Biomedical Signal Processing and Control最新文献

{"title":"An explainable AI for breast cancer classification using vision Transformer (ViT)","authors":"Marwa Naas ,&nbsp;Hiba Mzoughi ,&nbsp;Ines Njeh ,&nbsp;Mohamed BenSlima","doi":"10.1016/j.bspc.2025.108011","DOIUrl":"10.1016/j.bspc.2025.108011","url":null,"abstract":"<div><div>Manual classification of breast cancer (BC) through an optical microscope is regarded as an essential task throughout clinical routines, necessitating highly skilled pathologists. Computer-aided diagnosis (CAD) techniques based on deep learning (DL) are developed to assist the pathologists in making diagnostic decisions. Nevertheless, the black-box nature and the absence of interpretability and transparency of these DL-based models render their application highly difficult in sensitive and critical medical applications. In addition to providing explanations for the model predictions, explainable artificial intelligence (XAI) strategies help to gain the trust of clinicians. The current Convolutional Neural Network (CNN) architectures have limitations in capturing the global feature information details present in BC histopathological images. To overcome the challenge of long-range dependenciesin CNN-based models, Vision Transformer (ViT) architectures have recently been created.</div><div>These architectures have a self-attention mechanism that enables the analysis of images. As a result, the network is able to record the deep long-range dependence between pixels. The present work aims to develop an effective CAD tool for BC classification. In this study, we investigated a deep ViT architecture trained to perform binary lesions classification (malignant versus benign) using histopathology images. Various XAI techniques have been implemented: Gradient-Weighted Class Activation Mapping (Grad-CAM), Vanilla gradient, Integrated gradients, Saliency Maps, Local Interpretable Model Agnostic Explanation (LIME), and Attention Maps to highlight the most important features of the model prediction outcomes. The evaluation task was performed using the publicly accessible benchmark dataset BreakHis. Based on the research outcomes, our suggested ViT architecture demonstrates competitive performance, surpassing state-of-the-art CNN models in the analysis of histopathological images. Furthermore, the proposed models provide precise and accurate interpretations, reinforcing their reliability. Therefore, we can affirm that the proposed CAD system can be effectively integrated into clinical diagnostic routines, offering enhanced support for medical professionals.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 108011"},"PeriodicalIF":4.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pseudo Label-Guided Data Fusion and output consistency for semi-supervised medical image segmentation","authors":"Tao Wang ,&nbsp;Xinlin Zhang ,&nbsp;Yuanbin Chen ,&nbsp;Yuanbo Zhou ,&nbsp;Longxuan Zhao ,&nbsp;Bizhe Bai ,&nbsp;Tao Tan ,&nbsp;Tong Tong","doi":"10.1016/j.bspc.2025.107956","DOIUrl":"10.1016/j.bspc.2025.107956","url":null,"abstract":"<div><div>Supervised learning algorithms have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data which is a laborious and time-consuming process. Consequently, semi-supervised learning methods are increasingly becoming popular. We propose the Pseudo Label-Guided Data Fusion framework, which builds upon the mean teacher network for segmenting medical images with limited annotation. We introduce a pseudo-labeling utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on the Pancreas-CT, LA, BraTS2019 and BraTS2023 datasets demonstrate superior performance, with Dice scores of 80.90%, 89.80%, 85.47% and 89.39% respectively, when 10% of the dataset is labeled. Compared to MC-Net, our method achieves improvements of 10.9%, 0.84%, 5.84% and 0.63% on these datasets, respectively. The codes for this study are available at <span><span>https://github.com/ortonwang/PLGDF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107956"},"PeriodicalIF":4.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RLANET: An EEG denoising network for judgemental removal of long- and short-term distribution artefacts","authors":"Fengjie Wu ,&nbsp;Jisen Yang ,&nbsp;Jiarui Liu ,&nbsp;Zhaolong Lin ,&nbsp;Yan He ,&nbsp;Lihan Zhang","doi":"10.1016/j.bspc.2025.107962","DOIUrl":"10.1016/j.bspc.2025.107962","url":null,"abstract":"<div><div>The acquisition of electroencephalogram (EEG) signals is susceptible to contamination by various physiological artefacts, making the subsequent analysis of the EEG signals difficult. Deep learning (DL)-based methods of EEG denoising have achieved some effectiveness in addressing this issue. However, existing structural designs do not fully account for the randomness and waveform diversity of artefacts’ temporal distribution. Most network structures are processed directly on the samples disturbed by the mixture of artefacts, which overlooks the time-varying overlap between electromyography (EMG) and EEG in long-term distribution, as well as the mutual interference between long-and short-term artefacts. To overcome these issues, we propose an