In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
Different dosage forms can significantly impact pharmacokinetics in vivo,leading to varied effects and potential adverse reactions.This study aimed to evaluate the efficacy,safety,and cost-effectiveness of isosorbide ...Different dosage forms can significantly impact pharmacokinetics in vivo,leading to varied effects and potential adverse reactions.This study aimed to evaluate the efficacy,safety,and cost-effectiveness of isosorbide mononitrate sustained-release capsules(IMSRC)combined with conventional treatments,compared to isosorbide mononitrate tablets(IMT)combined with conventional treatments,for managing angina pectoris in patients with coronary heart diseases.A network meta-analysis(NMA)was conducted to assess the efficacy and safety of IMSRC and IMT.Relevant literature was sourced from databases,including PubMed,Embase,Cochrane Library,ScienceDirect,Web of Science,CNKI,Wanfang,and VIP,covering publications up to July 2023.The cost-effectiveness analysis(CEA)was performed from the perspective of China’s healthcare system,utilizing inputs derived from the NMA.The analysis included 15 studies.The NMA results revealed no significant difference in efficacy and safety between IMSRC plus conventional treatments and IMT plus conventional treatments.However,both combinations were more effective than conventional treatments without isosorbide mononitrate.No differences in safety were observed among the three groups.The surface under the cumulative ranking(SUCRA)of the NMA indicated that IMT had a slight edge over IMSRC in the total effective rate of angina pectoris,whereas IMSRC showed higher probabilities for markedly effective rate and ECG effective rate compared to IMT.The incidence of adverse events was ranked as IMT>conventional preparation>IMSRC.The CEA results highlighted that the incremental cost-effectiveness ratios(ICERs)for the markedly effective and total effective rates of angina pectoris were-133.41 and-260.20,respectively.The ICERs for ECG effective rates were-83.34 and-234.24,respectively.In conclusion,while IMSRC combined with conventional treatments and IMT combined with conventional treatments were similar in efficacy and safety,IMSRC proved to be more economical.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a s...In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a star point positioning algorithm based on the capsule network whose input and output are both vectors. First, a PCTL (Probability-Coordinate Transformation Layer) is designed to represent the mapping relationship between the probability output of the capsule network and the star point sub-pixel coordinates. Then, Coordconv Layer is introduced to implement explicit encoding of space information and the probability is used as the centroid weight to achieve the conversion between probability and star point sub-pixel coordinates, which improves the network’s ability to perceive star point positions. Finally, based on the dynamic imaging principle of star sensors and the characteristics of near-space environment, a star map dataset for algorithm training and testing is constructed. The simulation results show that the proposed algorithm reduces the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the star point positioning by 36.1% and 41.7% respectively compared with the traditional algorithm. The research results can provide important theory and technical support for the scheme design, index demonstration, test and evaluation of large dynamic star sensors in near space.展开更多
OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and ...OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases.Then the herbs-compounds-targets network,and protein-protein interaction(PPI) network was constructed.Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed by R software.Next,we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC.RESULTS:A total of 57 common targets of QGC and sarcopenia were obtained.Based on the enrichment analysis of GO and KEGG,we took the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia.Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy.In the model group,the expression of muscle ring finger-1 and Atrogin-1 were increased,while myosin heavy chain was decreased,QGC treatment reversed these changes.Moreover,compared with the model group,the expressions of pPI3K,p-Akt,p-mammalian target of rapamycin and pForkhead box O3 in the QGC group were all upregulated.CONCLUSION:QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.展开更多
The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image...The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.展开更多
Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at hig...Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.展开更多
Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is ...Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.展开更多
Objective:To explore the mechanism of action of Zangjiangzhi capsule(ZJZC)in treating hyperlipidemia(HLP).Methods:The components of ZJZC were analyzed and identified using ultra-high performance liquid chromatography ...Objective:To explore the mechanism of action of Zangjiangzhi capsule(ZJZC)in treating hyperlipidemia(HLP).Methods:The components of ZJZC were analyzed and identified using ultra-high performance liquid chromatography with Q-Exactive Orbitrap tandem mass spectrometry(UHPLC-Q-Exactive-Orbitrap-MS/MS).Network pharmacology analysis was used to explore the mechanism of action of ZJZC in HLP treatment.The SwissTargetPrediction database was used to predict compound targets,and GeneCards,DisGeNet,OMIM,and DRUGBANK databases were used to identify HLP-related targets.Proteineprotein interaction diagrams were constructed using the STRING database.The targets were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.The“herbingredient-target”network was visualized using Cytoscape.Preliminary validation was performed using molecular docking and enzyme-linked immunosorbent assay.Results:Ninety compounds were identified in ZJZC,including 34 flavonoids,12 phenols,10 terpenoids,10 alkaloids,8 organic acids,8 anthraquinones,and 9 other compounds.In total,904 targets were identified for these compounds.Among them,158 targets intersected with the HLP target network.Network pharmacology analysis showed that MAPK1,PPAR-a,RXRA,HSP90AA1,PIK3R1,AKT1,PIK3CA,IL6,TNF,and ESR1 are the key targets of action.KEGG enrichment analysis identified 164 pathways.Among these,the AGE-RAGE signaling pathway in diabetic complications,lipid and atherosclerosis pathways,regulation of lipids in adipocytes,and insulin resistance are related to HLP.Molecular docking showed good affinity between the key targets and ingredients.Further,ZJZC treatment in mice resulted in lower expression of MAPK1 protein and increased expression of PPAR-a protein,which have been shown to be strongly associated with HLP.Conclusions:This study showed that ZJZC contains various active ingredients and can modulate multiple targets and pathways associated with HLP,providing evidence at the molecular level for its clinical application in the treatment of HLP.展开更多
BACKGROUND Video capsule endoscopy(VCE)is a noninvasive technique used to examine small bowel abnormalities in both adults and children.However,manual review of VCE images is time-consuming and labor-intensive,making ...BACKGROUND Video capsule endoscopy(VCE)is a noninvasive technique used to examine small bowel abnormalities in both adults and children.However,manual review of VCE images is time-consuming and labor-intensive,making it crucial to develop deep learning methods to assist in image analysis.AIM To employ deep learning models for the automatic classification of small bowel lesions using pediatric VCE images.METHODS We retrospectively analyzed VCE images from 162 pediatric patients who underwent VCE between January 2021 and December 2023 at the Children's Hospital of Nanjing Medical University.A total of 2298 high-resolution images were extracted,including normal mucosa and lesions(erosions/erythema,ulcers,and polyps).The images were split into training and test datasets in a 4:1 ratio.Four deep learning models:DenseNet121,Visual geometry group-16,ResNet50,and vision transformer were trained using 5-fold cross-validation,with hyperparameters adjusted for optimal classification performance.The models were evaluated based on accuracy,precision,recall,F1-score,and area under the receiver operating curve(AU-ROC).Lesion visualization was performed using gradient-weighted class activation mapping.RESULTS Abdominal pain was the most common indication for VCE,accounting for 62%of cases,followed by diarrhea,vomiting,and gastrointestinal bleeding.Abnormal lesions were detected in 93 children,with 38 diagnosed with inflammatory bowel disease.Among the deep learning models,DenseNet121 and ResNet50 demonstrated excellent classification performance,achieving accuracies of 90.6%[95%confidence interval(CI):89.2-92.0]and 90.5%(95%CI:89.9-91.2),respectively.The AU-ROC values for these models were 93.7%(95%CI:92.9-94.5)for DenseNet121 and 93.4%(95%CI:93.1-93.8)for ResNet50.CONCLUSION Our deep learning-based diagnostic tool developed in this study effectively classified lesions in pediatric VCE images,contributing to more accurate diagnoses and increased diagnostic efficiency.展开更多
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso...Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.展开更多
Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, Caps...Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.展开更多
In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract i...In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.展开更多
Background:The purpose of the study was to investigate the active ingredients and potential biochemical mechanisms of Juanbi capsule in knee osteoarthritis based on network pharmacology,molecular docking and animal ex...Background:The purpose of the study was to investigate the active ingredients and potential biochemical mechanisms of Juanbi capsule in knee osteoarthritis based on network pharmacology,molecular docking and animal experiments.Methods:Chemical components for each drug in the Juanbi capsule were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,while the target proteins for knee osteoarthritis were retrieved from the Drugbank,GeneCards,and OMIM databases.The study compared information on knee osteoarthritis and the targets of drugs to identify common elements.The data was imported into the STRING platform to generate a protein-protein interaction network diagram.Subsequently,a“component-target”network diagram was created using the screened drug components and target information with Cytoscape software.Common targets were imported into Metascape for GO function and KEGG pathway enrichment analysis.AutoDockTools was utilized to predict the molecular docking of the primary chemical components and core targets.Ultimately,the key targets were validated through animal experiments.Results:Juanbi capsule ameliorated Knee osteoarthritis mainly by affecting tumor necrosis factor,interleukin1β,MMP9,PTGS2,VEGFA,TP53,and other cytokines through quercetin,kaempferol,andβ-sitosterol.The drug also influenced the AGE-RAGE,interleukin-17,tumor necrosis factor,Relaxin,and NF-κB signaling pathways.The network pharmacology analysis results were further validated in animal experiments.The results indicated that Juanbi capsule could decrease the levels of tumor necrosis factor-αand interleukin-1βin the serum and synovial fluid of knee osteoarthritis rats and also down-regulate the expression levels of MMP9 and PTGS2 proteins in the articular cartilage.Conclusion:Juanbi capsule may improve the knee bone microstructure and reduce the expression of inflammatory factors of knee osteoarthritis via multiple targets and multiple signaling pathways.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan...There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment.For this reason,the development of convolutional neural networks(CNNs)for the task of lung cancer classification has recently seen a trend in attention.CNNs have great potential,but they need a lot of training data and struggle with input alterations.To address these limitations of CNNs,a novel machine-learning architecture of capsule networks has been presented,and it has the potential to completely transform the areas of deep learning.Capsule networks,which are the focus of this work,are interesting because they can withstand rotation and affine translation with relatively little training data.This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification.The findings demonstrate that the proposed capsule network method outperforms CNNs on the lung cancer classification challenge.CapsNet with a single convolution layer and 32 features(CN-1-32),CapsNet with a single convolution layer and 64 features(CN-1-64),and CapsNet with a double convolution layer and 64 features(CN-2-64)are the three capsulel networks developed in this research for lung cancer classification.Lung nodules,both benign and malignant,are classified using these networks using CT images.The LIDC-IDRI database was utilized to assess the performance of those networks.Based on the testing results,CN-2-64 network performed better out of the three networks tested,with a specificity of 98.37%,sensitivity of 97.47%and an accuracy of 97.92%.展开更多
Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propos...Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.展开更多
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金The 2022 Ministry of Education General Project for Humanities and Social Sciences Research(Grant No.22YJAZH147)the General Subject of Guangzhou Philosophy and Social Science Development“14th Five-Year Plan”in 2023(Grant No.2023GZYB68)+2 种基金China University Industry-Academia-Research Innovation Fund-Huatong Guokang Medical Research Special Project(Grant No.2023HT017)2024 Guangdong Province General Project for the Planning of Philosophy and Social Sciences(Grant No.GD24CGL29)the Innovation Team Project of Colleges and Universities in Guangdong Province(Grant No.2022WCXTD011).
