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Platelet-derived growth factor AA (PDGF-AA) is an important promoter of tissue injury repair and might be a candidate for improving the mechanical properties of repaired tendons. Here, we designed a PDGF-AA-modified poly(lactide-co-glycolide) acid (PLGA) electrospun fibers to promote tendon rehabilitation after injury. In the present study, we grafted PDGF-AA on the surface of PLGA. In structural experiments, we found that the hydrophilicity of PLGA containing PDGF-AA (PLGA-PDGF-AA) increased, but the strength of the material did not change significantly. Moreover, no significant changes in tendon cell proliferation and viability were observed in the PLGA-PDGF-AA treatment compared with the control group. The mouse tendon injury model ( n = 9) experiment illustrated that PLGA-PDGF-AA effectively promoted tendon healing, and we confirmed that PLGA-PDGF-AA promoted collagen synthesis and deposition by immunohistochemistry and RT-PCR. Moreover, the mechanical strength of PLGA-PDGF-AA-treated mouse ( n = 9) tendon tissue was also higher than that of the PLGA-treated group alone. In conclusion, PLGA-PDGF-AA promoted regeneration after tendon injury and serves as a potential adjuvant material for surgical tendon injury repair.

Imaging registration has a significant contribution to guide and support physicians in the process of decision-making for diagnosis, prognosis, and treatment. However, existing registration methods based on the convolutional neural network cannot extract global features effectively, which significantly influences registration performance. Moreover, the smoothness of the displacement vector field (DVF) fails to be ensured due to the miss folding penalty.In order to capture abundant global information as well as local information, we have proposed a novel 3D deformable image registration network based on Transformer (TransDIR). In the encoding phase, the transformer with the atrous reduction attention block is designed to capture the long-distance dependencies that are crucial for extracting global information. A zero-padding position encoder is embedded into the transformer to capture the local information. In the decoding phase, an up-sampling module based on an attention mechanism is designed to increase the significance of ROIs. Because of adding folding penalty term into loss function, the smoothness of DVF is improved.Finally, we carried out experiments on OASIS, LPBA40, MGH10, and MM-WHS open datasets to validate the effectiveness of TransDIR. Compared with LapIRN, the DSC score is improved by 1.1% and 0.9% on OASIS and LPBA40, separately. In addition, compared with VoxelMorph, the DSC score is improved by 2.8% on the basis of the folding index decreased by hundreds of times on MM-WHS.The results show that the TransDIR achieves robust registration and promising generalizability compared with LapIRN and VoxelMorph.

Aim. To explore the mechanism of resveratrol in reducing the soft tissue damage of osteoarthritis (OA) based on network pharmacology. Methods. Pharmmapper was used to predict the target of resveratrol, OMIM and Genecards were used to collect OA-related disease genes, and David ver 6.8 was used for enrichment analysis. Then, animal experiments were carried out for verification. The rat OA model was established and the rats were randomly divided into 4 groups: model group, resveratrol low-dose group, resveratrol high-dose group, and blank control group for follow-up experiments. Hematoxylin-eosin (HE) staining was used to detect the degree of pathological damage of rat bones and joints. Enzyme-linked immunosorbent assay (ELISA) was used for the content of inflammatory factors. Western blot was used to detect the expression of Toll-like receptor 4 (TLR4), Myeloid differentiation factor 88 (MyD88), nuclear factor kappa B protein (NF-κB), cysteine protease-9 (CASP-9), Bcl-2 protein, and Bax protein. Results. Through network pharmacological analysis, this study found that resveratrol may regulate the TLR4 signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, Osteoclast differentiation, Rheumatoid arthritis, etc. Animal experiments showed that compared with the model group, the pathological damage of bone and joint in the resveratrol low-dose and high-dose groups was significantly improved. Compared with the model group, the serum levels of IL-1beta, IL-6, IL-17, TNF-α, and MCP-1 in the resveratrol low-dose and high-dose groups were significantly reduced ( P P P P

Broad Learning System (BLS) is an emerging network paradigm that has received considerable attention in the regression and classification fields. However, there are two deficiencies which seriously hinder its deployment in real applications. The first one is the internal correlations among samples are not fully considered in the modeling process. Second, the strict binary label matrix utilized in BLS provides little freedom for classification. In this paper, to address the above issues, we propose to impose group-sparsity constraints on the class-specific transformed features and label error terms, respectively. The effect is not only the more appropriate margins between data can be preserved, but also the learnt label space can be flexible for recognition. As a result, the obtained projection matrix can show more vital discriminative ability. Further, we employ the alternating direction method of multipliers to solve the resulting optimization problem. Extensive experiments and analysis on diverse benchmark databases are carried out to confirm our proposed model’s superiority in comparison with other competing classification methods.

Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the output layer, which neglects vast amounts of redundant information. It usually leads to be inefficient and overfitting. To resolve this issue, we propose sparse regularization-based compact broad learning system (CBLS) framework, which can simultaneously remove redundant nodes and weights. To be more specific, we use group sparse regularization based on [Formula: see text] norm to promote the competition between different nodes and then remove redundant nodes, and a class of nonconvex sparsity regularization to promote the competition between different weights and then remove redundant weights. To optimize the resulting problem of the proposed CBLS, we exploit an efficient alternative optimization algorithm based on proximal gradient method together with computational complexity. Finally, extensive experiments on the classification task are conducted on public benchmark datasets to verify the effectiveness and superiority of the proposed CBLS.

