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The role of adjunctive corticosteroids in reducing morbidity and mortality of viral CNS infections remains poorly defined. Clinicians are often left in a quagmire regarding steroid use in complex and rapidly evolving viral CNS infections. Limited studies have explored the underlying mechanisms behind the potential benefit of steroids. Here, we describe steroid use in three cases of viral CNS disease: varicella zoster virus (VZV), Powassan virus, and influenza A-associated acute necrotizing encephalopathy.

Learning effective feature representations with deep convolutional neural network (CNN) and metric learning methods to distinguish pedestrians is the key to the success of recent advances in person re-identification (re-ID) tasks. However, the features provided by the common CNN network are not strong enough to distinguish between similar subjects because these common features describe only the scattered local patterns while neglecting the correlation and combinations between them. In fact, the high-order correlations of common features can be significant to the recognition of identities. In this paper, to tackle this problem, we design a flexible high-order block (HOB) module and a scheme of deep metric learning to produce the high-order representations of deep features for the re-identification of pedestrians. Extensive experiments prove the superiority of our proposed HOB module for person re-ID issue. On three large-scale datasets, including Market-1501, DukeMTMC-ReID, and CUHK03-NP, the HOB-net method achieves the competitive results with the state-of-the-arts, particularly in the mAP.

Aim: The aim of this study was to assess the association between PEAR1 polymorphisms and ischemic clinical outcomes. Materials & methods: We searched the electronic database for articles on the relationship of PEAR1 SNPs and ischemic events in patients with coronary artery disease (CAD) up to October 2020. Results: A total of 9914 patients with CAD from six studies focusing on 12 SNPs of PEAR1 were included in this study. The A allele of rs12041331 were associated with ischemic events (odds ratio: 1.40; 95% CI: 1.04–1.88; p = 0.03). The AA homozygotes of rs2768759 was related to a higher risk of ischemic events than carriers of the C allele (odds ratio: 2.08; 95% CI: 1.09–3.97; p = 0.03). Conclusion: PEAR1 rs12041331 and rs2768759 are significantly associated with ischemic events in patients with CAD.

Objective The aim of this study was to describe the natural history of individuals with congenital HIV who develop JC virus (JCV) infection of the central nervous system (CNS). Methods We retrospectively evaluated individuals with congenital HIV who met criteria for progressive multifocal leukoencephalopathy (PML) or JCV granule cell neuronopathy (JCV GCN) at three major healthcare centres in the northeast USA. Data on adherence to combined antiretroviral therapy (cART), neurologic symptoms, serum markers of immunity and HIV infection, cerebrospinal fluid (CSF) analyses, radiographic features, modified Rankin Scale (mRS) scores and survival were collected from the electronic medical record up to a censoring date of 1 August 2020. Results Among 10 adults with congenitally acquired HIV, nine were diagnosed with definitive PML and one was diagnosed with probable JCV GCN. Individuals presented at the time of their PML or JCV GCN diagnosis with a mean mRS of 2.0 (standard deviation 1.0). A premorbid mRS was documented for six patients and was zero in all cases. The most common risk factor was confirmed cART nonadherence in nine individuals. Five individuals with PML and one with JCV GCN died, with a latency from symptom onset to death of approximately 3 months for three individuals, and approximately 2 years for the remaining two. Conclusion Youth-adulthood transition is a high-risk point for dropping off from medical care. The study of this timepoint in people living with HIV could help inform effective care in these individuals.

The salient target detection aims to use models or algorithms to detect and segment the salient target regions in the image. As an image preprocessing step, The salient target detection has been widely researched and applied in computer vision fields such as image segmentation, image compression, object recognition, and image retrieval. In the face of complex environments, such as poor lighting conditions and cluttered backgrounds, saliency detection is still challenging. RGB-T complementary fusion for saliency detection has become a hot research direction to solve the above problems. Therefore, this paper makes full use of the advantages of RGB and thermal infrared images to complement each other, and proposes an end-to-end salient target detection network. To this end, the VGG16 backbone network is first used for rough feature extraction, and then the attention mechanism module is used to enhance and merge the high-level features of RGB and thermal infrared images to enrich semantic information. Then the DFPN module is designed to accumulate high-level features to obtain high-level semantic information, and at the same time, the high-level features integrate the semantic information of thermal infrared images. Then we designed the SIPM self-interactive pooling module to further refine the features so that it can adaptively handle multi-scale targets. Finally, we compared the proposed method with several other advanced methods on several public datasets, and the experimental results proved the effectiveness of our method.