文摘Different dosage forms can significantly impact pharmacokinetics in vivo,leading to varied effects and potential adverse reactions.This study aimed to evaluate the efficacy,safety,and cost-effectiveness of isosorbide mononitrate sustained-release capsules(IMSRC)combined with conventional treatments,compared to isosorbide mononitrate tablets(IMT)combined with conventional treatments,for managing angina pectoris in patients with coronary heart diseases.A network meta-analysis(NMA)was conducted to assess the efficacy and safety of IMSRC and IMT.Relevant literature was sourced from databases,including PubMed,Embase,Cochrane Library,ScienceDirect,Web of Science,CNKI,Wanfang,and VIP,covering publications up to July 2023.The cost-effectiveness analysis(CEA)was performed from the perspective of China’s healthcare system,utilizing inputs derived from the NMA.The analysis included 15 studies.The NMA results revealed no significant difference in efficacy and safety between IMSRC plus conventional treatments and IMT plus conventional treatments.However,both combinations were more effective than conventional treatments without isosorbide mononitrate.No differences in safety were observed among the three groups.The surface under the cumulative ranking(SUCRA)of the NMA indicated that IMT had a slight edge over IMSRC in the total effective rate of angina pectoris,whereas IMSRC showed higher probabilities for markedly effective rate and ECG effective rate compared to IMT.The incidence of adverse events was ranked as IMT>conventional preparation>IMSRC.The CEA results highlighted that the incremental cost-effectiveness ratios(ICERs)for the markedly effective and total effective rates of angina pectoris were-133.41 and-260.20,respectively.The ICERs for ECG effective rates were-83.34 and-234.24,respectively.In conclusion,while IMSRC combined with conventional treatments and IMT combined with conventional treatments were similar in efficacy and safety,IMSRC proved to be more economical.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
文摘In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a star point positioning algorithm based on the capsule network whose input and output are both vectors. First, a PCTL (Probability-Coordinate Transformation Layer) is designed to represent the mapping relationship between the probability output of the capsule network and the star point sub-pixel coordinates. Then, Coordconv Layer is introduced to implement explicit encoding of space information and the probability is used as the centroid weight to achieve the conversion between probability and star point sub-pixel coordinates, which improves the network’s ability to perceive star point positions. Finally, based on the dynamic imaging principle of star sensors and the characteristics of near-space environment, a star map dataset for algorithm training and testing is constructed. The simulation results show that the proposed algorithm reduces the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the star point positioning by 36.1% and 41.7% respectively compared with the traditional algorithm. The research results can provide important theory and technical support for the scheme design, index demonstration, test and evaluation of large dynamic star sensors in near space.
基金Shanghai Clinical Research Center for Chronic Musculoskeletal Diseases (20MC1920600)Shanghai Key Clinical Specialty "Traditional Chinese Medicine Orthopaedic Traumatology"(shslczdzk03901)+3 种基金The Second Round of Construction Project of National TCM Academic School Inheritance Studio "Shi's Trauma Department"[Letter of the People's Education of Traditional Chinese Medicine (2019) No.62]Shanghai High-level Local Universities "Chronic Muscle and Bone Damage Research and Transformation" Innovation Team [No.3 of Shanghai Education Commission (2022)]Program for Shanghai High-Level Local University Innovation Team (SZY20220315)Shanghai Shenkang Hospital Development Center Clinical Three-year Action Plan (SHDC2020CR3090B)。
文摘OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases.Then the herbs-compounds-targets network,and protein-protein interaction(PPI) network was constructed.Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed by R software.Next,we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC.RESULTS:A total of 57 common targets of QGC and sarcopenia were obtained.Based on the enrichment analysis of GO and KEGG,we took the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia.Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy.In the model group,the expression of muscle ring finger-1 and Atrogin-1 were increased,while myosin heavy chain was decreased,QGC treatment reversed these changes.Moreover,compared with the model group,the expressions of pPI3K,p-Akt,p-mammalian target of rapamycin and pForkhead box O3 in the QGC group were all upregulated.CONCLUSION:QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.
文摘The application of image super-resolution(SR)has brought significant assistance in the medical field,aiding doctors to make more precise diagnoses.However,solely relying on a convolutional neural network(CNN)for image SR may lead to issues such as blurry details and excessive smoothness.To address the limitations,we proposed an algorithm based on the generative adversarial network(GAN)framework.In the generator network,three different sizes of convolutions connected by a residual dense structure were used to extract detailed features,and an attention mechanism combined with dual channel and spatial information was applied to concentrate the computing power on crucial areas.In the discriminator network,using InstanceNorm to normalize tensors sped up the training process while retaining feature information.The experimental results demonstrate that our algorithm achieves higher peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM)compared to other methods,resulting in an improved visual quality.
文摘Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.