Magnetic resonance imaging (MRI) is one of the most important modalities for clinical diagnosis. However, the main disadvantages of MRI are the long scanning time and the moving artifact caused by patient movement during prolonged imaging. It can also lead to patient anxiety and discomfort, so accelerated imaging is indispensable for MRI. Convolutional neural network (CNN) based methods have become the fact standard for medical image reconstruction, and generative adversarial network (GAN) have also been widely used. Nevertheless, due to the limited ability of CNN to capture long-distance information, it may lead to defects in the structure of the reconstructed images such as blurry contour. In this paper, we propose a novel Swin Transformer-based dual-domain generative adversarial network (SwinGAN) for accelerated MRI reconstruction. The SwinGAN consists of two generators: a frequency-domain generator and an image-domain generator. Both the generators utilize Swin Transformer as backbone for effectively capturing the long-distance dependencies. A contextual image relative position encoder (ciRPE) is designed to enhance the ability to capture local information. We extensively evaluate the method on the IXI brain dataset, MICCAI 2013 dataset and MRNet knee dataset. Compared with KIGAN, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are improved by 6.1% and 1.49% to 37.64 dB and 0.98 on IXI dataset respectively, which demonstrates that our model can sufficiently utilize the local and global information of image. The model shows promising performance and robustness under different undersampling masks, different acceleration rates and different datasets. But it needs high hardware requirements with the increasing of the network parameters. The code is available at: https://github.com/learnerzx/SwinGAN.

ObjectiveTo evaluate the randomized controlled trials (RCTs) of Curcumin and Curcuma longa Extract in the treatment of autoimmune diseases.MethodsDatabases such as Embase, Web of Science, PubMed and The Cochrane Library were searched from the database establishment to February 2022 to collect RCTs of Curcumin and Curcuma longa Extract in the treatment of autoimmune diseases. Then the literature was screened and the data were extracted. Meta-analysis was performed using RevMan 5.3 software.ResultsA total of 34 records were included, involving 31 RCTs and 10 types of autoimmune disease. Among them, ankylosing spondylitis (AS) involves one RCT, Behcet ‘s disease (BD) involves one RCT, Crohn ‘s disease involves two RCTs, multiple sclerosis (MS) involves two RCTs, oral lichen planus involves six RCTs, psoriasis involves two RCTs, rheumatoid arthritis (RA) involves five RCTs, systemic lupus erythematosus (SLE) involves two RCTs, arteritis involves one RCT, ulcerative colitis (UC) involves nine RCTs. Among them, most of the RCTs of ulcerative colitis (UC), oral lichen planus, RA showed that curcumin and curcumin extracts improved clinical or laboratory results. Crohn ‘ s disease, MS, SLE, psoriasis included two RCTs; they all showed improvements (at least one RCT reported improvements in clinical outcomes). AS, BD and arteritis included only one RCT, and the clinical results showed improvement. However, due to the small number of RCTs and the small number of patients involved in each disease, there is still a need for more high-quality RCTs.ConclusionCurcumin and Curcuma longa Extract had good clinical efficacy in the treatment of Psoriasis, UC and RA, so Curcumin and Curcuma longa Extract could be used in the treatment of the above diseases in the future. The results of Meta-analysis showed that Curcumin and Curcuma longa Extract did not show efficacy in the treatment of oral lichen planus, while Takayasu arteritis, SLE, MS, AS, BD and CD did not report sufficient clinical data for meta-analysis. Therefore, large-sample, multi-center clinical trials are still needed for revision or validation.

BackgroundModern pharmacological research found that the chemical components of Curcuma longa L. are mainly curcumin and turmeric volatile oil. Several recent randomized controlled trials (RCT) have shown that curcumin improves symptoms and inflammation in patients with arthritis.MethodsPubmed, Cochran Library, CNKI, and other databases were searched to collect the randomized controlled trials (RCTs). Then, the risk of bias of RCTs were assessed and data of RCTs were extracted. Finally, RevMan 5.3 was utilized for meta-analysis.ResultsTwenty-nine (29) RCTs involving 2396 participants and 5 types of arthritis were included. The arthritis included Ankylosing Spondylitis (AS), Rheumatoid Arthritis (RA), Osteoarthritis (OA), Juvenile idiopathic arthritis (JIA) and gout/hyperuricemia. Curcumin and Curcuma longa Extract were administered in doses ranging from 120 mg to 1500 mg for a duration of 4-36 weeks. In general, Curcumin and Curcuma longa Extract showed safety in all studies and improved the severity of inflammation and pain levels in these arthritis patients. However, more RCTs are needed in the future to elucidate the effect of Curcumin and Curcuma longa Extract supplementation in patients with arthritis, including RA, OA, AS and JIA.ConclusionCurcumin and Curcuma longa Extract may improve symptoms and inflammation levels in people with arthritis. However, due to the low quality and small quantity of RCTs, the conclusions need to be interpreted carefully.

To address the "overdetection" and "overtracking" problems experienced by traditional vehicle counting methods at low frame intervals, this paper proposes a vehicle counting method based on attention mechanism single-shot object detection (SSD) and state detection. First, we designed an attention-based saliency enhancement (ASE) module to fuse different scales of attention-enhanced feature mapping. Second, ASE modules were integrated into the SSD layers (from conv11 to conv15_2) to construct a vehicle detector for multiscale targets, named SSD-ASE. SSD-ASE was then used to detect and filter out vehicles located far from the identification line, where a filtering mechanism based on state detection that divides vehicles into four states based on their relative positions and the position identification line was used. Finally, by using the proposed state-based detector, the states of a vehicle were tracked using a relevant filtering algorithm, and counting was performed according to the state changes. The results of experiments performed on real traffic images/videos that we collected and on those obtained from the GRAM-RTM dataset showed that the proposed method achieved a higher detection accuracy (mAP = 95.94%) than the state-of-the-art methods and a higher tracking accuracy than traditional counting methods, while reducing the tracking number by approximately 60%.