Stereo depth estimation based on learning with CNNs has become the dominant paradigm due to their significant leap in performance. However, since the inconsistency of the distribution between the training data (synthetic image) and the actual scene, the generalization ability is insufficient for deployment. Recently, the work MADNet with modular adaptation was proposed to tackle this issue on the fly. However, it still runs slowly with not-so-ideal performance. This paper introduces a “harmless training or inference” (HToI) framework to improve their work in both convergence and prediction speed. Firstly, in the actual scenario, the images arrive in sequence without ground-truth. Online adaptation to new target domains is only through self-supervised loss with errors (e.g., occluded areas). This paper introduces a confidence-weighted map (CWM) to mask the incorrect loss. The confidence map will guide the model to use the correct loss for online adaptation to obtain better accuracy. Secondly, this paper proposes a supervisor, implemented by Mann-Kendall trend detection on loss (MKL), to monitor the performance changes during the adaptation. According to the MKL, the model can autonomously determine whether to keep harmless training. If not training (i.e., only inference), the model will only predict the disparity, significantly reducing the running time. Finally, experiments on real-world stereo datasets confirm the effectiveness of CWM and MKL, which can make the model adapt to the scene more stably and accurately.

Three-dimensional depth perception using structured light has been widely researched and developed due to its robustness, high precision, and density. An open research issue exists with current structured light technology to obtain depth maps of scenes with objects in different motion states, while ensuring high accuracy and resolution. In this paper, a new algorithm for depth acquisition of multi-state targets is designed. A novel projection pattern is designed based on sinusoidal, speckle and line patterns. The foreground targets are separated according to the background segmentation technology, and regions with different motion states are divided into static, slow motion or fast motion regions. Then, the different motion states are used to select different pattern decoding schemes. In the experiments, the depth of multi-state objects are successfully calculated by our technique.

Enteroviruses are responsible for a large number of meningoencephalitis cases, especially in children. The objective of this study was to identify modes of diagnosis including the significance of respiratory and cerebrospinal fluid samples, associated clinical characteristics, inpatient management, and outcome of individuals with EV infections of the central nervous system (CNS). Electronic medical records of individuals with enterovirus infections of the central nervous system who presented to the Columbia University Irving Medical Center and Children’s Hospital of New York between January 1, 2012 and December 31, 2017 were reviewed retrospectively for demographic, epidemiological and clinical data. The median age overall was 1.7 months (interquartile range 14 years), and most (62.4%) were male. The majority of CNS infections presented as meningitis (95.7%) and occurred in the summer (45.2%) and fall seasons (37.6%). Eighty-five cases (91.4%) demonstrated enterovirus positivity in cerebrospinal fluid, thirty cases (32.3%) exhibited both cerebrospinal fluid and respiratory positivity and eight cases (8.6%) exhibited respiratory positivity with coinciding neurological findings. Eighty-nine individuals overall (95.7%) received antibiotics and 37 (39.8%) received antiviral treatment. All surviving individuals had favorable Modified Rankin Scores within the zero to two range upon discharge. Testing respiratory samples in addition to cerebrospinal fluid was found to be an important diagnostic tool in enterovirus-associated cases. While clinical outcomes were favorable for an overwhelming majority of cases, etiological understanding of CNS infections is essential for to identify ongoing and changing epidemiological patterns and aid in improving the diagnosis and treatment.

To realize the continuous compaction control (CCC) of asphalt pavement during construction, continuous detection method was investigated for the compaction degree values. For the trajectory of rollers, a collaborative positioning method was proposed. For the monitoring of rolling process, an embedded-based detection system was designed. For the evaluation of rolling effect, harmonic analysis was introduced and a new index, vibration compaction energy value (VCVe), was proposed. Positioning experiments were conducted, and the accuracy was improved to 0.48 m. Rolling tests were performed, and typical compaction meter values (CMVs), compaction control values (CCVs), and VCVe were obtained. The referenced compaction degree by conventional way was 94.6%, which was used to calibrate the detected values of compaction degree indexes. The results showed that continuous compaction detection can be achieved based on positioning system and vibration analysis. Compared with CMV and CCV, VCVe is less discrete, more stable, and consistent to describe the compaction state. Though, all the CMV, CCV, and VCVe indexes are unable to be used for quality assurance directly or alone, they could be an aid for quality control. Continuous compaction detection system meets the monitoring requirements of pavement construction at a lower cost and could lay a foundation for the intelligent compaction (IC).

Fungal central nervous system infections present in a myriad of clinical manifestation and remain a significant diagnostic challenge. Presenting symptoms, cerebrospinal fluid analysis, conventional fungal serum markers and imaging correlates of disease are seldom sensitive or specific; fungal culture growth or histopathologic analysis are often required for definitive diagnoses but limited by feasibility. Novel diagnostic tools (such as the cryptococcal antigen and next-generation sequencing) have increased diagnostic potency when available but require further studies to define their utility.Invasive fungal infections are an emerging health threat in the setting of increased immunomodulatory treatments, advancements in transplant medicine and increased world-wide travel. Therefore, strong clinical suspicion from epidemiologic clues, clinical progression and presence of CNS dissemination risk factors must be exercised to pursue broad diagnostic workups and rapidly initiate medical and surgical management. This article will describe the epidemiology, clinical presentations, diagnostic approach and therapeutic interventions for fungal infections in the central nervous system.

