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Study design Interpretive description study. Purpose In management of patients with flexion tendon injuries, passive, control active and active motion protocols were proposed after repair to minimize tendon adhesion. The purpose of this study was to compare the excursion distance and the tension of Flexor Digitorum Profundus (FDP) during simulated active and passive motion using ultrasonography techniques using normal subjects. Methods Ultrasonographic assessment of FDP tendon of the middle finger was performed at the wrist level on 20 healthy college students using 3 types of treatment protocols: modified Kleinert protocol, modified Duran protocol, and active finger flexion protocol. The excursion distance was measured following the musculotendinous junction of FDP using the B mode ultrasound system. The elasticity of FDP tendon was measured using the shear wave elastography technique. The excursion distance and the elasticity value were compared among 3 protocols using one-way ANOVA analysis. Results Twelve male and 8 female students with mean age of 22.6 ± 1.8 years were invited to join the study. The excursion distance of FDP was 21.82 ± 3.77 mm using the active finger flexion protocol, 8.59 ± 2.59 mm using the modified Duran protocol, and 12.26 ± 2.71 mm using the modified Kleinert protocol. The elasticity was significantly higher in extension position when compared to passive flexion positions, but found lower than active flexion position. Discussion The active finger protocol was found to require strongest tension of the tendon and with longest excursion. There was similar tension generated using both passive motion protocols. The modified Duran protocol appeared to create less excursion upon movements than the modified Kleinert approach using the objective ultrasonic evaluation. It is suggested that if the surgical repair was strong and without any complications, the active flexion protocol might work best to regain tension excursion. However, if there are complex problems involved, then the Kleinert approach or Duran approach would be chosen.

Multiview fuzzy systems aim to deal with fuzzy modeling in multiview scenarios effectively and to obtain the interpretable model through multiview learning. However, current studies of multiview fuzzy systems still face several challenges, one of which is how to achieve efficient collaboration between multiple views when there are few labeled data. To address this challenge, this article explores a novel transductive multiview fuzzy modeling method. The dependency on labeled data is reduced by integrating transductive learning into the fuzzy model to simultaneously learn both the model and the labels using a novel learning criterion. Matrix factorization is incorporated to further improve the performance of the fuzzy model. In addition, collaborative learning between multiple views is used to enhance the robustness of the model. The experimental results indicate that the proposed method is highly competitive with other multiview learning methods.

DC circuit breakers (DCCBs) play a significant role in obtaining the reliability and stability of DC power systems. Selective and fast isolating of faults minimizes the power supply outages to end electricity users and protects the power converters implemented in the DC system. During the last decades, DCCBs have been deeply investigated in industry and academia. There has been impressive progress in terms of efficiency, response time, power-density, lifetime, and electrical operating ratings. Introducing wide bandgap (WBG) devices further has motivated researchers to propose more efficient and novel topologies. This paper provides a point of reference and a background review for DCCBs from novel aspects, fulfilling absent parts in the literature. The elaborated subjects include current commutation circuitry, DCCBs trip-curves developments, newly added functions to DCCBs, as well as marketing analysis. The advancements of DCCBs are surveyed and development needs are recognized. This paper aims to identify the future research needs of DCCBs for emerging DC power systems.

In this paper, we address the extended dissipativity (ED) performance of delayed T–S fuzzy model (TSFM)-based wind generation systems (WGSs). First, a concept of coupled leakage time-varying delay (CLTVD) is proposed to construct a more widespread TSFM. Second, the relaxed term with time-delay-product function (TDPF) is introduced. Then, a befitting Lyapunov-Krasovskii functional (LKF) is constructed, which can handle the delay and its derivation. Third, by using valid integral inequalities, new stabilization criteria are established. Meanwhile, the desired fuzzy quantized control with CLTVD is designed. Finally, simulation results are given to show the validity and superiority of our derived results.

Alternative splicing enables a gene translating into different isoforms and into the corresponding proteoforms, which actually accomplish various biological functions of a living body. Isoform-isoform interactions (IIIs) provide a higher resolution interactome to explore the cellular processes and disease mechanisms than the canonically studied protein-protein interactions (PPIs), which are often recorded at the coarse gene level. The knowledge of IIIs is critical to map pathways, understand protein complexity and functional diversity, but the known IIIs are very scanty. In this paper, we propose a deep learning based method called DeepIII to systematically predict genome-wide IIIs by integrating diverse data sources, including RNA-seq datasets of different human tissues, exon array data, domain-domain interactions (DDIs) of proteins, nucleotide sequences and amino acid sequences. Particularly, DeepIII fuses these data to learn the representation of isoform pairs with a four-layer deep neural networks, and then performs binary classification on the learnt representation to achieve the prediction of IIIs. Experimental results show that DeepIII achieves a superior prediction performance to the state-of-the-art solutions and the III network constructed by DeepIII gives more accurate isoform function prediction. Case studies further confirm that DeepIII can differentiate the individual interaction partners of different isoforms spliced from the same gene. The code and datasets of DeepIII are available at http://mlda.swu.edu.cn/codes.php?name=DeepIII.