Because pigmented skin lesion image classification based on manually designed convolutional neural networks (CNNs) requires abundant experience in neural network design and considerable parameter tuning, we proposed the macro operation mutation-based neural architecture search (OM-NAS) approach in order to automatically build a CNN for image classification of pigmented skin lesions. We first used an improved search space that was oriented toward cells and contained micro and macro operations. The macro operations include InceptionV1, Fire and other well-designed neural network modules. During the search process, an evolutionary algorithm based on macro operation mutation was employed to iteratively change the operation type and connection mode of parent cells so that the macro operation was inserted into the child cell similar to the injection of virus into host DNA. Ultimately, the searched best cells were stacked to build a CNN for the image classification of pigmented skin lesions, which was then assessed on the HAM10000 and ISIC2017 datasets. The test results showed that the CNN built with this approach was more accurate than or almost as accurate as state-of-the-art (SOTA) approaches such as AmoebaNet, InceptionV3 + Attention and ARL-CNN in terms of image classification. The average sensitivity of this method on the HAM10000 and ISIC2017 datasets was 72.4% and 58.5%, respectively.

Grain storage plays a vital part in social stability as well as people’s daily lives. By virtue of its unique storage advantages, wheat has become one of the most important grain reserves in the world. Wheat kernels continue consuming their own energy to maintain lives during the whole process of storage, which will lead to deterioration of their edible qualities as well as may be infected by certain mold provided that the surrounding conditions are suitable. Thus, establishing an efficient and accurate method to assess wheat kernel’s storage status is necessary for the sake of shaping a shelter to human beings as well as animals. For that reason, various wheat aging and wheat moldy detection methods have been summarized in this chapter. Up to now, study of aging and mold detection in wheat kernels can be divided into conventional chemical techniques and newly proposed physical detection methods. This review has analyzed in detail the limitations of conventional technologies, and then probes into some promising physical technologies for better detecting stored status of wheat kernels.

The quantity security of stored grain in warehouses is crucial for a country. Since manual inspection of stored grain quantity is inefficient, some online automeasurement methods have been proposed recently, but they are either time-consuming or too expensive to be popularized. This article proposes a deep learning-based method to automatically monitor changes in grain quantity of granaries. First, the image of the same scene in a granary is taken at different moments using a device that integrates a camera and an infrared laser rangefinder. Then, a deep semantic segmentation model based on an encoder–decoder framework is developed to extract the grain loading line and grain surface of the image. Finally, the distance and area between the extracted grain loading line and grain surface are calculated and compared with the previous measured one to determine whether the grain quantity of the granary has changed. To improve the accuracy of the segmentation results, a novel reverse attention model is proposed to provide guidance information to fuse low-level features, which calculates multiscale attention maps based only on the output of the last layer of the encoder. Furthermore, the proposed method and infrared laser rangefinder-based method are combined to get more accurate grain volume after an abnormal change of grain quantity is detected by our method. Experimental results show that our method is effective and feasible for monitoring changes in grain quantity and outperforms the state-of-the-art methods on semantic segmentation.

To evaluate the risk stratification of 2- to 5-cm gastric stromal tumors (GSTs) by analyzing their clinical and computed tomography (CT) manifestations with the goal of providing imaging evidence for rational selection of surgical methods.This study involved 223 patients with pathologically diagnosed GSTs of 2 to 5 cm in diameter. According to the pathological results and malignant risk category, the patients were divided into a low-risk biological behavior group (very low and low risk) and high-risk biological behavior group (intermediate and high risk). The clinical and CT manifestations were compared between the groups. The chi-square test was used to analyze categorical variables, and the independent-samples t test was used to analyze continuous variables. Multivariate logistic regression and receiver operating characteristic curve analysis were performed for statistically significant variables.The tumor contour, necrosis, surface ulceration, and long diameter were significantly different between the low-risk group and the high-risk group (P 0.05). Multivariate logistic regression analysis showed that the tumor contour and long diameter were independent risk factors. The area under the curve was 0.82, and the accuracy, sensitivity, and specificity were 0.78, 77.4 %, and 79.7 %, respectively.The risk associated with 2- to 5-cm GSTs can be preoperatively predicted in an indirect manner through analysis of clinical and CT manifestations, and this model has high diagnostic value.

Modern pharmacological research found that the chemical components ofPubmed, Cochran Library, CNKI, and other databases were searched to collect the randomized controlled trials (RCTs). Then, the risk of bias of RCTs were assessed and data of RCTs were extracted. Finally, RevMan 5.3 was utilized for meta-analysis.Twenty-nine (29) RCTs involving 2396 participants and 5 types of arthritis were included. The arthritis included Ankylosing Spondylitis (AS), Rheumatoid Arthritis (RA), Osteoarthritis (OA), Juvenile idiopathic arthritis (JIA) and gout/hyperuricemia. Curcumin and Curcuma longa Extract were administered in doses ranging from 120 mg to 1500 mg for a duration of 4-36 weeks. In general, Curcumin and Curcuma longa Extract showed safety in all studies and improved the severity of inflammation and pain levels in these arthritis patients. However, more RCTs are needed in the future to elucidate the effect of Curcumin and Curcuma longa Extract supplementation in patients with arthritis, including RA, OA, AS and JIA.Curcumin and Curcuma longa Extract may improve symptoms and inflammation levels in people with arthritis. However, due to the low quality and small quantity of RCTs, the conclusions need to be interpreted carefully.

A stable least residue method for solving a nonlinear fractional integro-differential equation with a weakly singular kernel in the reproducing kernel space is proposed. To solve the equation, the multiwavelets bases in the reproducing kernel space are constructed based on the cubic Legendre wavelets in L 2 [ 0 , 1 ] . The best approximate solution could be obtained by solving the normal equation. Meanwhile, we provide the adaptive convergence order and the stability proof of the scheme. It is encouraging that the new method is stable and the accuracy of the method is preserved even in the case if the solution has fast oscillations. Four examples strongly demonstrate the validity of the scheme.