A large class of Person Re-identification (ReID) approaches identify pedestrians with the TriHard loss. Though the TriHard loss is a robust ReID method, pose variance and viewpoint in pedestrians constrain the performance. To address this problem, we introduce a spatial transformer network (STN) to align pedestrians. Then, we illustrate the generality of the STN module in pose variance problem through the evaluations on feature representation network (FRN) like VGG, ResNet and DenseNet architectures respectively. Furthermore, based on the evaluation results, we propose a robust and high-performance ReID model which consists of the STN module, DenseNet backbone and TriHard loss. And finally, we prove that our ReID model is whole differentiable by formula derivation, therefore achieving an end-to-end high-performance ReID system. The experiments show that our ReID system outperforms the state-of-art methods on Market-1501, DukeMTMC-reID and CUHK03 datasets.

Convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth sensor, which directly reduces the external noise of the depth sensor so as to increase its accuracy. To reduce the amount of training data and avoid overfitting, a patch size of the laser stripe image is determined, on the basis of which a small-scale dataset called Laser Stripe Image Patch (LSIP) is created. Also, a 14-layers convolutional autoencoder is constructed to reduce the noise of the image patches, which can learn the most salient features on the LSIP dataset. Moreover, the trained convolutional autoencoder is applied to an omnidirectional structured light system. Experimental results demonstrate that the proposed method obtains useful features and superior performance both visually and quantitatively on denoising tasks, and significantly improves the accuracy of the structured light system. Keywords: Convolutional autoencoder, Deep learning, Depth perception, Image denoising, Laser stripe, Structured light

Spinal dural arteriovenous fistula (SDAVF) is an elusive and underdiagnosed disease. Congestive myelopathy occurs from increased venous pressure transmitted by the fistula between a radiculomeningeal artery and the spinal venous plexus. While its cause remains unknown, associations between SDAVF and hyper-vascular states have been reported. We present the first documented case report of a de novo SDAVF diagnosis in a patient with active renal cell carcinoma (RCC) metastasis to the spinal epidural space and review the literature.

Objective Most SARS‐CoV‐2‐infected individuals never require hospitalization. However, some develop prolonged symptoms. We sought to characterize the spectrum of neurologic manifestations in non‐hospitalized Covid‐19 “long haulers”. Methods This is a prospective study of the first 100 consecutive patients (50 SARS‐CoV‐2 laboratory‐positive (SARS‐CoV‐2+) and 50 laboratory‐negative (SARS‐CoV‐2‐) individuals) presenting to our Neuro‐Covid‐19 clinic between May and November 2020. Due to early pandemic testing limitations, patients were included if they met Infectious Diseases Society of America symptoms of Covid‐19, were never hospitalized for pneumonia or hypoxemia, and had neurologic symptoms lasting over 6 weeks. We recorded the frequency of neurologic symptoms and analyzed patient‐reported quality of life measures and standardized cognitive assessments. Results Mean age was 43.2 ± 11.3 years, 70% were female, and 48% were evaluated in televisits. The most frequent comorbidities were depression/anxiety (42%) and autoimmune disease (16%). The main neurologic manifestations were: “brain fog” (81%), headache (68%), numbness/tingling (60%), dysgeusia (59%), anosmia (55%), and myalgias (55%), with only anosmia being more frequent in SARS‐CoV‐2+ than SARS‐CoV‐2‐ patients (37/50 [74%] vs. 18/50 [36%]; p

The omnidirectional ring structured light depth perception system can project a 360-degree structured light.However, due to the influence of ambient light, refraction and other noise factors, it is easy to cause false detection of structured light. Therefore, denoising plays an important role in the omnidirectional ring structured light depth perception system.Firstly, this paper uses the DCGAN network to generate more data sets due to the lack of data sets. Secondly, based on the above data sets, this paper uses the autoencoder network to denoise the ring structured light. Experiments show that by using a combination of DCGAN and autoencoder network for denoising, the structured light’s noise is lesstherefore more robust to the surrounding environment compared to other methods.

A depth perception method based on omnidirectional image and ring structured light is proposed. Firstly, a new omnidirectional ring structured light pattern (ORSL) was presented. ORSL can simplify feature point extraction and locate the scene position more robust and fast. Secondly, a new sensor is proposed, which including an omnidirectional camera, laser projector and control circuit system. Thirdly, measurement accuracy was improved on error correction methods. Finally, a pose estimation method is designed to compute the location and direction of the imaging system. The experimental results demonstrated that the proposed method can acquire omnidirectional depth information about large-scale scene. The error analysis of multiple sets of depth data show the effectiveness of the method.

To better understand task failures in cloud computing systems, the authors analyze failure frequency of tasks based on Google cluster dataset, and find some frequently failing tasks that suffer from long-term failures and repeated rescheduling, which are called killer tasks as they can be a big concern of cloud systems. Hence there is a need to analyze killer tasks thoroughly and recognize them precisely. In this article, the authors first investigate resource usage pattern of killer tasks and analyze rescheduling strategies of killer tasks in Google cluster to find that repeated rescheduling causes large amount of resource wasting. Based on the above observations, they then propose an online killer task recognition service to recognize killer tasks at the very early stage of their occurrence so as to avoid unnecessary resource wasting. The experiment results show that the proposed service performs a 93.6% accuracy in recognizing killer tasks with an 87% timing advance and 86.6% resource saving for the cloud system averagely.