This paper proposes a new modular active injection circuit (AIC) for solid-state circuit breaker (SSCB). The modular AIC is used to achieve soft-switching turn-off during DC current interruption. The proposed AIC presents following contributions: selectable resonant currents are obtained to obtain a reliable artificial zero crossing points in DC currents; current rating scalability is provided; passive components of the AIC are optimized using particle swarm optimization (PSO) algorithm and new proposed objective functions; an external wireless charger is developed to charge the injection capacitor of the AIC to provide voltage isolation, compactness, and flexibility in choosing the pre-charge voltage value on the injection capacitor; the proposed AIC needs no discharge circuits which simplifies the control and accelerates reclosing process. The proposed SSCB is simulated in a 6kV system in PSCAD/EMTDC in interrupting currents of normal and different scenarios . The designed SSCB can interrupt the DC currents up to 200A with the adaptive response time from 3.74s to 6.71s, faster response time in interrupting higher values of DC currents. The effectiveness of the proposed SSCB in practice is validated by experiments of a downsize 380V/20A prototype with only 2.8s response time and 99.91% efficiency.

Multitask modeling methods for Takagi-Sugeno-Kang (TSK) fuzzy systems exhibit better generalization ability attributed to the utilization of the knowledge of inter-task correlation. However, existing methods usually ignore the balance between the sharing of the common knowledge across multiple tasks and the preservation of the task-specific characteristics of each rule. To this end, we propose a novel manifold-regularized multitask modeling method for TSK fuzzy system by introducing low-rank and sparse structures into consequent parameters across multiple tasks. Specifically, we decompose the consequent parameters into two components the low-rank structure shared by multiple tasks and the task-specific component that encodes the sparse characteristics of the individual tasks. An efficient Augmented Lagrange Multiplier is developed to solve the optimization problem. The experimental results demonstrate that the proposed model significantly outperforms the existing methods.

Multi-label active learning (MAL) aims to learn an accurate multi-label classifier by selecting which examples (or example-label pairs) will be annotated and reducing query effort. MAL is more complicated, since one example can be associated with a set of non-exclusive labels and the annotator has to scrutinize the whole example and label space to provide correct annotations. Instead of scrutinizing the whole example for annotation, we may just examine some of its subexamples with respect to a label for annotation. In this way, we can not only save the annotation cost but also speedup the annotation process. Given that, we a two-stage Cost-effective MAL strategy (CMAL) by querying subexamples. CMAL firstly selects the most informative example-label pairs by leveraging uncertainty, label correlation and label space sparsity. Next, CMAL greedily queries the most probable positive subexample-label pairs of the selected example-label pairs. In addition, we propose rCMAL to account for the representative of examples to more reliably select example-label pairs. Extensive experiments on multi-label datasets show that our proposed CMAL and rCMAL can better save the query cost than state-of-the-art MAL methods. The contribution of leveraging label correlation, label sparsity and representative for saving cost is also confirm.

Feature extraction is one of the most important tasks in multi-label learning. The performance of multi-label classification can be effectively improved by reducing the dimension of multi-label datasets. Although research on multi-label feature extraction has received extensive attention and made significant progress, further improvement is still necessary. One of the concerns is robustness, where existing multi-label feature extraction algorithms are usually sensitive to noise and outliers. To address this issue, a robust multi-label relief feature selection algorithm based on fuzzy margin co-optimization, called ML-FS-FM, is proposed in this article. Under the multi-label learning framework, the classical fuzzy relief feature weighting algorithm is introduced to ML-FS-FM, which involves the mechanisms of fuzzy feature weighting, fuzzy nearest neighbor and fuzzy instance force coefficient, so as to effectively reduce the influence of noise and outliers, and to extract feature subsets that are beneficial to the classification task. The effectiveness of the proposed algorithm is verified by a large number of experiments on multi-label datasets.

Recent 3-D scanning techniques can produce various kinds of digitized 3-D data. Most of these scanned data are in a format of unstructured point clouds. Such low-level representation of 3-D data usually contains only geometric properties (point positions), while lacking higher level structure cues, for example, feature lines. Feature lines can be defined as a visually prominent characteristic of the shape, including edges, ridges, and valley lines in multiple scales, which can support a lot of downstream applications, such as shape reconstruction and analysis. We present a two-phase algorithm for extracting line-type features on point clouds. To extract both large-scale and shallow feature lines, we first define a statistical metric to detect all potential feature points while immune to the noise to some extent. Then, for correctly reconstructing the feature lines from these identified coarse feature points, we introduce an anisotropic contracting scheme to force feature points lying on the underlying real feature lines. To illustrate the reliability of our method, various experiments have been conducted on both synthetic and raw data. Both visual and quantitative comparisons show that our method is robust to noise and can correctly extract multiscale feature lines. In addition, our method is generally applicable to robotic picking.