Surface defect classification (SDC) is the visual inspection of the surface of an object to identify appearance defects. Efficient and accurate SDC is i mportant for improving the quality of industrial products. A manually designed convolutional neural network (CNN) is traditionally used for SDC. In this study, a simpler SDC scheme with a higher classification accuracy, named NAS-SDC, is developed based on the neural architecture search (NAS) technique. A max-pooling cell based on NASNet is introduced to reduce the search space and the number of network parameters, thus simplifying the candidate operators for the search. Two network architectures are proposed to stack the search candidates or the best cells. The proposed method can be used to automatically design an efficient CNN model for SDC on a specific dataset. Experimental results show that the proposed method can find the best cells in ~11 h using a single graphics processing unit (GPU) and achieves higher classification accuracies (99.98%, 99.8% and 99.26%) than state-of-the-art methods on the Northeastern University (NEU-CLS), DAGM, and bridge defect datasets. The number of network parameters used in the proposed method is only 0.35 M, and the average test time per sample is approximately 61 ms, thus achieving a balance between performance and speed.

Regularly checking the quantity of stored grain in warehouses is essential for the grain safety of a country. However, current manual inspection ways fail to get real-time measurement results and require spending a lot of manpower and resources. In this paper, we proposed a computer vision-based method to automatically monitor the change in grain quantity of a granary. The proposed method was motivated from the observation that warehouse managers can use a camera to remotely monitor the grain security of a granary, which determines whether grain quantity is reduced by checking the distance between the grain surface and the grain loading line at the outlet of a granary. To this end, images were first captured by a camera, and a two-level spatial constraints-based SVM classifier was learned to detect the grain surface and the grain loading line of the images. During the test phase, the detected result of a test image obtained by SVM was further refined by GrabCut with higher order potentials to get the more accurate segmentation result. Finally, the area between the grain surface and the grain loading line was calculated, and then compared with the previous measured one to determine whether the grain surface had dropped. The experiment results validate the effectiveness of the two-level spatial constraints SVM and the strategy for monitoring the change in grain quantity.

Voltage-dependent resistors (VDRs) are important circuit-protection devices. Their performance is affected by packaging quality. To identify VDR packaging defects more accurately and efficiently, we have proposed a convolutional neural network (CNN)-based VDR appearance quality inspection method that includes four stages: image acquisition, data augmentation, neural architecture design, and CNN training and testing. In designing the neural architecture, we have proposed two VDR-oriented network blocks, which consist of a compressed subnet and a multiscale subnet. Then, a stacking-block-based neural architecture design (BlockNAD) strategy is employed to determine the number of blocks. The last block is connected to a classification layer composed of a global average pooling (GAP) layer and a full connection (FC) layer. Further, using a VDR dataset containing 8058 images, we compared the identification performances of the candidate networks with different structures on 12 categories of VDR defects by adopting a variety of indicators, such as the mean average precision (mAP) and average test time per sample. The experimental results of the proposed method demonstrate competitive results compared to the state-of-the-art methods in identifying VDR defects, with a mAP value of approximately 99.9% and an average test time per sample of approximately 3 ms.

The vibration and noise produced by the powertrain and waves inside ship cabins limit working efficiency and crew and passengers’ accommodation quality. This paper simplifies ship cabins as cavities and explores active control techniques to attenuate sound transmission via multiple parallel-supported flexible subplates. The theoretical formulations of the interaction between multiple subplates and cavities were performed and the coupling relationships between them were analyzed. Based on the multiple subplates and the cavity coupling models, numerical simulations were performed using the derived optimal controller to minimize the transmission of sound into the cavities through two and nine parallel-supported subplates. The various control strategies were explored to minimize the coupling system’s acoustic potential energy, and the control performances were compared and discussed. The mechanism of reducing sound transmission through multiple supported subplates into a cavity is revealed. The simulation results showed that the vibration pattern of the controlled subplate is changed after it is regulated, which increases its radiation to subdue the other subplates’ radiation, while increasing vibration of the controlled subplate. The more subplates a cavity has, the more kinetic energy the controlled subplate possess. Furthermore, the noise reduction performance of a cavity with fewer subplates is better than that with more subplates.

Made available in DSpace on 2020-12-10T17:06:00Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-14 Operational modal analysis is an experimental modal analysis approach, which uses vibration data collected when the structure is under operating conditions. Amongst the methods for operational modal analysis, blind source separation-based methods have been shown to be efficient and powerful. The existing blind source separation modal identification methods, however, require the number of sensors to be at least equal to the number of modes in the frequency range of interest to avoid spatial aliasing. In this article, a frequency domain algorithm that overcomes this problem is proposed, which is based on the joint diagonalization of a set of weighted covariance matrices. In the proposed approach, the frequency range of interest is partitioned into several frequency ranges in which the number of active modes in each band is less than the number of sensors. Numerical simulations and an experimental example demonstrate the efficacy of the method. Harbin Engn Univ, Harbin, Peoples R China Univ Lyon, INSA Lyon, Lyon, France Univ Estadual Paulista, Sao Paulo, Brazil Univ Estadual Paulista, Sao Paulo, Brazil

Deformable image registration (DIR) plays an important role in assisting disease diagnosis. The emergence of the Transformer enables the DIR framework to extract long-range dependencies, which relieves the limitations of intrinsic locality caused by convolution operation. However, suffering from the interference of missing or spurious connections, it is a challenging task for Transformer-based methods to capture the high-quality long-range dependencies.In this paper, by staking the graph convolution Transformer (Graformer) layer at the bottom of the feature extraction network, we propose a Graformer-based DIR framework, named GraformerDIR. The Graformer layer is consist of the Graformer module and the Cheby-shev graph convolution module. Among them, the Graformer module is designed to capture high-quality long-range dependencies. Cheby-shev graph convolution module is employed to further enlarge the receptive field.The performance and generalizability of GraformerDIR have been evaluated on publicly available brain datasets including the OASIS, LPBA40, and MGH10 datasets. Compared with VoxelMorph, the GraformerDIR has obtained performance improvements of 4.6% in Dice similarity coefficient (DSC) and 0.055 mm in the average symmetric surface distance (ASD) while reducing the non-positive rate of Jacobin determinant (Npr.Jac) index about 60 times on publicly available OASIS dataset. On unseen dataset MGH10, the GraformerDIR has obtained the performance improvements of 4.1% in DSC and 0.084 mm in ASD compared with VoxelMorph, which demonstrates the GraformerDIR with better generalizability. The promising performance on the clinical cardiac dataset ACDC indicates the GraformerDIR is practicable.With the advantage of Transformer and graph convolution, the GraformerDIR has obtained comparable performance with the state-of-the-art method VoxelMorph.

