elsevier]

Ethnopharmacological relevance: Zhen-wu-tang (ZWT) is a blended traditional Chinese medicine specifically used for various kidney diseases. Aim of the study: The present study is to evaluate the effects of ZWT on diabetic nephropathy and investigate the potential anti-diabetic mechanisms. Materials and methods: Streptozotocin (STZ)-induced diabetic rats were orally administrated ZWT (80,320mg/kg b.w.) once a day, for a period of 12 weeks. Body weight, urinary volume, urine protein and blood biochemical parameters were measured every 3 weeks. At the end of the observation period, kidneys were isolated for histology, angiotensin II content determination, real time PCR and Western blot analyses. Results: ZWT (320mg/kg b.w.) prevented body weight loss, reduced polyurea, urinary protein excretion, serum creatinine and blood urea nitrogen, although it did not alter the hyperglycemia. It ameliorated glomerular hypertrophy and expansion of the mesangial area, swell and effacement of foot process. It also suppressed the increased expression of angiotensin II, nephrin and podocin. Conclusions: Data from this study suggest that ZWT possess a protective effect on renal damage of STZ-induced diabetic nephropathy in rats, by suppressing hyperactivity of renal renin–angiotensin system that turns in to modulate renal nephrin and podocin expressions, thereby protecting podocyte from injury. [Copyright &y& Elsevier]

Abstract: Anti-cancer agent adriamycin (ADR) has demonstrated high anti-tumor efficacy. However, its use in chemotherapy has been limited largely due to its diverse toxicities, including renal toxicity, such as nephrotic syndrome with proteinuria. Podocyte injury leads to glomeruli proteinuria. Wulingsan (WLS) is a blended traditional Chinese herbal medicine specifically used for various kidney diseases. In the present study, we found that a water extract of WLS (480mg/kg, p.o.,×28 days) reduced ADR-induced increase in urine protein excretion, plasma total cholesterol and triglyceride, and decrease in plasma total protein and albumin in rats. Furthermore, the results of electron microscopy demonstrated suppression by WLS of ADR-induced increase in width of foot process, increase in surface density and decrease in volume density. These results suggest that WLS ameliorates ADR-induced proteinuria and podocyte injury. Gene analysis results demonstrated a suppression of renal overexpression of nephrin mRNA and protein by WLS. Radioimmunoassay showed that WLS suppressed ADR-induced increased renal angiotensin II content in rats. Thus our results demonstrate that WLS ameliorates ADR-induced nephrotic syndrome in rats possibly by suppressing ADR-induced hyperactivity of renal renin–angiotensin system to modulate renal nephrin gene expression, thereby protecting podocyte from injury. [Copyright &y& Elsevier]

• Our evidence has shown that active case-finding of tuberculosis might be effective in high prevalence area with a low economic level, particularly among the elderly and people with a history of TB. • New rapid diagnosis technology should be considered to decrease the TB prevalence in people with a history of TB. • ACF screening could identify more active TB cases than PCF by a TB surveillance system in high prevalence area with underdeveloped economics. Tuberculosis (TB) is a critical global public threat, and limited epidemiology studies have been performed to explore the efficacy of active TB screening. Three sites located in eastern China were chosen in 2013, and three rounds of systematic screenings were performed in permanent residents aged older than 15 years. The TB incidence showed a downtrend after several rounds of active screening at the three sites, and a significant change was observed at site A in the overall population. In the target population at sites A and B, both the elderly and people with a history of TB had a remarkable decline through the first or second round of screening. The implementation of active case-finding identified 2.36 [1.47,3.81] (2013 vs. 2012) and 1.49 [1.1,2.03] (2013–2015 vs. 2010–2012) more potential cases than the passive case-finding by the surveillance system at site A. Active case-finding of tuberculosis might be effective in high prevalence area with a low economic level, particularly among the elderly and people with a history of TB. Additionally, new rapid diagnosis technology should be considered to decrease the prevalence among people with a history of TB. Ultimately, active screening identified more active TB cases than passive case-finding, particularly in high prevalence area with underdeveloped economics. [ABSTRACT FROM AUTHOR]