基金supported by the International Research Center of Big Data for Sustainable Development Goals under Grant No.CBAS2022GSP05the Open Fund of State Key Laboratory of Remote Sensing Science under Grant No.6142A01210404the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant No.KLIGIP-2022-B03.
文摘Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.
基金supported by the National Natural Science Foundation of China(22067016)the Project of Qinghai Outstanding Youth Fund Project(2023-ZJ-964J).
文摘Objective:To explore the mechanism of action of Zangjiangzhi capsule(ZJZC)in treating hyperlipidemia(HLP).Methods:The components of ZJZC were analyzed and identified using ultra-high performance liquid chromatography with Q-Exactive Orbitrap tandem mass spectrometry(UHPLC-Q-Exactive-Orbitrap-MS/MS).Network pharmacology analysis was used to explore the mechanism of action of ZJZC in HLP treatment.The SwissTargetPrediction database was used to predict compound targets,and GeneCards,DisGeNet,OMIM,and DRUGBANK databases were used to identify HLP-related targets.Proteineprotein interaction diagrams were constructed using the STRING database.The targets were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.The“herbingredient-target”network was visualized using Cytoscape.Preliminary validation was performed using molecular docking and enzyme-linked immunosorbent assay.Results:Ninety compounds were identified in ZJZC,including 34 flavonoids,12 phenols,10 terpenoids,10 alkaloids,8 organic acids,8 anthraquinones,and 9 other compounds.In total,904 targets were identified for these compounds.Among them,158 targets intersected with the HLP target network.Network pharmacology analysis showed that MAPK1,PPAR-a,RXRA,HSP90AA1,PIK3R1,AKT1,PIK3CA,IL6,TNF,and ESR1 are the key targets of action.KEGG enrichment analysis identified 164 pathways.Among these,the AGE-RAGE signaling pathway in diabetic complications,lipid and atherosclerosis pathways,regulation of lipids in adipocytes,and insulin resistance are related to HLP.Molecular docking showed good affinity between the key targets and ingredients.Further,ZJZC treatment in mice resulted in lower expression of MAPK1 protein and increased expression of PPAR-a protein,which have been shown to be strongly associated with HLP.Conclusions:This study showed that ZJZC contains various active ingredients and can modulate multiple targets and pathways associated with HLP,providing evidence at the molecular level for its clinical application in the treatment of HLP.
文摘BACKGROUND Video capsule endoscopy(VCE)is a noninvasive technique used to examine small bowel abnormalities in both adults and children.However,manual review of VCE images is time-consuming and labor-intensive,making it crucial to develop deep learning methods to assist in image analysis.AIM To employ deep learning models for the automatic classification of small bowel lesions using pediatric VCE images.METHODS We retrospectively analyzed VCE images from 162 pediatric patients who underwent VCE between January 2021 and December 2023 at the Children's Hospital of Nanjing Medical University.A total of 2298 high-resolution images were extracted,including normal mucosa and lesions(erosions/erythema,ulcers,and polyps).The images were split into training and test datasets in a 4:1 ratio.Four deep learning models:DenseNet121,Visual geometry group-16,ResNet50,and vision transformer were trained using 5-fold cross-validation,with hyperparameters adjusted for optimal classification performance.The models were evaluated based on accuracy,precision,recall,F1-score,and area under the receiver operating curve(AU-ROC).Lesion visualization was performed using gradient-weighted class activation mapping.RESULTS Abdominal pain was the most common indication for VCE,accounting for 62%of cases,followed by diarrhea,vomiting,and gastrointestinal bleeding.Abnormal lesions were detected in 93 children,with 38 diagnosed with inflammatory bowel disease.Among the deep learning models,DenseNet121 and ResNet50 demonstrated excellent classification performance,achieving accuracies of 90.6%[95%confidence interval(CI):89.2-92.0]and 90.5%(95%CI:89.9-91.2),respectively.The AU-ROC values for these models were 93.7%(95%CI:92.9-94.5)for DenseNet121 and 93.4%(95%CI:93.1-93.8)for ResNet50.CONCLUSION Our deep learning-based diagnostic tool developed in this study effectively classified lesions in pediatric VCE images,contributing to more accurate diagnoses and increased diagnostic efficiency.