Made available in DSpace on 2022-04-28T19:50:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 There is an ever-increasing requirement for higher power vibrating platforms to test large-scale structures. Whilst this may be achieved with a single shaker, this is an expensive option. An alternative solution is to drive a platform with two or more smaller shakers. To do this effectively, however, requires the identical amplitude and phase response of the shakers. In practice, due to manufacturing tolerances and uneven loading, this is not possible without a control system. The design and implementation of such a system is the objective of this paper. An adaptive FxLMS algorithm is used in the synchronous control of a dual-shaker system, considering the dynamic coupling between the shakers. A simulation is presented to verify the effectiveness of the control algorithm before the control system is integrated with practical a dual-shaker system driving a vibrating platform. It is shown that there are significant differences between the controlled and the uncontrolled system, demonstrating the efficacy of the control approach. College of Power and Energy Engineering Harbin Engineering University Department of Mechanical Engineering UNESP Department of Mechanical Engineering UNESP

Objective. To evaluate the effectiveness and safety of mesenchymal stem cells (MSCs) in the treatment of osteoarthritis (OA). Methods. Chinese databases (such as CNKI and SinoMed) and English databases (such as PubMed and Embase) were searched to collect randomized controlled trials (RCTs) of MSCs in the treatment of OA. The retrieval time is from inception to October 10, 2021. The literature was strictly selected according to the inclusion and exclusion criteria, data was extracted, and the quality was evaluated. RevMan 5.3 software was used for meta-analysis. STATA was used to evaluate publication bias. The registration number of this systematic review and meta-analysis is CRD42021277145. Results. A total of 28 RCTs involving 1494 participants were included. The primary outcomes showed that MSCs may reduce WOMAC pain and VAS at the 3rd-month follow-up [WOMAC pain: -3.81 (-6.95, -0.68), P = 0.02 . VAS: -1.11 (-1.53, -0.68), P < 0.00001 ], and the effect lasts for at least 12 months [WOMAC pain: -4.29 (-7.12, -1.47), P = 0.003 . VAS: -1.77 (-2.43, -1.12), P < 0.00001 ]. MSCs may also reduce WOMAC stiffness and physical function at the 6th-month follow-up [WOMAC stiffness: -1.12 (-2.09, -0.14), P = 0.03 . WOMAC physical function: -4.40 (-6.84, -1.96), P = 0.0004 ], and the effect lasts for at least 12 months [WOMAC stiffness: -0.99 (-1.95, -0.03), P = 0.04 . WOMAC physical function: -3.26 (-5.91, -0.61), P = 0.02 ]. The improvement of WOMAC pain, VAS, WOMAC stiffness, and WOMAC physical function may be clinically significant. Meanwhile, after the MSC injection, Lequesne had been reduced compared with the control group [-4.49 (-8.21, -0.77), P = 0.002 ]. For adverse events, there is no significant difference in the safety of MSC injection and the control group [1.20 (0.97, 1.48), P = 0.09 ]. The quality of WOMAC physical function and adverse events were moderate. Conclusion. Based on current evidence, MSCs may be a safety therapy that have a good curative effect in the treatment of OA, the onset time is no later than 3 months, and the time to maintain the curative effect is no less than 12 months. However, these results should be generalized with caution due to the generally low quality of evidence and RCTs.

Multimodal medical image registration has an important role in monitoring tumor growth, radiotherapy, and disease diagnosis. Deep-learning-based methods have made great progress in the past few years. However, its success depends on large training datasets, and the performance of the model decreases due to overfitting and poor generalization when only limited data are available. In this paper, a multimodal medical image registration framework based on few-shot learning is proposed, named reverse-net, which can improve the accuracy and generalization ability of the network by using a few segmentation labels. Firstly, we used the border enhancement network to enhance the ROI (region of interest) boundaries of T1 images to provide high-quality data for the subsequent pixel alignment stage. Secondly, through a coarse registration network, the T1 image and T2 image were roughly aligned. Then, the pixel alignment network generated more smooth deformation fields. Finally, the reverse teaching network used the warped T1 segmentation labels and warped images generated by the deformation field to teach the border enhancement network more structural knowledge. The performance and generalizability of our model have been evaluated on publicly available brain datasets including the MRBrainS13DataNii-Pro, SRI24, CIT168, and OASIS datasets. Compared with VoxelMorph, the reverse-net obtained performance improvements of 4.36% in DSC on the publicly available MRBrainS13DataNii-Pro dataset. On the unseen dataset OASIS, the reverse-net obtained performance improvements of 4.2% in DSC compared with VoxelMorph, which shows that the model can obtain better generalizability. The promising performance on dataset CIT168 indicates that the model is practicable.

Surface defect detection is an important way to improve the production quality of voltage-dependent resistors (VDRs). To improve the accuracy and efficiency of VDR surface quality detection, an end-to-end surface quality detection method based on deep convolutional neural networks (CNNs) was proposed. The method includes four stages: data preparation, convolution neural network design, CNN training, and testing. First, images of VDRs were acquired from three perspectives, i.e., the front, back, and side, and then training, validation and testing sets were obtained. Second, the proposed CNN models for VDR surface defect detection were constructed. Third, during the training stage, the images with class labels from the established training sets were input to the proposed network for training and validation. Finally, in the testing stage, test images from a total of 408 samples of two VDR models were used to test the trained network. The sensitivity, specificity, accuracy, precision and F measure of the proposed algorithm were compared with those of state-of-the-art methods, and the experimental results showed that the proposed method has a high recognition speed and accuracy and meets the requirements of online real-time detection.