Modular multilevel converters (MMC) with the conventional control are subjected to large capacitor voltage ripples, especially at low-line-frequencies. The existing attenuation approaches such as the second order or high-frequency harmonic injections are designed based on average models limiting the achievable performance. This letter aims to greatly increase the control time resolution, investigating the possibility to form the control in the timescale of a switching-cycle. The circulating current is allowed to have multiple changes in one switching period. This critical alternation equips the MMC with the capability to achieve voltage balancing in a single switching-cycle, shifting the MMC from a long-deemed line-cycle balancing converter to a switching-cycle balancing converter. The proposed approach is experimentally verified under dc-dc operation which is the most challenging scenario for conventional controls. Test results under 6 kV dc-link voltage are provided performed with the custom-build 10 kV SiC MOSFET based MMC prototype.

Band selection is one of the most effective methods to reduce the band redundancy of hyperspectral images (HSIs). Most existing band selection methods tend to regard each band as a whole, and then explore the band redundancy with the pixel-wise features directly. However, since the regions of HSIs corresponding to different objects have diverse spectral properties and spatial structure, such above scheme limits the performance of hyperspectral band selection due to the lack of spatial information. To address above issues, a novel band selection method via region-aware latent features fusion based clustering (RLFFC) is proposed. Specifically, we employ the superpixel segmentation to segment HSIs into multiple regions so that the spatial information of HSIs can be fully preserved. In order to capture the priori information, we construct its corresponding Laplacian matrix from which a group of low dimensional latent features are generated to further enhance the separability among different bands. Then, a shared latent feature representation of HSIs is obtained by fusing region-aware latent features to effectively capture the band redundancy of HSIs. Finally, the k -means clustering algorithm is utilized to obtain the index of the selected bands from the shared latent feature representation. As a result, the spectral and spatial properties are well exploited in the proposed method. Extensive experiments on four public hyperspectral datasets show that the proposed method achieves superior performance when compared with other state-of-the-art ones.

Anomaly detection for liquid rocket engine (LRE) is essential to improve the reliability and safety of space launch missions. However, it is difficult for existing methods to implement fast and precise detection with single-source information and absent anomalous samples. The Internet of Things (IoT) enables intelligent LRE anomaly detection via edge computing using multi-source data. This paper proposed an edge-based detection algorithm named stepwise generative adversarial network (StepGAN) for anomaly detection of LRE with multi-source fusion at the Edge of IoT. StepGAN incorporates deep autoencoder and relativistic generative adversarial network. Through multistage stepwise training with unlabeled normal data, an encoder-generator-discriminator network is obtained to identify LRE anomalies at the edge. In addition, by fusing multi-source information at feature level and aggregating neighboring information at decision level during real-time detection, the performance of StepGAN is further improved. To demonstrate its ability for IoT edge-based anomaly detection, the proposed method is compared with typical methods, using real static ignition data of LRE as cloud and edge data, and achieves the best result under multiple evaluation indexes. To evaluate the proposed fusing approach, a channel-varying case is carried out and extensive experimental results indicate its effectiveness.

We studied the lung-protective effect and mechanisms of the anti-inflammatory and antioxidant effects of ultra-short-wave diathermy (USWD) in a rat model of LPS-induced acute lung injury. Histological examination of the lung tissues was performed and the levels of oxidative stress-related factors and inflammatory cytokines were measured. It was shown that the lung injury score, the lung wet-to-dry weight ratio (W/D), oxidative stress-related factors malondialdehyde and acyl-CoA synthetase long-chain family member 4 (ACSL4), and inflammatory cytokines were increased after LPS administration, while USWD treatment reduced these parameters. In addition, superoxide dismutase and glutathione peroxidase 4 were decreased in rats with LPS-induced acute lung injury, while USWD therapy up-regulated the expression of these enzymes. Thus, USWD could antagonize lung injury by inhibiting oxidative stress and inflammatory response in rats with acute lung injury. USWD can be a promising adjunctive treatment to counter oxidative stress and inflammation and a potential therapeutic candidate for the treatment of patients with this pathology.

In this paper, we propose a simultaneous localization and mapping (SLAM) framework that combines the methods of the unscented article filter (UPF) and particle smothers (PS). The UPF is used to estimate the position and orientation of the mobile robot and particle smothers are used to update the locations of landmarks. We show that UPF is capable of estimating the pose of the robot more consistently and accurately than generic particle filter and unscented Kalman filter (UKF) do, especially when the dynamic models of the robot are highly nonlinear or noises are non-Gaussian. Then, the proposed SLAM framework is applied to high frequency (HF) band radio-frequency identification (RFID) system. Since detection model of the HF band RFID system is highly non-Gaussian, the traditional SLAM frameworks with the Gaussian assumption are not suitable and the proposed UPF-PS framework presents the superior performance. The proposed SLAM framework is evaluated through simulated experiments with a HF band RFID system and a range and bearing sensor. The comparisons between the proposed SLAM framework and four other SLAM frameworks, i.e., FastSLAM, UFastSLAM, and P-SLAM 1.0 and 2.0 are also given. Finally, the proposed SLAM framework is verified in a real experimental environment. The experimental results validate the effectiveness and superiority of the proposed SLAM framework.