As an important and challenging task for intelligent video surveillance systems, video anomaly detection is generally referred to as automatic recognition of video frames that contain abnormal targets, behavior or events. Although it has been widely applied in real scenes, anomaly detection remains a challenging task because of the vague definition of anomaly and the lack of the anomaly samples. Inspired by the widespread application of Generative Adversarial Network (GAN), we propose an end-to-end pipeline called NM-GAN which assembles an encode-decoder reconstruction network and a CNN-based discrimination network in a GAN-like architecture. The generalization ability of the reconstruction network is properly modulated via the adversarial learning around reconstruction error maps and noise maps. Meanwhile, the discrimination network is trained to distinguish anomaly samples from normal samples based on the reconstruction error maps. Finally, the output of the discrimination network is transferred to evaluate anomaly score of the input frame. The thorough proof-of-principle experiments and ablation tests on several popular datasets reveal that the proposed model enhance the generalization ability of the reconstruction network and the distinguishability of the discrimination network significantly. The comparison with the state-of-the-art shows that the proposed NM-GAN model outperforms most competing models in precision and stability.

Background Lung squamous cell carcinoma (LUSC) accounts up for approximately 30% of all lung cancers with a high mortality. The study was aimed at finding genes critical in the diagnosis and prognosis of LUSC. Materials and Methods The differentially expressed (DE) genes (DEGs) and DE lncRNAs (DELs) from 501 LUSC and 49 normal lung tissues, and DE miRNAs (DEMs) from 478 LUSC and 45 normal lung tissues were respectively obtained via the TCGA database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and co-expression network analyses were performed. Survival analysis and receiver operating characteristic curve of hub mRNAs were also analyzed. Competitive endogenous RNA networks of lncRNAs, miRNAs and mRNAs were constructed. Results A total of 5747 DEGs, 378 DEMs and 3141 DELs in LUSC were identified in LUSC. The DEGs including AUARK, CDK1, KIF11 and EXO1 were proven to be significant metastatic indicators in LUSC, and 2 DEGs were significantly associated with the survival in LUSC patients. Some genes might have connections with many other gene nodes through a co-expression network. Four lncRNAs, 2 mRNAs and 2 miRNAs were identified as the candidates for the competitive miRNA-mRNA-lncRNA network and might serve as prognostic markers in LUSC. Conclusions We identified the differentially expressed lncRNAs, miRNAs and mRNAs in LUSC, providing further insights into the molecular mechanism of LUSC tumorigenesis and the potential prognostic biomarkers or therapeutic targets for LUSC.

High-precision and high-resolution depth perception based on structured light is widely used. This measurement system consists of projector and camera equipment. Depth sensing based on the structured light method, the calibration process of obtaining the intrinsic and extrinsic parameters of the measurement system is a necessary step. However, the traditional calibration method has certain limitations. In this paper, we propose a self-calibration method. We overcome the problems in existing calibration techniques, and can quickly obtain the intrinsic and extrinsic parameters of the random speckle structured light system in motion scene. We designed a random speckle matching algorithm and proposed a non-coplanar point acquisition scheme. An effective method for finding the correspondence points between dense point pattern views is introduced. Next, the self-calibration process is described, using non-coplanar points matching to calculate the parameters required by the random speckle structured light system in real time. The results obtained in this paper are shown in the experimental section and analyzed from both quantitative and qualitative aspects.

In order to quickly obtain a precise point cloud model of the target, this paper proposes a 3D point cloud reconstruction method. First, a point cloud acquisition system, which consists of Kinect, turntable, green background frame, and computer, is constructed to acquire the point cloud model of the target at 16 shooting angles. After that, when the coordinate system conversion is finished, the original point cloud model are processed by both a pass-through filter and a voxel filter: the green background and other obstacles in the original point cloud model are removed by the Straight-through filter; and cloud models are further simplified by the voxel filter in order to alleviate the computer's computational pressure, improve the registration speed, and reduce the interference of noise on the registration process. At this point, the number of point clouds is simplified to an acceptable level and the basic features of the target point cloud model are not missing. Finally, this paper proposes a improved ICP algorithm with curvature as the feature descriptor and the KD-Tree as the search mechanism to register the processed point cloud models. In this improved method, the curvature can be calculated relatively simply and not susceptible to noise interference, as well as, the KD-Tree can solve the problem of the inefficiency of the classical ICP algorithm search mechanism and boost the search speed. And, a layer-by-layer registration strategy is adopted in the register to reduce the cumulative error. Several experiments have been carried out on several targets in this paper using the above method, and satisfactory experimental results are obtained.