EEG denoising network for the judgemental removal of long- and short-term distribution artefacts. This network, which we call RLANET, consists of a segmentation network, a short-term denoising network, and a long-term denoising network. The segmentation network ResUNet is used to enable discrimination of the temporal distribution characteristics of the artefacts. The short-term denoising network LWTCN learns temporal correlations and capture local waveform variations to remove short-term distribution artefacts from EEG signals. The long-term denoising network ADDPM is used to reconstruct EEG signals affected by long-term distribution artefacts, improving the quality of noise removal. The experimental results demonstrate that RLANET’s denoising performance is significantly superior to that of current mainstream denoising methods. Specifically, in the removal of mixed artefacts, RLANET achieved improvements of 1.31% and 1.5316 in Correlation Coefficient (CC) and Signal-to-Noise Ratio (SNR), respectively, demonstrating its outstanding performance in handling mixed artefacts.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"109 ","pages":"Article 107962"},"PeriodicalIF":4.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving remote heart rate estimation using Block-wise Frequency Domain Transformation","authors":"Haodong Huang,&nbsp;Weihua Ou,&nbsp;Jiahao Xiong","doi":"10.1016/j.bspc.2025.107893","DOIUrl":"10.1016/j.bspc.2025.107893","url":null,"abstract":"<div><div>Remote heart rate estimation has attracted much attention in recent years in fields such as medical monitoring, mental health assessment, and exercise monitoring due to its non-contact characteristic. Most remote heart rate estimation methods usually adopt remote photoplethysmography (rPPG) to obtain the blood volume pulse (BVP) signal from the facial video. However, facial information is highly sensitive and poses a significant risk of privacy leakage. Currently, most face privacy preserving methods suitable for remote heart rate estimation are based on facial perturbations, if these methods are used to protect facial information, the accuracy of heart rate estimation might decrease. To address the above problems, we proposed a Privacy-Preserving Remote Heart Rate Estimation (PP-RHRE) architecture, with Block-wise Frequency Domain Transformation (BFDT) privacy-preserving method. This BFDT method can balance face privacy preserving effect and original remote heart rate estimation model accuracy. Specifically, we crop each raw image of the facial videos according to the face area for removing environmental visual information without rPPG signals. Subsequently, we divide the images into blocks, apply discrete cosine transform (DCT) on the each block, and then we discard some high-frequency parts to blur the images and enhance the representation of the BVP signal. Extensive experiments demonstrate that our method can improve original model accuracy while protecting face privacy in most BFDT scheme.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107893"},"PeriodicalIF":4.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AMFTCNet: A multi-level attention-based multi-scale fusion temporal convolutional network for decoding MI-EEG signals","authors":"Qianfeng Huang,&nbsp;Yuanpo Yang,&nbsp;Jun Li,&nbsp;Xiuling Liu,&nbsp;Xiaoguang Liu","doi":"10.1016/j.bspc.2025.107916","DOIUrl":"10.1016/j.bspc.2025.107916","url":null,"abstract":"<div><div>Currently, Brain-Computer Interface (BCI) technology based on Electroencephalography (EEG) and Motor Imagery (MI) is widely applied in fields such as neural rehabilitation, assistive communication, and virtual reality. However, MI-EEG signals are susceptible to various factors that affect decoding accuracy and generalization capability. Effectively utilizing training data and integrating signal features remain major challenges in MI-EEG decoding algorithms. To address these issues, this paper proposes a novel AMFTCNet model. The model improves decoding results through multi-scale feature learning and dynamic integration of features from different scales. The AMFTCNet model first extracts multi-scale feature representations from the raw EEG signals using Convolutional Blocks (CV) and Multi-Scale Branch Structures (MSB). It then extracts high-dimensional features from single scales through Parallel Attention Temporal Convolution Blocks (PAT). Additionally, this paper introduces a new attention block, the PSCA block, which dynamically weights and combines high-dimensional features from different scales to integrate signal features and enhance decoding performance. Experimental results demonstrate the superior performance of the AMFTCNet model across multiple datasets. The model achieves accuracies of 87.77%, 88.26%, and 95.62% on the BCI Competition IV-2a, BCI Competition IV-2b, and High Gamma datasets, respectively. These results provide valuable insights for exploring how to effectively fuse multiple types of feature information and how to utilize attention mechanisms more efficiently to improve decoding accuracy.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107916"},"PeriodicalIF":4.