International audience; Invasive growth along white matter (WM) tracts is one of the most prominent clinicopathological features of glioma and is also an important reason for surgical treatment failure in glioma patients. A full understanding of relevant clinical features and mechanisms is of great significance for finding new therapeutic targets and developing new treatment regimens and strategies. Herein, we review the imaging and histological characteristics of glioma patients with WM tracts invasion and summarize the possible molecular mechanism. On this basis, we further discuss the correlation between glioma molecular typing, radiotherapy and tumor treating fields (TTFields) and the invasion of glioma along WM tracts.

Images obtained in dim light do not clearly represent the target scene, limiting information transmission on the image carrier. This study proposes a texture‐preserving image enhancement method, i.e. ETPR. The proposed method draws the illumination map of a low light image by Max‐RGB, and then applies the weighted median filter algorithm and Retinex to improve the illumination map further. Then, the enhanced image is changed from RGB mode to YCbCr mode and the texture of the luminance component Y is described. It is achieved by denoising the texture of the image to be enhanced, and then the final enhanced image is obtained by the method of sub‐region image fusion denoising. The effectiveness of ETPR is established by comparing it with existing enhancement technologies using public data and real images.

Sliding-window-based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates applications such as multisource or multiscale data fusion, data assimilation, and product uncertainty quantification since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of handling uncertainty in HSI patch mixing caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10%-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive.

The study aimed to compare the performance of human papillomavirus (HPV)-based strategies to cytology for detecting cervical intraepithelial neoplasia grade 3 or worse (CIN3+) in routine program in China. The study included 50,434 women ages 30–64 years from January 2015 to December 2019, to assess four strategies: cytology with HPV triage (strategy 1), primary HPV testing with reflex cytology (strategy 2), primary HPV testing with HPV-16/18 genotyping and reflex cytology for non-16–18 high-risk HPV genotypes (strategy 3), and co-testing (strategy 4). The main outcomes were sensitivity for CIN3+ and colposcopy referral rate. Overall, the rates of HPV positivity and cytologic abnormality were 7.0% [95% confidential interval (CI), 6.8–7.2] and 3.5% (95% CI, 3.3–3.6], respectively. The sensitivity for CIN3+ were 88.5% for strategy 4, 83.2% for strategy 3, 64.6% for strategy 1, and 60.1% for strategy 2. The relative sensitivity of strategy 4 and strategy 3 compared with strategy 1 for detecting CIN3+ were 1.38 (95% CI, 1.24–1.54) and 1.29 (95% CI, 1.14–1.46), respectively. The colposcopy referral rates of strategies 4 and 3 were significantly higher than that of strategy 1 (2.4% and 2.2% vs. 1.4%, P < 0.001). In conclusion, co-testing and primary HPV testing with HPV-16/18 genotyping and reflex cytology improved the sensitivity for CIN3+ compared with cytology but increased the colposcopy referral rate. Long-term negative predicted value for HPV-negative women should be studied to determine the screening interval. Our study provides further evidence to the introduction of HPV-based strategies in China. Prevention Relevance: Both co-testing and primary HPV testing with HPV-16/18 genotyping and reflex cytology triage provided higher sensitivity for detecting CIN3+; however, the number of colposcopy referrals also increased compared with cytology in a routine program. It has great public health implications for the introduction of HPV-based screening strategies in China.

The B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) has shown its effectiveness for developmental dysplasia of the hip (DDH) in infants. In this work, a two-stage meta-learning based deep exclusivity regularized machine (TML-DERM) is proposed for the BUS-based CAD of DDH. TML-DERM integrates deep neural network (DNN) and exclusivity regularized machine into a unified framework to simultaneously improve the feature representation and classification performance. Moreover, the first-stage meta-learning is mainly conducted on the DNN module to alleviate the overfitting issue caused by the significantly increased parameters in DNN, and a random sampling strategy is adopted to self-generate the meta-tasks; while the second-stage meta-learning mainly learns the combination of multiple weak classifiers by a weight vector to improve the classification performance, and also optimizes the unified framework again. The experimental results on a DDH ultrasound dataset show the proposed TML-DERM algorithm achieves the superior classification performance with the mean accuracy of 85.89%, sensitivity of 86.54%, and specificity of 85.23%.

Background: The purpose of this study was to investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma multiforme (GBM), which may be of value to optimize patients’ management. Methods: We retrospectively evaluated the clinical information and MR images (T1-weighted and fluid-attenuated inversion recovery sequences) of 22 patients with histologically diagnosed GBM. Kaplan-Meier’s method was used for survival analysis. Peritumoral edema regions were manually segmented by an expert into recurrence and non-recurrence subregions. A set of 94 radiomic features were obtained from both subregions using a three-dimensional slicer. Features with significant differences between the two subregions were identified using the paired t test. Subsequently, the data of two patients from The Cancer Genome Atlas (TCGA) database were used to evaluate the clinical value of the selected features. Findings: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from TCGA database with pathologically confirmed diagnosis of GBM. Interpretation: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM. Funding Information: This work was supported by grants from the Natural Science Fund of Guangdong Province (No. 2017A030313597), “Climbing Program” Special Fund of Guangdong Province (No. pdjh2019b0100, No. pdjh2020b0112) and Southern Medical University (No. LX2016N006, No. KJ20161102, No.201912121004S, No.201912121013，No. S202012121088, No. X202012121354). Declaration of Interests: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Ethics Approval Statement: Ethics committee approval and patient consent were obtained.