Bugs severely damage the reliability of open source software. In order to improve the reliability of open source software, bug tracking system is built to collect and manage bugs reported from users all over the world. When system failures occur, users investigate whether failures are induced by software bugs and then report bugs. However, it is usually difficult and time consuming to identify bugs from system failures. To accelerate bug reporting and reduce the time users spend on identifying bugs, we present BugIdentifier, an automatic bug identifying approach based on log mining. BugIdentifier combines Doc2Vec with Deep Neural Network (DNN) and treats bug identifying as a binary classification problem. Doc2Vec is adopted to train a log sequence embedding model that transforms log sequences into feature vectors, and then DNN is used to identify whether the log sequence is bug-induced or not. The results of our empirical evaluation show that our approach can automatically identify real-world bugs of Hadoop and OpenStack with the F1-score higher than 75%, specifically, old-version bugs of OpenStack can be identified with 97% F1-score, as a result, bug reporting can be accelerated correspondingly.

In order to obtain all-round information of an object or scene, 3D reconstruction technology used to reconstruct it in the past decades. This technology needs to reconstruct the 3D virtual model of the surface of the object in the computer, so it is necessary to carry out complex modeling processing on the object. Aiming at the complexity of traditional modeling, drawing three-dimensional objects based on virtual viewpoint synthesis is proposed. IBR technology with implicit geometric information mainly used in Depth-Image-Based Rendering (DIBR). The reconstruction of virtual objects is realized by bidirectional mapping, and finally the reproduction of the entire three-dimensional object can be completed by using a small amount of image data.

We propose an image-based face swapping algorithm, which can be used to replace the face in the reference image with the same facial shape and features as the input face. First, a face alignment is made based on a group of detected facial landmarks, so that the aligned input face and the reference face are consistent in size and posture. Secondly, an image warping algorithm based on triangulation is presented to adjust the reference face and its background according to the aligned input faces. In order to achieve more accurate face swapping, a face parsing algorithm is introduced to realize the accurate detection of the face-ROIs, and then the face-ROI in the reference image is replaced with the input face-ROI. Finally, a Poisson image editing algorithm is adopted to realize the boundary processing and color correction between the replacement region and the original background, and then the final face swapping result is obtained. In the experiments, we compare our method with other face swapping algorithms and make a qualitative and quantitative analysis to evaluate the reality and the fidelity of the replaced face. The analysis results show that our method has some advantages in the overall performance of swapping effect.

Graph-based salient object detection methods have gained more and more attention recently. However, existing works fail to separate effectively salient object and background in some challenging scenes. Inspired by this observation, we propose an effective salient object detection method based on a novel boundary-guided graph structure. More specifically, the input image is firstly segmented into a series of superpixels. Then we integrate two prior cues to generate the coarse saliency map, a novel weighting mechanism is proposed to balance the proportion of two prior cues according to their performance. Secondly, we propose a novel boundary-guided graph structure to explore deeply the intrinsic relevance between superpixels. Based on the proposed graph structure, an iterative propagation mechanism is constructed to refine the coarse saliency map. Experimental results on four datasets show adequately the superiority of the proposed method than other state-of-the-art methods.

A depth perception method combining omnidirectional images and encoding structured light was proposed. First, a new structured light pattern was presented by using monochromatic light. The primitive of the pattern consists of four-direction sand clock-like (FDSC) image. FDSC can provide more robust and accurate position compared with conventional pattern primitive. Second, on the basis of multiple reference planes, a calibration method of projector was proposed to significantly simplify projector calibration in the constructed omnidirectional imaging system. Third, a depth point cloud matching algorithm based on the principle of prior constraint iterative closest point under mobile condition was proposed to avoid the effect of occlusion. The experimental results demonstrated that the proposed method can acquire omnidirectional depth information about large-scale scenes. The error analysis of 16 groups of depth data reported a maximum measuring error of 0.53 mm and an average measuring error of 0.25 mm.

In this paper, a new saliency detection model is proposed based on a space-to-frequency transformation. Firstly, the equivalence of spatial filtering and spectral modulation is demonstrated to explain the intrinsic mechanism of typical frequency-based saliency models. Then a novel frequency-based saliency model is presented based on the Fourier Transformation of multiple spatial Gabor filters. Besides, a new saliency measurement is proposed to implement the competition between saliency maps at multiple scales and the fusion of color channels. In experiments, we use a set of typical psychological patterns and four popular human fixation datasets to test and evaluate the proposed model. In addition, a new energy-based criterion is proposed to evaluate the performance of our model and is compared with five traditional saliency metrics for validation. Experimental results show that our model outperforms most of the competing models in salient object detection and human fixation prediction. HighlightsSome frequency-based saliency models can be transformed into the space domain.The frequency-based model derived from the transformation of multiple Gabor filters outperforms most of competing models.The SWE-based scale competition is a better scheme for scale combination.ARI is a better saliency metric than other AUC-based metrics.