Background Inguinal lymphadenectomy (iLAD) is effective for penile carcinoma treatment, but usually results in many complications. This study aims to clinically evaluate the feasibility and clinical significance of a laparoscopic radical iLAD approach partly preserving great saphenous vein branches for penile carcinoma patients. Methods A total of 48 patients with penile cancer who underwent laparoscopic radical iLAD with retention of the great saphenous vein in Henan Cancer Hospital from 2012 Jan to 2020 Dec were included in this study. Sixteen penile carcinoma patients who underwent laparoscopic radical iLAD preserving parts of superficial branches of the great saphenous vein were identified as the sparing group, and the matched 32 patients who incised those branches were identified as control group. This new procedure was performed by laparoscopy, preserving parts of superficial branches of the great saphenous vein, superficial lateral and medial femoral veins. Clinicopathological features and perioperative variables were recorded. Postoperative complications, including skin flap necrosis, lymphorrhagia, and lower extremity edema were analyzed retrospectively. Results We found that the operative time of the sparing group is significantly longer than the control group (p = 0.011). There was no statistical difference in intraoperative blood loss, the lymph node number per side, average time to remove the drainage tube and postoperative hospital stay between the two groups. Compared to the control group, the sparing group showed a significantly decreased incidence of lower extremity edema (p = 0.018). The preservation of parts of superficial branches of the great saphenous vein was mainly decreased the incidence of edema below ankle (p = 0.034). Conclusions This study demonstrated that the iLAD with preserving parts of superficial branches of the great saphenous vein, with a decreased incidence of postoperative complications, is a safe and feasible approach for penile cancer.

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed.Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package “limma,” and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package “clusterProfiler.” Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves.Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations.Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

Background: Bladder cancer (BLCA) is a common malignant tumor of the genitourinary system, and there is a lack of specific, reliable, and non-invasive tumor biomarker tests for diagnosis and prognosis evaluation. Homeobox genes play a vital role in BLCA tumorigenesis and development, but few studies have focused on the prognostic value of homeobox genes in BLCA. In this study, we aim to develop a prognostic signature associated with the homeobox gene family for BLCA.Methods: The RNA sequencing data, clinical data, and probe annotation files of BLCA patients were downloaded from the Gene Expression Omnibus database and the University of California, Santa Cruz (UCSC), Xena Browser. First, differentially expressed homeobox gene screening between tumor and normal samples was performed using the “limma” and robust rank aggregation (RRA) methods. The mutation data were obtained with the “TCGAmutation” package and visualized with the “maftools” package. Kaplan–Meier curves were plotted with the “survminer” package. Then, a signature was constructed by logistic regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using “clusterProfiler.” Furthermore, the infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, the performance of the signature was evaluated by receiver-operating characteristic (ROC) curve and calibration curve analyses.Results: Six genes were selected to construct this prognostic model: TSHZ3, ZFHX4, ZEB2, MEIS1, ISL1, and HOXC4. We divided the BLCA cohort into high- and low-risk groups based on the median risk score calculated with the novel signature. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The infiltration levels of almost all immune cells were significantly higher in the high-risk group than in the low-risk group. The average risk score for the group that responded to immunotherapy was significantly lower than that of the group that did not.Conclusion: We constructed a risk prediction signature with six homeobox genes, which showed good accuracy and consistency in predicting the patient’s prognosis and response to immunotherapy. Therefore, this signature can be a potential biomarker and treatment target for BLCA patients.

The prognosis of patients with advanced urothelial carcinoma is dismal. Platinum-based chemotherapy is still the main first-line treatment for advanced urothelial carcinoma, while immunotherapy can be used as a first-line treatment option for people who cannot tolerate platinum. Immunotherapy is preferred in the second-line treatment of bladder urothelial carcinoma. PD-1 inhibitors (Pembrolizumab, nivolumab and atezolizumab) and PD-L1 inhibitors (Ddurvalumab and avelumab) have not been approved for the treatment of advanced urothelial cancer in China. We describe a patient with advanced urothelial carcinoma experienced disease progression after gemcitabine chemotherapy. Following a treatment of domestic PD-1 inhibitor (sintilimab), the patient achieved a durable complete response with mild toxicity. This case indicates that PD-1 inhibitor sintilimab might be a second-line treatment choice for advanced urothelial carcinoma.

Made available in DSpace on 2020-12-12T02:15:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Vibration isolation of a floating raft system typically found in a marine application is of concern in this paper. Housed on the raft are electrical motors each driving two counter rotating shafts with eccentric masses attached. An experimental study is described in which vibration transmitted to a large-scale, laboratory-based hull-like structure is attenuated by using three measures. They are, the use of vibration isolators between the raft and the hull-like structure, adjustment of the phase of the electrical supply between the motors to minimize vibration transmitted through the isolators, and the application of an active vibration control system using hydraulic actuators placed in parallel with the isolators. The results show that this combination of measures is extremely effective at reducing the vibration transmission to the hull-like structure, and consequent reduction the sound radiation. It is more effective than the application of any of the vibration control measures applied by themselves. Power and Energy Engineering College Harbin Engineering University Departamento de Engenharia Mecânica Universidade Estadual Paulista (UNESP) Ilha Solteira (SP) Departamento de Engenharia Mecânica Universidade Estadual Paulista (UNESP) Ilha Solteira (SP)

When exposed to high levels of noise, earmuffs are often used to avoid hearing loss. However, active noise control earmuffs may exhibit nonlinearities under excessive levels of noise, due to their low-power characteristics of the loudspeakers, and thus nonlinear control algorithms are required to improve the control performance. In this paper, an analytical model of a nonlinear active noise control earmuff is investigated. Based on this model, a robust state feedback control law is designed in the framework of linear matrix inequalities with respect to the parametric uncertainties of the loudspeaker and the limitation of control input. Then the backstepping approach is adopted to force the nonlinear part of the loudspeaker to track the derived state feedback signal and estimate the unknown parameters. Both recorded vehicle noise and multi-frequency noise are used to test the effectiveness of the proposed controller and the control performance is compared with that of a widely accepted nonlinear generalized functional link artificial neural network algorithm. Simulation results demonstrate that the proposed controller is capable of attenuating the interior noise and reducing harmonic and intermodulation distortions significantly.