Background: HIV-related stigma poses a crucial barrier to HIV therapy and prevention worldwide. Stigma taking place in healthcare settings has also been a global challenge for years. Objective: To measure HIV-related stigma among health care facilities in Baoan, Shenzhen and to take decision regarding stigma-reduction intervention. Methods: We conducted a cross-sectional survey in Shenzhen Baoan Women's and Children's Hospital, China in February 2019. We collected data on HIV-related stigma from 207 hospital staff randomly selected for an anonymous survey online by a standardized questionnaire. We analyzed the influence factors on HIV-related stigma taking place in health facilities using the statistical method. Results: The percentages of worry expressed when touching clothing, dressing wound, and drawing blood for PLWH were 60.9%, 84.5%, and 82.6%, respectively. 76.3% of respondents reported at least one form of extra precautions taken during service provision to PLWH. Younger hospital employees with less working experience were more likely concerned about occupation risk of HIV infection (P Conclusion: HIV-related stigma in health facilities remains a significant challenge, and evidence- based interventions at both systematic and individual levels are needed for improvements.

Based on localized spoof surface plasmons (LSSPs), a new microwave nondestructive testing (NDT) sensor is proposed to measure the thickness of magnetic absorbing coating on metallic surface. The metallic spiral structure (MSS) sensor fed by microstrip ring generates the LSSPs, which is used to interact with material under test (MUT) and measure the thickness of MUT. Through measurement mechanism and numerical analysis, the magnitude of the reflection coefficient is sensitive to the thickness variation of the coating. Taking a kind of magnetic absorbing film A4000 as an example, the experimental results reveal that the MSS sensor can estimate the film thickness with a range of 0.05-1.03mm, and the error is within 6%. The highest resolution can reach 0.05mm in the experiment. In fact, the formula shows that the highest resolution can reach 0.01mm. Compared with conventional monopole antenna, the microstrip ring is a new way to feed the MSS, which makes the sensor structure more stable and more compact. The sensitivity of the sensor is improved by using electric field and magnetic field to interact with the MUT together. The sensor has the advantages of high resolution, small error, stable performance and no limit to the size of the MUT.

Performance-based salary distribution is one of the important contents of modern hospital management system. In general, the distribution of performance salary in public hospitals of China can be divided into two stages: one is from hospitals to departments, the other is from departments to individuals. It is of great significance to improve the performance-based salary distribution system in clinical departments of public hospital, which is beneficial to ensure the public nature, motivate hospital staff to work hard, and raise public healthcare service quality. Therefore, this paper focuses on the issue of performance-based salary distribution in clinical departments of public hospital, adopts super-efficiency DEA model to evaluate the performance of each clinical department, introduces a new utility function for processing the original values of DEA efficient DMUs in order to encourage more clinical departments to pursue higher performance, and finally verifies the comprehensive model by empirical analysis. The result of empirical analysis shows that the performance of DMU7 is highest with an efficiency value of 1.53, followed by DMU3, DMU8, and DMU1. The efficiency value of DMU9 is lowest in all clinical departments.

PURPOSE Pneumothorax is a life-threatening emergency that requires immediate treatment. Frontal-view chest X-ray images are typically used for pneumothorax detection in clinical practice. However, manual review of radiographs is time-consuming, labor-intensive, and highly dependent on the experience of radiologists, which may lead to misdiagnosis. Here, we aim to develop a reliable automatic classification method to assist radiologists in rapidly and accurately diagnosing pneumothorax in frontal chest radiographs. METHODS A novel residual neural network (ResNet)-based two-stage deep-learning strategy is proposed for pneumothorax identification: local feature learning (LFL) followed by global multi-instance learning (GMIL). Most of the nonlesion regions in the images are removed for learning discriminative features. Two datasets are used for large-scale validation: a private dataset (27,955 frontal-view chest X-ray images) and a public dataset (the National Institutes of Health (NIH) ChestX-ray14; 112,120 frontal-view X-ray images). The model performance of the identification was evaluated using the accuracy, precision, recall, specificity, F1-score, receiver operating characteristic (ROC), and area under ROC curve (AUC). 5-fold cross validation is conducted on the datasets, and then the mean and standard deviation of the above-mentioned metrics are calculated to assess the overall performance of the model. RESULTS The experimental results demonstrate that the proposed learning strategy can achieve state-of-the-art performance on the NIH dataset with an accuracy, AUC, precision, recall, specificity, and F1-score of 94.4 ± 0.7%, 97.3 ± 0.5%, 94.2 ± 0.3%, 94.6 ± 1.5%, 94.2 ± 0.4%, and 94.4 ± 0.7%, respectively. CONCLUSIONS The experimental results demonstrate that our proposed CAD system is an efficient assistive tool in the identification of pneumothorax. This article is protected by copyright. All rights reserved.

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.