Abdominoperineal resection and pelvic exenteration for resection of malignancies can lead to large perineal defects with significant surgical-site morbidity. Myocutaneous flaps have been proposed in place of primary closure to improve wound healing. A systematic review was conducted to compare primary closure with myocutaneous flap reconstruction of perineal defects following abdominoperineal resection or pelvic exenteration with regard to surgical-site complications.A comprehensive literature search was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in the MEDLINE, EMBASE, Google Scholar, and Cochrane Library databases. After data extraction from included studies, meta-analysis was performed to compare outcome parameters defining surgical-site complications of flap and primary closure.Our systematic review yielded 10 eligible studies (one randomized controlled trial and nine retrospective studies) involving 566 patients (226 flaps and 340 primary closures). Eight studies described rectus abdominis myocutaneous flaps and two studies used gracilis flaps. In meta-analysis, primary closure was more than twice as likely to be associated with total perineal wound complications compared with flap closure (OR, 2.17; 95 percent CI, 1.34 to 3.14; p = 0.001). Rates of major perineal wound complications were also significantly higher in the primary closure group (OR, 3.64; 95 percent CI, 1.43 to 7.79; p = 0.005). There was no statistically significant difference between primary and flap closure for minor perineal wound complications, abdominal hernias, length of stay, or reoperation rate.This is the first systematic review with meta-analysis comparing primary closure with myocutaneous flap closure for pelvic reconstruction. The authors' results have validated the use of myocutaneous flaps for reducing perineal morbidity following abdominoperineal resection or pelvic exenteration.Therapeutic, II.

This paper presents a depth perception system based on one-shot coding structured light method. The designed monochromatic pattern is robust against occlusion and sharp deformation, suitable for dynamic scene, and less sensitive for color interference, with symbol density spectrum (SDS) rule, legitimately increasing number of feature points inside element which further enhancing resolution. For the decoding stage, firstly, the proposed adaptive-symmetry-template (ASD) can robustly detect feature points, secondly, in virtue of transfer learning, using DenseNet to identify element label, thirdly, graph topological structure and look-up-table (LUT) help reducing decoding time. The experiments show that the encoding method increases resolution fairly a lot and for several complex objects, the decoding method can achieve high accuracy and keep robustness.

Hydrogen economy based on electrochemical water splitting represents one of the most promising means for overcoming the rapid consumption of fossil fuels and the serious deterioration of global climate. The development of earth-abundant, efficient, and durable electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) plays a vital role in the commercialization of water electrolysis. The self-supported electrode is regarded as an effective approach to developing highly active electrocatalysts with exceptional stability, compared to the conventional coated powdery-type electrocatalysts. However, lack of systematic reviews for the development of self-supported electrodes limits its innovation. The one-dimensional (1D) and two-dimensional (2D) nanostructures are substantially investigated for boosting catalytic activity due to their large surface areas, effective mass transport, and fast electron transfer. Herein, we review the state-of-the-art development of self-supported transition-metal-based electrodes with 1D/2D nanostructures for HER, OER, as well as both HER and OER, classify them as general 1D/2D nanoarrays, hierarchical 1D/2D nanostructures, and hollow 1D/2D nanostructures. This review will advance future researches and provide a guideline in developing self-supported electrodes with excellent electrocatalytic performances.

Objective: The purpose of this study was to explore the application of dexmedetomidine (Dex) in cardiopulmonary bypass. Methods: A total of 60 patients undergoing elective cardiopulmonary bypass were divided into control (C) group and Dex group. In the Dex group, appropriate amount of Dex was added into the membrane lung prefilling solution before anesthesia induction, while those in control group were given normal saline. The levels of mean arterial pressure (MAP) and heart rate (HR) at different times were measured. The levels of cardiac troponin I (CTNI), malondialdehyde (MDA), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α) at different points (T0/T1/T2/T3/T4) in both groups were measured by enzyme-linked immunosorbent assay kits. Results: The intraoperative and postoperative levels of MAP and HR in the 2 groups were significantly lower than those preoperatively ( P < .05). The levels of MAP and HR in the Dex group were significantly lower than those of the C group ( P < .05). The levels of CTNI/MDA/IL-6/TNF-α at different points in both groups were significantly higher than those at T0 ( P < .05). The serum levels of CTNI, MDA, IL-6, and TNF-α in the Dex group at T1/T2/T3/T4 were significantly lower than those in the C group ( P < .05). The rate of arrhythmia in the Dex group was significantly lower than that in the C group ( P < .05). Conclusion: Dexmedetomidine has a stable effect in cardiopulmonary priming solution.

With the development of information technology and the popularization of monitoring network, how to quickly and automatically detect abnormal behaviors in surveillance video is becoming more and more important for public security and smart city. The emergence of deep learning has greatly promoted the development of anomaly detection and much remarkable work has been presented on this topic. However, the existing approaches for anomaly detection generally encounter problems such as insufficient utilization of motion patterns and instability on different datasets. To improve the performance of anomaly detection in surveillance video, we propose a framework based on bidirectional prediction, which predicts the same target frame by the forward and the backward prediction subnetworks, respectively. Then the loss function is constructed based on the real target frame and its bidirectional prediction frame. Furthermore, we also propose an anomaly score estimation method based on the sliding window scheme which focuses on the foregrounds of the prediction error map. The comparison with the state-of-the-art shows that the proposed model outperforms most competing models on different video surveillance datasets.