The segmentation of brain tumors in magnetic resonance imaging (MRI) images plays an important role in early diagnosis, treatment planning and outcome evaluation. However, due to gliomas’ significant diversity in structure, the segmentation accuracy is low. In this paper, an automatic segmentation method integrating the small kernels two-path convolutional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extraction ability of SK-TPCNN and the joint optimization capability of model are presented respectively. The SK-TPCNN structure combining the small convolutional kernels and large convolutional kernels can enhance the nonlinear mapping ability and avoid over-fitting, the multiformity of features is also increased. The learned features from SK-TPCNN are then applied to the RF classifier to implement the joint optimization. RF classifier effectively integrates redundancy features and classify each MRI image voxel into normal brain tissues and different parts of tumor. The proposed algorithm is validated and evaluated in the Brain Tumor Segmentation Challenge (Brats) 2015 challenge Training dataset and the better performance is achieved.

Nonrigid registration of medical images is especially critical in clinical treatment. Mutual information is a popular similarity measure for medical image registration; however, only the intensity statistical characteristics of the global consistency of image are considered in MI, and the spatial information is ignored. In this paper, a novel intensity‐based similarity measure combining normalized mutual information with spatial information for nonrigid medical image registration is proposed. The different parameters of Gaussian filtering are defined according to the regional variance, the adaptive Gaussian filtering is introduced into the local structure tensor. Then, the obtained adaptive local structure tensor is used to extract the spatial information and define the weighting function. Finally, normalized mutual information is distributed to each pixel, and the discrete normalized mutual information is multiplied with a weighting term to obtain a new measure. The novel measure fully considers the spatial information of the image neighborhood, gives the location of the strong spatial information a larger weight, and the registration of the strong gradient regions has a priority over the small gradient regions. The simulated brain image with single‐modality and multimodality are used for registration validation experiments. The results show that the new similarity measure improves the registration accuracy and robustness compared with the classical registration algorithm, reduces the risk of falling into local extremes during the registration process.

This work investigates a self-tuning resonator composed of a slender clamped–clamped steel beam and a freely movable slider. The clamped–clamped beam exhibits hardening nonlinearity when it vibrates in large amplitude, providing a broad bandwidth of dynamic response. The moving slider changes the mass distribution of the whole structure, and provides a passive self-tuning approach for capturing the high-energy orbit of the structure. In the case without inclination, adequate inertial force that mainly depends on the vibration amplitude of the beam and the position of the slider can drive the slider to move from the side toward the centre of the beam. This movement amplifies the beam response when the excitation frequency is below 37 Hz in our prototyped device. In the multi-orbit frequency range (28–37 Hz), the self-tuning and magnification of beam response can be achieved when the slider is initially placed in an appropriate position on the beam. Once the beam is disturbed, however, the desired response in the high-energy orbit can be lost easily and cannot be reacquired without external assistance. In an improved design with a small inclination, the introduced small gravitational component enables the slider to move from the higher side toward the lower side when the beam amplitude is small. This property sacrifices the less efficient self-tuning region below 25 Hz, but can enable the beam to acquire and maintain the high-energy orbit response in the multi-orbit frequency range (28–39 Hz), which is resistant to disturbance. The proposed resonator in this paper not only broadens the frequency bandwidth of dynamic response, but also enables capture and maintenance of the high-energy orbit in a completely passive way. Such a passive self-tuning structure presents an advantage in the design of broadband vibration energy-harvesting systems.

Melanoma is the deadliest form of skin cancer, and its incidence is increasing. The first step in automated melanoma analysis of dermoscopy images is to segment the area of the lesion from the surrounding skin. To improve the accuracy and adaptability of segmentation, an algorithm called sampling with level set by integrating color and texture (SLS-CT) is proposed that not only designs a new way to incorporate textural and color features in the definition of the energy functional but also utilizes an index called texture level, proposed in this work, to automatically decide the weight of each feature in the combined energies. First, at the preprocessing stage, hair and black frame removal is applied, and a potential lesion area is then obtained using Otsu thresholding and entropy maximization. Thereafter, the probability distribution of prior color in this potential lesion area is calculated as well. Second, Gabor wavelet-based texture features are extracted and integrated with the prior color into the evolving energies of the level set based on the texture level. To achieve global optimization, a Markov chain Monte Carlo sampling approach guided by the combined energy is adopted in evolving the level set, which ultimately defines a border in the image to segment a lesion from normal skin. Finally, morphological operations are used for postprocessing. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms, demonstrating that the proposed algorithm outperforms the other tested ones in terms of accuracy and adaptability to different databases.

Clustering refers to the problem of partitioning data into several groups according to the predefined criterion. Graph-based method is one of main clustering approaches and has been shown impressive performance in many literatures. The core issue of graph-based clustering is how to construct a good adjacency graph. A large number of works employ the sparse representation of data as the similarity measure by ℓ1 regularization. However, due to the flat nature of the ℓ1 norm, such methods solve the sparse representation of each data point individually, which do not take into account the global structure of data. To exploit the global and essential structure in data, in contrast to existing methods, we propose to learn a graph with group sparsity. To incorporate more information into the graph, we also use the manifold regularization with adaptive similarity during the process of group sparse self-representation. The resulting model is handled by Alternating Direction Method of Multipliers (ADMM). Further, we employ Iterative Re-weighted Least Squares (IRLS) algorithm and threshold operator to solve the ADMM subproblems. Experimental results on real-world datasets demonstrate the superiority of our method compared to the competing clustering methods.