In the present study, the flexural behavior of concrete-filled steel tube (CFST) beams strengthened by welding round steel at the soffit of the beam has been investigated. First, a total of six specimens were tested under four-point bending load, which includes one reference beam and five retrofitted beams strengthened by welding round steel of different diameters at the soffit of the beams. Then, the corresponding nonlinear finite-element models have been developed and calibrated against the experimental results. By combining indoor experiments and numerical simulations, the bending moment versus mid-span curvature curves, the flexural capacities, flexural stiffness, ductility, vertical deflection curves, strain distribution across the cross-section, and neutral axis offset of the test beams have been systematically analyzed. The results show that the welded round steel at the soffit can improve the flexural bearing capacity and stiffness of the CFST beams significantly. This can be attributed to the CFST beams benefitting from the flexural bearing effect of the round steel and the downward offset of the neutral axis which causes more core concrete to be in compression. Further, the larger the diameter of the round steel, the greater the benefit. Finally, comparisons have been made with the flexural stiffness calculated using the existing design codes, such as AIJ standard, BS5400, Eurocode 4 and AISC specification. The comparisons show that the flexural stiffness of the unstrengthened CFST beam can be predicted by AIJ and BS5400 perfectly, whereas those of the strengthened CFST beams can only be accurately calculated by AISC.

Objectives: To describe and compare the research and development (R&D) pipeline of cancer new drugs and newly approved drugs in China and the USA in 2020, thus to provide decision-making evidence for related stakeholders. Methods: Clinical trials and tested cancer new drugs information in China and the USA were respectively acquired from Information Disclosure Platform for Drug Clinical Studies and Trialtrove database. Drug approval was tracked from the official release. Subgroup comparison in terms of initiated trials and drugs were conducted between the two countries. Results: In 2020, 577 trials on 335 cancer new drugs were registered in China, accounting for 22.6% of all clinical drug trials, while in the USA, 916 trials on 678 cancer new drug trials were captured, accounting for 19.9% of the total. Relatively, a lower proportion of earlier phase (76.9% vs 87.4%), global (17.7% vs 39.0%), and top 20 pharmaceutics contribution (15.8% vs 43.2%) were found for cancer drug trials initiated in China. The fight against solid tumor took top billing in both countries, and the different distribution of cancer indications associated with cancer spectrum was also observed. Compared with the USA, more targeted agents (87.5% vs 77.0%, P

Background and Objective: Osteoporosis is a worldwide healthcare challenge. Conventional medications for osteoporosis prevention are not clinically effective or associated with gastrointestinal tract adverse effects. The present study aimed to comparatively investigate the effects of technetium-99 conjugated with methylene diphosphonate (99Tc-MDP) and calcium carbonate and alendronate on the prevention and treatment of osteoporosis in glucocorticoid-induced osteoporosis rabbit model through evaluating bone alkaline phosphatase (B-ALP), TRAP-5b levels and histopathological parameters. Methods: Forty healthy female New Zealand rabbits were randomly divided into five groups (each n=8), including control group (Control Group), osteoporosis model group (GIO Group), osteoporosis model + 99Tc-MDP group (99Tc-MDP Group), osteoporosis model + alendronate group (Alendronate Group), and osteoporosis model + calcium carbonate group (calcium carbonate Group). Animals in each group were treated with corresponding interventions for 14 weeks. The blood samples were collected at the first and 14th week, and B-ALP and TRAP-5b levels were detected by enzyme- linked immunosorbent assay (ELISA). The rabbits were anesthetized at the 14th week, and pathological cytological observation was performed on both femurs. Results: The age and weights of rabbits in different groups had no statistically significant differences (P>0.05). B-ALP levels in serum of all groups except for the Control Group decreased after treatment, but the differences were not statistically significant (P>0.05). TRAP-5b levels in serum of all groups increased after treatment. Specifically, differences in the GIO Group and calcium carbonate group were statistically significant (P99Tc-MDP Group and alendronate Group were not statistically significant (P99Tc- MDP group and alendronate group was similar to control group and GIO group. Moreover, the calcium carbonate group and GIO group exhibited similar bone tissue morphology. Conclusions: 99Tc-MDP has a preventive effect on the glucocorticoid-induced osteoporotic rabbit model. This osteoporosis preventive effect might be attributed to the capacities of 99Tc-MDP in promoting the osteoblasts generation and inhibiting the generation and reducing the activity of osteoclasts.

Yucheng Fu, Guoyu He, Zhuochao Liu, Jun Wang, Zhusheng Zhang, Qiyuan Bao, Junxiang Wen, Zhijian Jin, Weibin Zhang Department of Orthopedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopedics, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, People’s Republic of ChinaCorrespondence: Weibin ZhangDepartment of Orthopedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopedics, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, People’s Republic of ChinaTel +86-21-64370045, Ext 666983Fax +86-21-54660217Email zhangweibin10368@163.comBackground: Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature.Methods: The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan–Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry.Results: The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients’ samples revealed the same result.Conclusion: The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.Keywords: osteosarcoma, inflammatory response, metastasis, prognosis, immune

Increasing the aperture of the parabolic trough concentrator (PTC) can effectively reduce costs and help promote. This paper proposed a circle and semicircle absorber tubes (AT) with a flat reflector (FR) in it for the large-aperture PTC system and the size of the AT was determined for the PTC with 8 m aperture width and 80° half rim-angle. The result shown the PTC system using a semi-circular AT with a diameter of 100 mm has the highest average thermal efficiency of 71.6% with DNI 400–1000 W/m2 at about 500 °Cand has best uniformity of 38.8%. However, its deformation is highest and 1.7 and 1.6 times other two ATs. Therefore, how to reduce the deflection has become a research direction in the future. Currently, the circular AT with a diameter of 80 mm is the most practical with an average thermal efficiency of 69.1% and a minimum deflection.