Macular disorders, such as age-related macular degeneration (AMD) and diabetic macular edema (DME), have plagued humans for many years, yet the traditional recognition methods are often complicated, time-consuming and laborious. Therefore, this paper proposes an effective recombined residual convolutional neural network (CNN) with less computation to recognize AMD, DME and the normal of SD-OCT images. This paper adopts the18-layer Residual CNN as the basis and removes some of convolutional layers in the convolutional groups which extract low-level or high-level features, while add them to the middle part of the CNN, to enhance the ability of extracting mid-level features. This paper also adjusts the kernel size to change the visual receptive fields during convolution calculations. Experiments show that the recombined residual CNN with the kernel size of 3×3 shows better performance than the original one and recently proposed methods on the same dataset, with the highest overall accuracy up to 90%. Moreover, in terms of feature extraction complexity and time consumption, the recombined residual CNN is also more dominant.

Commodity display is very important in online shopping. In this paper, a method of reconstructing the commodity 3D model is proposed. Firstly, this paper accepts point clouds of a commodity from different angles. Secondly, before initial alignment, this paper compresses point clouds based on octree. Thirdly, this paper aligns all point clouds. At last, we reconstruct the commodity surface. In this step, a point cloud smoothing algorithm based on MLS and a Delaunay growth projection algorithm are proposed. Experimental results demonstrate that the computation time consumed by our improved registration algorithm is only one sixth of that of traditional one. And the quality only decreases slightly. The commodity 3D model has been successfully and efficiently reconstructed.

Faced with the problem of high cost and low accuracy of 3D modeling in industry, this paper build a low-cost 3D data measurement system with a self-made line laser scanner and improves the algorithm of extracting center of light bar. First, the self-made line laser scanner is used to collect the image information of the laser line on the surface of the measured object. Secondly, the center position of the line laser image is calculated by the improved gray centroid algorithm, and the depth information of the target is obtained based on the triangulation principle. Finally, the conversion from the depth information to the three-dimensional coordinate in space is completed through establishing the Cartesian coordinate system, and the three-dimensional coordinates are visualized as the point cloud model. According to the experimental data, the measurement error of the 3D data measurement system designed in this paper is 5mm in the short distance (less than 1m). And the improved gray centroid algorithm proposed is more accurate, faster and better robust, which will have a broader application prospect.

Superpixel segmentation is a widely used preprocessing method in computer vision, but its performance is unsatisfactory for color images in cluttered indoor environments. In this work, a superpixel method named weighted coplanar feature clustering (WCFC) is proposed, which produces full coverage of superpixels in RGB-depth (RGBD) images of indoor scenes. Basically, a linear iterative clustering is adopted based on a cluster criterion that measures the color similarity, space proximity and geometric resemblance between pixels. However, to avoid the adverse impact of RGBD image flaws and to make full use of the depth information, WCFC first preprocesses the raw depth maps with an inpainting algorithm called a Cross-Bilateral Filter. Second, a coplanar feature is extracted from the refined RGBD image to represent the geometric similarities between pixels. Third, combined with the colors and positions of the pixels, the coplanar feature constructs the feature vector of the clustering method, thus, the distance measure, as the cluster criterion, is computed by normalizing the feature vectors. Finally, in order to extract the features of the RGBD image dynamically, a content-adaptive weight is introduced as a coefficient of the coplanar feature, which strikes a balance between the coplanar feature and other features. Experiments performed on the New York University (NYU) Depth V2 dataset demonstrate that WCFC outperforms the available state-of-the-art methods in terms of accuracy of superpixel segmentation, while maintaining a high speed.

This paper aims to study the construction of 3D temperature distribution reconstruction system based on depth and thermal infrared information. Initially, a traditional calibration method cannot be directly used, because the depth and thermal infrared camera is not sensitive to the color calibration board. Therefore, this paper aims to design a depth and thermal infrared camera calibration board to complete the calibration of the depth and thermal infrared camera. Meanwhile a local feature descriptors in thermal and depth images is proposed. The belief propagation matching algorithm is also investigated based on the space affine transformation matching and local feature matching. The 3D temperature distribution model is built based on the matching of 3D point cloud and 2D thermal infrared information. Experimental results show that the method can accurately construct the 3D temperature distribution model, and has strong robustness.

