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Dong J, Zhang G, Hu Y, Wu Y, Rong H. An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation. Int J Neural Syst 2024; 34:2450036. [PMID: 38686911 DOI: 10.1142/s0129065724500369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved.
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Affiliation(s)
- Jianping Dong
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Gexiang Zhang
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yangheng Hu
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yijin Wu
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
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Jiménez-Huete A, Villino-Rodríguez R, Ríos-Rivera MM, Rognoni T, Montoya-Murillo G, Arrondo C, Zapata C, Rodríguez-Oroz MC, Riverol M. Clusters of cognitive performance predict long-term cognitive impairment in elderly patients with subjective memory complaints and healthy controls. Alzheimers Dement 2024. [PMID: 38779851 DOI: 10.1002/alz.13903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
INTRODUCTION Patients with subjective memory complaints (SMC) may include subgroups with different neuropsychological profiles and risks of cognitive impairment. METHODS Cluster analysis was performed on two datasets (n: 630 and 734) comprising demographic and neuropsychological data from SMC and healthy controls (HC). Survival analyses were conducted on clusters. Bayesian model averaging assessed the predictive utility of clusters and other biomarkers. RESULTS Two clusters with higher and lower than average cognitive performance were detected in SMC and HC. Assignment to the lower performance cluster increased the risk of cognitive impairment in both datasets (hazard ratios: 1.78 and 2.96; Plog-rank: 0.04 and <0.001) and was associated with lower hippocampal volumes and higher tau/amyloid beta 42 ratios in cerebrospinal fluid. The effect of SMC was small and confounded by mood. DISCUSSION This study provides evidence of the presence of cognitive clusters that hold biological significance and predictive value for cognitive decline in SMC and HC. HIGHLIGHTS Patients with subjective memory complaints include two cognitive clusters. Assignment to the lower performance cluster increases risk of cognitive impairment. This cluster shows a pattern of biomarkers consistent with incipient Alzheimer's disease pathology. The same cognitive cluster structure is found in healthy controls. The effect of memory complaints on risk of cognitive decline is small and confounded.
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Affiliation(s)
| | | | | | - Teresa Rognoni
- Department of Neurology, Clínica Universidad de Navarra, Madrid, Spain
| | | | - Carlota Arrondo
- Department of Neurology, Clínica Universidad de Navarra, Madrid, Spain
| | - Carolina Zapata
- Department of Neurology, Clínica Universidad de Navarra, Madrid, Spain
- Departament of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Facultad de Medicina, Avinguda de Can Domènech, Barcelona, Spain
| | | | - Mario Riverol
- Department of Neurology, Clínica Universidad de Navarra, Madrid, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Recinto del Hospital Universitario de Navarra, Pamplona, Spain
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3
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Jiang L, Eickhoff SB, Genon S, Wang G, Yi C, He R, Huang X, Yao D, Dong D, Li F, Xu P. Multimodal Covariance Network Reflects Individual Cognitive Flexibility. Int J Neural Syst 2024; 34:2450018. [PMID: 38372035 DOI: 10.1142/s0129065724500187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Guangying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Xunan Huang
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Foreign Languages, University of Electronic Science and Technology of China, Sichuan, Chengdu 611731, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Debo Dong
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Faculty of Psychology, Southwest University, Chongqing 400715, P. R. China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, ChengDu 610041, P. R. China
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China
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Ma Y, Zhang W, Du M, Jing H, Zheng N. Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex. Int J Neural Syst 2024; 34:2450002. [PMID: 38084473 DOI: 10.1142/s0129065724500023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.
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Affiliation(s)
- Yongqiang Ma
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Wen Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Ming Du
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Haodong Jing
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
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5
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Chen K, Zhuang W, Zhang Y, Yin S, Liu Y, Chen Y, Kang X, Ma H, Zhang T. Alteration of the large-scale white-matter functional networks in autism spectrum disorder. Cereb Cortex 2023; 33:11582-11593. [PMID: 37851712 DOI: 10.1093/cercor/bhad392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/20/2023] Open
Abstract
Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.
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Affiliation(s)
- Kai Chen
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yanfang Zhang
- Department of Ultrasonic Medicine, Baiyun Branch, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Shunjie Yin
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yinghua Liu
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yuan Chen
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No. 81 Bayi Road, Yongning Street, Wenjiang District, Chengdu City 610075, China
| | - Hailin Ma
- Plateau Brain Science Research Center, Tibet University, 10 Zangda East Road, Lhasa City 510631, China
| | - Tao Zhang
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
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6
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Hu J, Yu C, Yi Z, Zhang H. Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation. Int J Neural Syst 2023; 33:2350060. [PMID: 37743765 DOI: 10.1142/s0129065723500600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model's robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation.