Objective Heightened inflammation, dysregulated immunity, and thrombotic events are characteristic of hospitalized COVID‐19 patients. Given that platelets are key regulators of thrombosis, inflammation, and immunity they represent prime candidates as mediators of COVID‐19‐associated pathogenesis. The objective of this study was to understand the contribution of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) to the platelet phenotype via phenotypic (activation, aggregation) and transcriptomic characterization. Approach and Results In a cohort of 3915 hospitalized COVID‐19 patients, we analyzed blood platelet indices collected at hospital admission. Following adjustment for demographics, clinical risk factors, medication, and biomarkers of inflammation and thrombosis, we find platelet count, size, and immaturity are associated with increased critical illness and all‐cause mortality. Bone marrow, lung tissue, and blood from COVID‐19 patients revealed the presence of SARS‐CoV‐2 virions in megakaryocytes and platelets. Characterization of COVID‐19 platelets found them to be hyperreactive (increased aggregation, and expression of P‐selectin and CD40) and to have a distinct transcriptomic profile characteristic of prothrombotic large and immature platelets. In vitro mechanistic studies highlight that the interaction of SARS‐CoV‐2 with megakaryocytes alters the platelet transcriptome, and its effects are distinct from the coronavirus responsible for the common cold (CoV‐OC43). Conclusions Platelet count, size, and maturity associate with increased critical illness and all‐cause mortality among hospitalized COVID‐19 patients. Profiling tissues and blood from COVID‐19 patients revealed that SARS‐CoV‐2 virions enter megakaryocytes and platelets and associate with alterations to the platelet transcriptome and activation profile.

Background The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT. Methods Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database. Results In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT. Conclusions These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Detecting and tracking moving vehicles is one of the most fundamental functions of autonomous vehicles driving in complex scenarios, as it forms the foundation of decision making and path planning. In order to estimate the pose information of moving vehicles accurately, 3D-LiDAR is widely used for accurate distance data. This paper proposed a real-time tracking algorithm based on L-Shape fitting. The algorithm detects the corners of moving vehicles and uses RANSAC to take a limited amount of noisy data. In addition, a vehicle tracking system with multi-weight Rao-Blackwellized Particle Filtering (RBPF) is built upon the orientation estimation given by L-Shape fitting. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired from an autonomous vehicle at Carnegie Mellon University. Furthermore, the proposed solution is implemented in an autonomous vehicle at Tongji University. The experiments illustrate that the proposed algorithm achieves real-time performance, mitigates the effect of noisy data and improves estimation accuracy.

Triple-negative breast cancer (TNBC), which is the most malignant subtype of breast cancer (BC), accounts for 10%–20% of all BC cases. TNBC, which occurs more frequently in young women, is characterized by high rates of cell proliferation and metastasis and poor prognosis. Chemotherapy is the primary systemic therapeutic strategy for TNBC. However, chemotherapy is largely unsuccessful, and effective targeted therapies for TNBC have not been established. Therefore, it is a matter of great urgency to identify precise molecular targets for the promising prognosis of patients with TNBC. Circular RNAs (circRNAs), which are a type of non-coding RNAs (ncRNAs), are abundantly expressed in the eukaryotic cells and exhibit diverse cellular functions. The roles of circRNAs are to sponge microRNA or RNA-binding proteins, regulate gene expression, and serve as templates for translation. Here, we review the current findings on the potential of circRNAs as a diagnostic, prognostic, and therapeutic biomarker for TNBC. However, further studies are essential to elucidate the functions of circRNAs in TNBC. This review also discusses the current limitations and future directions of TNBC-associated circRNAs, which can facilitate the translation of experimental research into clinical application., Graphical abstract, In this manuscript, the efficacy of current therapeutic strategies was addressed for human triple-negative breast cancer (TNBC). Then, dysregulated circular RNAs (circRNAs) were summarized to reveal the biomarker potential and therapeutic mechanism. Finally, novel perspectives were provided for future studies to improve the poor overall survival of TNBC.