This paper presents a novel 3D depth information extraction method without calibration. Firstly, this paper develops an omni-directional 3D camera system, which consists of a CCD camera, hyperbolic mirror, infrared laser diodes and diffractive of element (DOE). Secondly, a depth measurement model is proposed to obtain the 3D depth information. Finally, in order to calculate the speckle shift accurately between the reference image and the object image, a dot matrix pattern and sequence coding algorithm are designed to find the corresponding speckles in the two images. Experimental results show that the reconstructed depth data have a good correlation with the actual distance. The accuracy of the data is also found to be influenced by the distance between the object and the camera. A novel omni-directional 3D camera framework is proposed.The omni-directional camera and infrared dot matrix structured light can be merged.The model can reveal the relation between the object depth and its pixel offset.We can obtain the pixel offset with the sequence coding algorithm.

This paper aims to study the projector calibration algorithm in omnidirectional structured light (OSL). The traditional projector calibration method can not directly be used in omnidirectional system, because the projector is perpendicular to the omnidirectional camera in our experiment. Therefor, we design a complete algorithm for the calibration of omnidirectional structured light. Firstly, a calibration plane is applied. And a checkerboard calibration board are placed on that and the checkerboard pattern projected from the projector onto that. Secondly, the equation of the calibration plane are computed based on the extrinsic parameters of the calibration board. Thirdly, the corners of the projected pattern are detected in the image captured by omnidirectional camera. Lastly, 3D projected points for each projected corner are obtained based on the ray-plane intersection. We designed a complete set of OSL calibration toolbox based on the proposed methods in Matlab. The proposed method and toolbox in Matlab have been shown to be accurate and easyto-use in projector calibration.

In this paper, a novel design of wide-band wide-beam antenna is proposed. A lotus-leaf-shaped metal surface is used to increase beamwidth of antenna. Four L-shaped probes are applied for circularly-polarized excitation. In order to improve the radiation performance, a wideband frequency selective surface (FSS) is added into the antenna.

Supplemental Digital Content is available in the text, Cognitive impairment has been described in elderly subjects with high normal concentrations of serum uric acid. However, it remains unclear if gout confers an increased poorer cognition than those in individuals with asymptomatic hyperuricemia. The present study aimed at evaluating cognitive function in patients suffering from gout in an elderly male population, and further investigating the genetic contributions to the risk of cognitive function. This study examined the cognitive function as assessed by Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in 205 male gout patients and 204 controls. The genetic basis of these cognitive measures was evaluated by genome-wide association study (GWAS) data in 102 male gout patients. Furthermore, 7 loci associated with cognition in GWAS were studied for correlation with gout in 1179 male gout patients and 1848 healthy male controls. Compared with controls, gout patients had significantly lower MoCA scores [22.78 ± 3.01 vs 23.42 ± 2.95, P = .023, adjusted by age, body mass index (BMI), education, and emotional disorder]. GWAS revealed 7 single-nucleotide polymorphisms (SNPs) associations with MoCA test at a level of conventional genome-wide significance (P .05). Elderly male subjects with gout exhibit accelerated decline in cognition performance. Several neurodegenerative disorders risk loci were identified for genetic contributors to cognitive performance in our Chinese elderly male gout population. Larger prospective studies of the cognitive performance and genetic analysis in gout subjects are recommended.

Weld seam tracking technique was a hot topic of welding automation study. In this paper, depth information extraction of the weld seam was developed based on binocular stereo vision. Firstly, the binocular camera was calibrated. After acquiring the intrinsic and extrinsic parameters of both left and right camera, the acquired sample images were corrected using epipolar constraint method. Then, disparity map was acquired utilizing stereo matching method. Finally, depth information of weld seam was extracted on triangulation method. In the experiments, we compared the binocular stereo vision and line structured light method. The experimental results indicated that the visual information of binocular camera was so rich and can get more 3D shape information of the weldments, which were advantageous to weld large weldments. The measurement accuracy of linear structured light was high.

Detecting runtime anomalies is very important to monitoring and maintenance of distributed services. People often use execution logs for troubleshooting and problem diagnosis manually, which is time consuming and error-prone. In this paper, we propose an approach for automatic anomaly detection based on logs. We first mine a hybrid graph model that captures normal execution flows inter and intra services, and then raise anomaly alerts on observing deviations from the hybrid model. We evaluate the effectiveness of our approach by leveraging logs from an IBM public cloud production platform and two simulated systems in the lab environment. Evaluation results show that our hybrid graph model mining performs over 80% precision and 70% recall and anomaly detection performs nearly 90% precision and 80% recall on average.

Detecting execution anomalies is very important to monitoring and maintenance of cloud systems. People often use execution logs for troubleshooting and problem diagnosis, which is time consuming and error-prone. There is great demand for automatic anomaly detection based on logs. In this paper, we mine a time-weighted control flow graph (TCFG) that captures healthy execution flows of each component in cloud, and automatically raise anomaly alerts on observing deviations from TCFG. We outlined three challenges that are solved in this paper, including how to deal with the interleaving of multiple threads in logs, how to identify operational logs that do not contain any transactional information, and how to split the border of each transaction flow in the TCFG. We evaluate the effectiveness of our approach by leveraging logs from an IBM public cloud production platform and two simulated systems in the lab environment. The evaluation results show that our TCFG mining and anomaly diagnosis both perform over 80% precision and recall on average.