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variational Onsager Neural Network optimized with Golden search optimization algorithm fostered for lung disease detection system in IoT","authors":"G. Sudha ,&nbsp;V. Angayarkanni ,&nbsp;K.R. Kanagavalli ,&nbsp;Tareek Pattewar","doi":"10.1016/j.bspc.2025.107951","DOIUrl":"10.1016/j.bspc.2025.107951","url":null,"abstract":"<div><div>Pneumonia causes a high rate of newborn morbidity and mortality. The challenge is accurately identifies respiratory disorders while overcoming the limitations of existing technologies such as low accuracy, delayed response, and restricted scalability. To overcome this complication, Variational Onsager Neural Network optimized with Golden search optimization algorithm fostered for Lung Disease Detection system in IoT (LDD-VONN-CXR-IoT) is proposed. Initially, input CXR images are gathered from chest-X-ray Dataset. Then, pre-process the input CXR images using Two-way Recursive filtering (TWRF) for normalizing image and increasing the quality of the images. Afterwards, the preprocessed image is supplied to the feature extraction. Adaptive Synchro Extracting Transform (ASET) is employed to extract the statistical features. Finally, the extracted features are fed into Variational Onsager Neural Networks (VONN) which classifies the input CXR image into normal and pneumonia. The Golden Search Optimization Algorithm (GSOA) is used to optimize VONN that accurately detects the Lung Disease. The proposed LDD-VONN-CXR-IoT method is implemented. The performance metrics, like precision, accuracy, F1-score, Sensitivity, specificity, Error rate, ROC, computational time are examined. The proposed LDD-VONN-CXR-IoT approach attains 99.57%, 98.46%, and 98.13% for accuracy, F1 score, and precision respectively. These outcomes prove that this method for the Lung Disease Detection system in IoT is effectual tool to assist in clinical diagnosis. This method allows expertise to acquire exact results, thus providing the proper treatment.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107951"},"PeriodicalIF":4.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TASTE:Triple-attention with weighted skeletonized Tversky loss for enhancing airway segmentation accuracy","authors":"Ziteng Zhou ,&nbsp;Guang Li ,&nbsp;Ning Gu","doi":"10.1016/j.bspc.2025.107955","DOIUrl":"10.1016/j.bspc.2025.107955","url":null,"abstract":"<div><div>Airway segmentation plays a crucial role in medical image processing. However, the accuracy and efficiency of existing segmentation methods still cannot meet the demands of practical applications. This paper proposes a novel airway segmentation method based on 3D UNet, which integrates a triple-attention mechanism and a new loss function based on skeletonization to improve the accuracy of airway segmentation. First, we obtain the multi-scale connectivity features and attention map by constructing a connectivity matrix. Then, by combining this attention map, we introduce spatial and channel attention mechanisms. Additionally, we incorporate an airway skeletonized loss function. This approach effectively address discontinuity issues and class imbalance in airway segmentation tasks, thereby improving the accuracy of airway segmentation. To validate the effectiveness of the method, we conducted a series of experiments on a publicly available dataset. The experimental results demonstrate significant performance improvements compared to the state-of-the-art methods in most metrics, especially in DLR and DBR, reaching 95.8% and 92.5%.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107955"},"PeriodicalIF":4.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized lifestyle recommendations for improved diabetes management leveraging machine learning","authors":"V L Deves Sabari ,&nbsp;G.R. Brindha ,&nbsp;Priya Dharshini Veeraragavan ,&nbsp;A. Sathya ,&nbsp;Muthu Thiruvengadam","doi":"10.1016/j.bspc.2025.107983","DOIUrl":"10.1016/j.bspc.2025.107983","url":null,"abstract":"<div><div>Diabetes is an extremely dangerous condition that is rapidly expanding, and its early diagnosis and effective management are crucial. Healthcare professionals must prioritize rapid diagnosis and personalized treatment strategies; however, with increasing patient numbers, existing healthcare facilities may be unable to meet this increased demand. As a result, it is critical that patients adopt self-management practices that are easy to comprehend using machine learning techniques. In this study, real-time blood glucose levels, blood pressure, and other lifestyle factors such as diet, exercise, stress, and sleep were collected, and Exploratory Data Analysis was conducted to compare the findings with those of previous research. Caloric estimation was performed using an extensive formulation for each participant. The parameters were divided into nine distinct cluster models using the K means clustering technique, and the resulting clusters were examined for features that yielded significant observations. The classification algorithms were used to segregate the new data into their appropriate clusters, and the obtained data were subjected to 5-fold cross validation to avoid overfitting and were classified into clusters using different classification algorithms such as Random Forest, Decision Tree, Naive Bayes, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor. Pertinent recommendations were provided to each cluster’s members based on the literature. An interactive web application was created by integrating machine learning models to improve user experience, which could be integrated into wearables in the future to revolutionize healthcare.