Highlights • A latent tuberculosis infection (LTBI) rate of 33.9% was found among TB healthcare workers in southeastern China. • Factors associated with LTBI at both the individual and facility level were identified. • Comprehensive TB infection control measures and regular testing for LTBI are needed in TB hospitals. Abstract Objectives Healthcare workers (HCWs) are at high risk of latent tuberculosis infection (LTBI), and the baseline prevalence of LTBI among frontline TB HCWs in southeastern China remains unknown. The aim of this study was to assess the prevalence of LTBI among TB HCWs and to analyze factors associated with LTBI at both the individual and institutional level. Methods Based on a cross-sectional study design, 31 out of 89 TB-designated hospitals in Zhejiang Province of China were selected. Information on TB infection control measures was collected through field visits to each of the selected hospitals. All TB HCWs from the selected hospitals were recruited to answer a questionnaire and to undergo LTBI testing by TB interferon gamma release assay. Univariate analyses and a generalized linear mixed model were applied to analyze factors associated with LTBI at both the individual and hospital level. Results A total of 487 TB HCWs were recruited at the 31 TB-designated hospitals; 33.9% of them tested positive for LTBI. At the institutional level, a low TB epidemic level, regular infection control training for HCWs, and regular maintenance of ultraviolet disinfection equipment were found to be significantly associated with a lower LTBI rate among HCWs. At the individual level, alcohol use, a greater number of years working on TB, and a longer weekly duration of contact with TB patients were identified as associated factors for LTBI among HCWs. Conclusions The LTBI rate among frontline TB HCWs was found to be high in southeastern China. Factors at the institutional and individual level could both affect the prevalence of LTBI among HCWs. [ABSTRACT FROM AUTHOR]

Abstract Detecting anomalies in sequence data has become an important research topic with applications in the fields of network intrusion and cluster system reliability. Especially, detecting and locating unknown abnormal information are very important tasks. One of the challenges that is highly valued by both academia and industry is reducing the training time and the complexity of the model. Moreover, the model should not only improve the detection efficiency but should also quickly obtain accurate results. This paper proposes a novel anomaly detection algorithm with fuzzy clustering for the session feature similarity (SFAD). The proposed algorithm consists of three main steps. First, we establish sliding windows to collect the web access information of different users. Second, we use the P a g e R a n k algorithm to determine the webpage weight information and calculate the similarity information between users. Finally, using Lambda Cut method of fuzzy clustering to identify suspect users, we can locate abnormal users based on the information returned from multiple windows. The experimental results show that our method is simple and practical; namely, through five groups of comparison experiments on the msnbc.com experimental dataset, the results show that the SFAD method can effectively achieve higher detection accuracy and a lower false alarm rate compared to other methods. [ABSTRACT FROM AUTHOR]

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging disease caused by the SFTS virus (SFTSV) of the family Bunyaviridae . Since the virus was first isolated in 2009, it has become widespread in China, with an increasing number of cases year on year. Although the disease has been researched extensively in past years, there are still no effective measures to suppress the epidemic situation. This article reports a pilot study of comprehensive measures, including health education and risk communication, weed removal, livestock management, and tick control, to prevent this emerging disease in an endemic region of China. The density of ticks decreased dramatically month by month after acaricides were sprayed in the areas surrounding recreational and agricultural settings. The number of SFTS cases and villages involved declined in the years after the integrated measures were applied. Comprehensive measures, especially community-based tick control, may be a promising means of preventing SFTS in endemic regions. [ABSTRACT FROM AUTHOR]

Although research on detection of saliency and visual attention has been active over recent years, most of the existing work focuses on still image rather than video based saliency. In this paper, a deep learning based hybrid spatiotemporal saliency feature extraction framework is proposed for saliency detection from video footages. The deep learning model is used for the extraction of high-level features from raw video data, and they are then integrated with other high-level features. The deep learning network has been found extremely effective for extracting hidden features than that of conventional handcrafted methodology. The effectiveness for using hybrid high-level features for saliency detection in video is demonstrated in this work. Rather than using only one static image, the proposed deep learning model take several consecutive frames as input and both the spatial and temporal characteristics are considered when computing saliency maps. The efficacy of the proposed hybrid feature framework is evaluated by five databases with human gaze complex scenes. Experimental results show that the proposed model outperforms five other state-of-the-art video saliency detection approaches. In addition, the proposed framework is found useful for other video content based applications such as video highlights. As a result, a large movie clip dataset together with labeled video highlights is generated. [ABSTRACT FROM AUTHOR]

Most haze removal methods fail to restore long-shot images naturally, especially for the sky region. To solve this problem, we proposed a Fusion of Luminance and Dark Channel Prior (F-LDCP) method to effectively restore long-shot images with sky. The transmission values estimated based on a luminance model and dark channel prior model are fused together based on a soft segmentation. The transmission estimated from the luminance model mainly contributes to the sky region, while that from the dark channel prior for the foreground region. The airlight also is adjusted to adapt to real light by sky region detection. A user study and objective assessment comparison with a variety of methods on long-shot haze images demonstrate that our method retains visual truth and removes the haze effectively. [ABSTRACT FROM AUTHOR]

This paper proposes a video super-resolution method based on an adaptive superpixel-guided auto-regressive (AR) model. Key-frames are automatically selected and super-resolved by a sparse regression method. Non-key-frames are super-resolved by exploiting the spatio-temporal correlations: the temporal correlation is exploited by an optical flow method while the spatial correlation is modeled by a superpixel-guided AR model. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of both subjective visual quality and objective peak signal-to-noise ratio (PSNR). The proposed method requires less computation and is suitable for practical applications. [ABSTRACT FROM AUTHOR]