The popularity of smartphones and the advent of GPS positioning and wireless communication technologies in the recent decade have facilitated large-scale implementations of dynamic ridepooling services, such as Uber Pool, Lyft Line, and Didi Pinche. As in such services trips usually start before the appearance of pooling partners, knowing the probability of getting matching with another order (i.e., matching probability), the expected detour distance, and the expected shared distance before the start of each trip is essential for passengers to evaluate their willingness to pool and for ridepooling platforms to offer attractive discounts. In this paper, assuming that every ridepooling passenger shares vehicle space with at most one another during the entire trip, and ridepooling orders in each (origin-destination) OD pair appear following a Poisson process with a given rate, we propose a mathematical modeling approach to predict the matching probability, the expected ride distance, and the expected shared distance of each order under a first-come-first-serve strategy in dynamic ridepooling service. The method defines unmatched passengers at different locations along the exclusive-riding path of each OD pair into different seeker- and taker-states, formulates the complex interdependency of the matching probabilities, matching rates and arrival rates of (unmatched) passengers in different states into a system of nonlinear equations, and generates the matching probabilities and expected ride/shared distances of all OD pairs simultaneously. Under the same first-come-first-serve strategy, we simulated the occurrence, movements and state transitions of ridepooling orders based on a 30*30 grid network and the real network of Haikou City in China. In comparison with simulation results, we show that the method proposed in this paper can generate fairly satisfactory predictions under diverse matching conditions and demand intensities.

Background Recent studies have indicated that P2Y receptors in spinal microglia play a role in the development of neuropathic and inflammatory pain. However, it remains unclear whether P2Y receptors in microglia are involved in the pathogenesis of migraine. Therefore, the aim of this study was to investigate the role of microglial P2Y14 receptor in trigeminal cervical complex (TCC) in migraine. Methods We used a rat model of migraine induced by repeated inflammatory stimulation of the dura and examined the expression of P2Y14 receptor in the TCC in migraine rats by Western Blotting and immunofluorescence staining. Then, we determined the effect of P2Y14 antagonist PPTN on inflammatory soup (IS)-induced mechanical allodynia, microglial activation and ERK expression in TCC. Results The expression level of P2Y14 receptor increased significantly in microglia in TCC after 4 or 7 days of repeated IS stimulation of the dura. Application of PPTN significantly attenuated the decrease of periorbital pain threshold in migraine model rats. In addition, repeated IS stimulation of the dura induced the activation of microglia and the phosphorylation of the ERK1/2 in microglia in TCC, which were abolished by the application of PPTN. Conclusion Our findings suggest that the increased P2Y14 receptor in microglia in TCC play a crucial role in the generation of mechanical allodynia in migraine rat model. Furthermore, the activation of the P2Y14 receptor is involved in microglial activation and ERK phosphorylation as well. The P2Y14 receptor in microglia might be used as a potential target for migraine treatment.

Background: Anopheles gambiae (An. gambiae) is considered the most effective malaria vector worldwide and is widely distributed in Africa. The purpose of this study is to determine the potential impact of climate change on An. gambiae and predict the present and future potential suitable habitat globally. Methods: In this study, environmental variables, global occurrence data of An. gambiae, and the maximum entropy model (MaxEnt) were used to evaluate the contribution of environmental factors and predict the habitat suitability of An. gambiae under the current and future scenarios. Results: Among all environmental variables, isothermality (Bio3, 34.5%) contributed the most to An. gambiae distribution. Under current climate conditions, the potential suitable areas for An. gambiae are mainly located near the equator (approximately 30°N-30°S), with a total area of 16.58 million km2, including central and northern South America, a fraction of areas near the equator of North America, central and southern Africa, some tropical regions of southern Asia, and small areas of Oceania. The areas of potential suitable habitats would be reduced to varying degrees in future climate scenarios. Conclusions: Potential suitable habitats for An. gambiae may not be limited to Africa. Necessary surveillance and preventive measures should be undertaken in high-risk regions, including those outside Africa, to monitor and control the spread of An. gambiae.

To improve the detection for different fabric types and defect kinds, an approach based on K-Singular Value Decomposition (K-SVD) dictionary learning method is designed to detect fabric defects, wh...

With the continuous development of information technology, the amount of data in information systems continues to increase. Big data has become an inevitable trend in the development of information technology, and the problems faced by the secure storage of big data have also become problems that must be solved. On the one hand, the research in this paper meets the storage requirements of unstructured massive data. On the other hand, it applies digital certificate authentication technology to realize storage security identity authentication based on digital certificate, and solves the application problem of high-security identity authentication in distributed storage. The innovation of this paper is to use the corresponding virtual storage space mapping algorithm to realize the unified management of resource pool. And before data transmission, the digital certificate authentication method is used to realize the authority control of the storage resource pool. This paper has completed the design and implementation of the distributed secure storage system, and completed the related testing and verification work, which significantly improved the performance of data storage and solved the problem of big data secure storage.

Chronic visceral pain (CVP) is one of the common symptoms of many diseases triggered by underlying diseases of the internal organs of the human body. Its causes include vascular mechanisms, mechanical factors, persistent inflammation, and unexplained functional mechanisms. Although the pathogenesis is unclear, more and more research has begun to shift from the neuronal aspect to the glial cells in recent years. Some data highlight that the spinal glial cells, particularly the microglia and astrocytes, play an essential role in CVP. Based on this, we highlight the mechanisms of microglia and astrocytes in CVP concerning the release of cytokines, chemokines, and neuroactive substances and alterations in intracellular signaling pathways during the process. Finally, because CVP is widespread in various diseases, we present future perspectives targeting microglia and astrocytes for treatment.

The p-i-n junction structure has garnered great interest from researchers due to its increased photoactive area and wider space charger region compared with that of the p-n junction. Herein the ult...