文摘Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.
基金the following funds:The Key Scientific Research Project of Anhui Provincial Research Preparation Plan in 2023(Nos.2023AH051806,2023AH052097,2023AH052103)Anhui Province Quality Engineering Project(Nos.2022sx099,2022cxtd097)+1 种基金University-Level Teaching and Research Key Projects(Nos.ch21jxyj01,XLZ-202208,XLZ-202106)Special Support Plan for Innovation and Entrepreneurship Leaders in Anhui Province。
文摘Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.
基金the Scientific Research Foundation of Liaoning Provincial Department of Education(No.LJKZ0139)the Program for Liaoning Excellent Talents in University(No.LR15045).
文摘In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.
基金funding from the Basic Research Project of the Education Department of Shaanxi Province(21JC010,21JP035)the Young and Middle-Aged Scientific Research and Innovation Team of the Shaanxi Provincial Administration of Traditional Chinese Medicine(2022SLRHLJ001)the 2023 Central Financial Transfer Payment Local Project“Innovation and Improvement of Five Types of Hospital Preparations,Such as Roumudan Granules”.
文摘Background:The purpose of the study was to investigate the active ingredients and potential biochemical mechanisms of Juanbi capsule in knee osteoarthritis based on network pharmacology,molecular docking and animal experiments.Methods:Chemical components for each drug in the Juanbi capsule were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,while the target proteins for knee osteoarthritis were retrieved from the Drugbank,GeneCards,and OMIM databases.The study compared information on knee osteoarthritis and the targets of drugs to identify common elements.The data was imported into the STRING platform to generate a protein-protein interaction network diagram.Subsequently,a“component-target”network diagram was created using the screened drug components and target information with Cytoscape software.Common targets were imported into Metascape for GO function and KEGG pathway enrichment analysis.AutoDockTools was utilized to predict the molecular docking of the primary chemical components and core targets.Ultimately,the key targets were validated through animal experiments.Results:Juanbi capsule ameliorated Knee osteoarthritis mainly by affecting tumor necrosis factor,interleukin1β,MMP9,PTGS2,VEGFA,TP53,and other cytokines through quercetin,kaempferol,andβ-sitosterol.The drug also influenced the AGE-RAGE,interleukin-17,tumor necrosis factor,Relaxin,and NF-κB signaling pathways.The network pharmacology analysis results were further validated in animal experiments.The results indicated that Juanbi capsule could decrease the levels of tumor necrosis factor-αand interleukin-1βin the serum and synovial fluid of knee osteoarthritis rats and also down-regulate the expression levels of MMP9 and PTGS2 proteins in the articular cartilage.Conclusion:Juanbi capsule may improve the knee bone microstructure and reduce the expression of inflammatory factors of knee osteoarthritis via multiple targets and multiple signaling pathways.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
文摘There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment.For this reason,the development of convolutional neural networks(CNNs)for the task of lung cancer classification has recently seen a trend in attention.CNNs have great potential,but they need a lot of training data and struggle with input alterations.To address these limitations of CNNs,a novel machine-learning architecture of capsule networks has been presented,and it has the potential to completely transform the areas of deep learning.Capsule networks,which are the focus of this work,are interesting because they can withstand rotation and affine translation with relatively little training data.This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification.The findings demonstrate that the proposed capsule network method outperforms CNNs on the lung cancer classification challenge.CapsNet with a single convolution layer and 32 features(CN-1-32),CapsNet with a single convolution layer and 64 features(CN-1-64),and CapsNet with a double convolution layer and 64 features(CN-2-64)are the three capsulel networks developed in this research for lung cancer classification.Lung nodules,both benign and malignant,are classified using these networks using CT images.The LIDC-IDRI database was utilized to assess the performance of those networks.Based on the testing results,CN-2-64 network performed better out of the three networks tested,with a specificity of 98.37%,sensitivity of 97.47%and an accuracy of 97.92%.
基金supported by the National Natural Science Foundation of China[grant number 41671452].
文摘Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.