Magnetic resonance imaging (MRI) is one of the most important methods for clinical diagnosis. However, the main drawback of MRI is the long imaging time, which will cause the moving artifact by patient movements. With the rapid development of the computing power of computer, deep learning is widely used in computer vision, natural language processing, visual recognition and so on. Meanwhile, a large number of reconstruction methods based on deep learning have also emerged. Recently, many generative models have been proposed to solve the perception quality problem that existed in fast MRI images. In this paper, we manage to survey the motivations and reconstruction strategies of generative-based methods published in journals and conferences over the past five years. First, the background and theoretical basis of MRI reconstruction are introduced. Secondly, the application of generative-based methods in MRI reconstruction field is comprehensively summarized and analyzed, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and VAE-GAN. Then the advantages and disadvantages of the existing generative-based MRI reconstruction methods are discussed. Finally, several publicly available MR image datasets and evaluation metrics are presented, which can provide a reference for researchers and practitioners working in related domains. The conclusions and challenges are also given.

In this paper, the vibration energy transfer mechanism of the single-sided vibro-impact (SSVI) nonlinear energy sink (NES) is studied. The concept of impact mode is proposed where the device velocities are decomposed into an energy free impact mode (EFIM) and an energy dissipation impact mode (EDIM). The impact modes are applied to the analysis of a single-degree-of-freedom (SDOF) vibration system with a SSVI NES. This analysis shows that the energy in the system can be redistributed between the different impact modes and that energy transferred from the EFIM to the EDIM is beneficial, because portions of the energy in the EDIM will be dissipated by impact, while the energy in the EFIM will not. More importantly, the mechanism of the SSVI NES to realize the local dissipation of energy is revealed. Furthermore, in order to better evaluate the energy dissipation performance of the SSVI NES, a new evaluation criterion called the vibro-impact vibration reduction (VVR) factor is proposed. Then, the relationship between the VVR factor and the energy free impact mode coefficient is investigated using the Hilbert transform. Finally, the effect of the SSVI NES parameters on the energy dissipation performance of the SSVI NES with various initial conditions is discussed, and a satisfactory region for the SSVI NES design, which is identified via numerical simulation, is proposed.

Background Renal cell carcinoma (RCC) is a common malignant tumour of the genitourinary system. We aimed to analyse the potential value of metastasis-related biomarkers, circulating tumour cells (CTCs) and the proliferative marker Ki-67 in the diagnosis of RCC. Methods Data from 24 laparoscopic radical nephrectomies (RNs) and 17 laparoscopic partial nephrectomies (PNs) were collected in 2018. The numbers and positive rates of CTCs and circulating tumour microemboli (CTM) in the peripheral blood were obtained at three different time points: just before surgery, immediately after surgery and 1 week after surgery. Ki-67 protein expression was evaluated in the RCC tissue by immunohistochemistry. Results Except for the statistically significant association between the preoperative CTC counts and tumour size, no association between the number and positive rate of perioperative CTCs and clinicopathological features was found. The CTC counts gradually decreased during the perioperative period, and at 1 week after surgery, they were significantly lower than those before surgery. High Ki-67 expression was significantly positively correlated with preoperative CTC counts. In addition, Ki-67 expression was higher in the high CTC group (≥ 5 CTCs). Conclusion Our results suggest that surgical nephrectomy is associated with a decrease in CTC counts in RCC patients. CTCs can act as a potential biomarker for the diagnosis and prognosis of RCC. A careful and sufficient long-term follow-up is needed for patients with high preoperative CTC counts.

Background. Osteoporosis is an important health problem worldwide. Liuwei Dihuang Decoction (LDD) and its main ingredients may have a good clinical effect on osteoporosis. Meanwhile, its mechanism for treating osteoporosis needs to be further revealed in order to provide a basis for future drug development. Methods. A systematic biological methodology was utilized to construct and analyze the LDD-osteoporosis network. After that, the human transcription data of LDD intervention in patients with osteoporosis and protein arrays data of LDD intervention in osteoporosis rats were collected. The human transcription data analysis, protein arrays data analysis, and molecular docking were performed to validate the findings of the prediction network (LDD-osteoporosis PPI network). Finally, animal experiments were conducted to verify the prediction results of systematic pharmacology. Results. (1) LDD-osteoporosis PPI network shows the potential compounds, potential targets (such as ALB, IGF1, SRC, and ESR1), clusters, biological processes (such as positive regulation of calmodulin 1-monooxygenase activity, estrogen metabolism, and endothelial cell proliferation), and signaling and Reactome pathways (such as JAK-STAT signaling pathway, osteoclast differentiation, and degradation of the extracellular matrix) of LDD intervention in osteoporosis. (2) Human transcriptomics data and protein arrays data validated the findings of the LDD-osteoporosis PPI network. (3) The animal experiments showed that LDD can improve bone mineral density (BMD), increase serum estradiol (E2) and alkaline phosphatase (ALP) levels, and upregulate Wnt3a and β-catenin mRNA expression ( P < 0.05 ). (4) Molecular docking results showed that alisol A, dioscin, loganin, oleanolic acid, pachymic acid, and ursolic acid may stably bind to JAK2, ESR1, and CTNNB1. Conclusion. LDD may have a therapeutic effect on osteoporosis through regulating the targets (such as ALB, IGF1, SRC, and ESR1), biological processes (such as positive regulation of calmodulin 1-monooxygenase activity, estrogen metabolism, and endothelial cell proliferation), and pathways (such as JAK-STAT signaling pathway, osteoclast differentiation, and degradation of the extracellular matrix) found in this research.

OBJECTIVE: In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS: First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS: In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS: This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.