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107983"},"PeriodicalIF":4.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional generative diffusion model with cascaded symmetric attention for brain connectivity-to-connectivity translation","authors":"Qiankun Zuo ,&nbsp;Yi Di ,&nbsp;Conghuan Ye ,&nbsp;Binghua Shi ,&nbsp;Junyi Chen ,&nbsp;Hui Wei ,&nbsp;Ruiheng Li ,&nbsp;Bangjun Lei","doi":"10.1016/j.bspc.2025.107900","DOIUrl":"10.1016/j.bspc.2025.107900","url":null,"abstract":"<div><div>Understanding the complex relationship between structural and functional connectivity is fundamental to discovering the pathogenesis of brain diseases. However, it is challenging to establish nonlinear connective relationships between structural and functional modalities. In this study, a novel bidirectional generative diffusion model (BGDM) is proposed to construct bidirectional mapping for connectivity-to-connectivity translation. The proposed BGDM is an AIGC framework to learn bidirectional translation between structural and functional connectivity. By designing the cascaded symmetric attention module, the BGDM can learn multi-channel and cascaded edge features to estimate diffusion noise, which improves computational efficiency and enhances the model’s ability to predict brain connectivity patterns. Furthermore, the connectivity noise-balanced loss is devised to adaptively adjust the importance of individual noisy connections and ensure more accurate and reliable translations between diverse brain connectivity representations. We demonstrate the effectiveness of BGDM through comprehensive experiments on the HCP datasets. Our model achieves the best translation performance with the mean MAE/SSIM of 0.065/0.835 for SC-to-FC translation, and the mean MAE/SSIM of 0.050/0.932 for FC-to-SC translation. Compared with the state-of-the-art method, the results of our model show improvements of 0.014 (MAE) and 0.06 (SSIM) for the SC-to-FC task, 0.014 (MAE) and 0.04 (SSIM) for FC-to-SC task. Our model can generate incomplete multimodal brain networks, which can be used to improve the accuracy of brain disease diagnosis and provide clinicians with disease-related biomarkers. Moreover, the proposed model has the potential to bridge the gap between structural connectivity and functional connectivity, offering new opportunities for understanding the brain’s working mechanisms and revealing brain disease’s pathogenesis.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107900"},"PeriodicalIF":4.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced feature vector reduction of S-transformed electroencephalography signal for optimal Parkinson’s disease detection","authors":"Melina Maria Afonso ,&nbsp;Damodar Reddy Edla ,&nbsp;Sridhar Chintala ,&nbsp;Ragoju Ravi","doi":"10.1016/j.bspc.2025.107922","DOIUrl":"10.1016/j.bspc.2025.107922","url":null,"abstract":"<div><div>Electroencephalography (EEG) has emerged as a valuable tool for detecting Parkinson’s disease (PD) due to its non-invasive nature and simplicity in data collection. By using deep learning and transformation techniques, researchers can extract numerous features from EEG signals, allowing for a detailed analysis of the disease. However, handling such a huge amount of data is computationally demanding, especially for the fabrication of small, portable handheld PD detection devices, which are a dire need due to the alarming rates at which PD is rising. To overcome this challenge, an enhanced version of the artificial butterfly optimization (ABO) algorithm is introduced to select the most significant features from the feature vectors extracted from the time–frequency transformed EEG signals using a deep convolution network. The algorithm improves upon the original ABO by enhancing its ability to explore and refine feature selection. The main improvement is an adaptive mutation rate, which changes dynamically during the process, starting with a higher rate for more exploration in the beginning and lowering it over time for a more focused optimal solution as the training progresses. This allows for a more efficient selection of crucial features, which are then given to a deep neural network to detect PD. Results show that using this modified ABO (M-ABO) paired with a multilayer perceptron as a fitness evaluator leads to faster, more effective performance compared to the original ABO and particle swarm optimization, achieving accuracy rates of 98.24% and 96.85% on two publicly available datasets.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107922"},"PeriodicalIF":4.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}