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Affiliation(s)
- Junjie Hu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Chengrong Yu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Haixian Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
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7
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De Nardin A, Zottin S, Piciarelli C, Colombi E, Foresti GL. Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding. Int J Neural Syst 2023; 33:2350052. [PMID: 37567858 DOI: 10.1142/s0129065723500521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
Over the years, the humanities community has increasingly requested the creation of artificial intelligence frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at identifying the different structural components of a document page, is a particularly interesting task connected to this trend, specifically when it comes to handwritten texts. While there are many effective approaches to this problem, they all rely on large amounts of data for the training of the underlying models, which is rarely possible in a real-world scenario, as the process of producing the ground truth segmentation task with the required precision to the pixel level is a very time-consuming task and often requires a certain degree of domain knowledge regarding the documents at hand. For this reason, in this paper, we propose an effective few-shot learning framework for document layout segmentation relying on two novel components, namely a dynamic instance generation and a segmentation refinement module. This approach is able of achieving performances comparable to the current state of the art on the popular Diva-HisDB dataset, while relying on just a fraction of the available data.
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Affiliation(s)
- Axel De Nardin
- Department of Mathematics, Computer Science and Physics, Università degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Silvia Zottin
- Department of Mathematics, Computer Science and Physics, Università degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Claudio Piciarelli
- Department of Mathematics, Computer Science and Physics, Università degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Emanuela Colombi
- Department of Humanities and Cultural Heritage, Università degli Studi di Udine, Vicolo Florio 2/b, 33100 Udine, Italy
| | - Gian Luca Foresti
- Department of Mathematics, Computer Science and Physics, Università degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy
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8
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Xian R, Lugu R, Peng H, Yang Q, Luo X, Wang J. Edge Detection Method Based on Nonlinear Spiking Neural Systems. Int J Neural Syst 2023; 33:2250060. [PMID: 36328966 DOI: 10.1142/s0129065722500605] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a distinguishing feature: nonlinear spiking mechanism. To handle edge detection of images, this paper proposes a variant, nonlinear spiking neural P (NSNP) systems with two outputs (TO), termed as NSNP-TO systems. Based on NSNP-TO system, an edge detection framework is developed, termed as ED-NSNP detector. The detection ability of ED-NSNP detector relies on two convolutional kernels. To obtain good detection performance, particle swarm optimization (PSO) is used to optimize the parameters of the two convolutional kernels. The proposed ED-NSNP detector is evaluated on several open benchmark images and compared with seven baseline edge detection methods. The comparison results indicate the availability and effectiveness of the proposed ED-NSNP detector.
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Affiliation(s)
- Ronghao Xian
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Rikong Lugu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaohui Luo
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
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Wang J, Zhang L, Zhang Y. Mixture 2D Convolutions for 3D Medical Image Segmentation. Int J Neural Syst 2023; 33:2250059. [PMID: 36328969 DOI: 10.1142/s0129065722500599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Three-dimensional (3D) medical image segmentation plays a crucial role in medical care applications. Although various two-dimensional (2D) and 3D neural network models have been applied to 3D medical image segmentation and achieved impressive results, a trade-off remains between efficiency and accuracy. To address this issue, a novel mixture convolutional network (MixConvNet) is proposed, in which traditional 2D/3D convolutional blocks are replaced with novel MixConv blocks. In the MixConv block, 3D convolution is decomposed into a mixture of 2D convolutions from different views. Therefore, the MixConv block fully utilizes the advantages of 2D convolution and maintains the learning ability of 3D convolution. It acts as 3D convolutions and thus can process volumetric input directly and learn intra-slice features, which are absent in the traditional 2D convolutional block. By contrast, the proposed MixConv block only contains 2D convolutions; hence, it has significantly fewer trainable parameters and less computation budget than a block containing 3D convolutions. Furthermore, the proposed MixConvNet is pre-trained with small input patches and fine-tuned with large input patches to improve segmentation performance further. In experiments on the Decathlon Heart dataset and Sliver07 dataset, the proposed MixConvNet outperformed the state-of-the-art methods such as UNet3D, VNet, and nnUnet.
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Affiliation(s)
- Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Lei Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Yi Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China
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Sun Q, Wang X, Shi C, Guan J, Chen L, Wang Y, Wang S, Diwu J. Effective mitigation of gadolinium deposition using the bidentate hydroxypyridinone ligand Me-3,2-HOPO. Dalton Trans 2022; 51:13055-13060. [PMID: 35971987 DOI: 10.1039/d2dt00747a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
With the extensive usage of gadolinium-based contrast agents (GBCAs) in magnetic resonance imaging (MRI), gadolinium deposition has been observed in the brain, kidneys, liver, etc., and this is also closely related to the development of nephrogenic systemic fibrosis (NSF) in patients with renal dysfunction. Chelation, thereby promoting the elimination of deposited Gd(III), seems to be promising for alleviating these problems. Despite many ligands suitable for chelation therapy having been studied, the decorporation of transition metals (e.g. iron, copper, lead, etc.) and actinides (e.g. uranium, plutonium, etc.) has long been a primary concern, whereas the study of Gd(III) has been extremely limited. Due to their excellent metal binding abilities in vivo and therapeutic effects toward neurodegenerative diseases, bidentate hydroxypyridinone ligands are expected to be able to remove Gd(III) from the brain, kidneys, bones, and liver. Herein, the Gd(III) decorporation efficacy of a bidentate hydroxypyridinone ligand (Me-3,2-HOPO) has been evaluated. The complexation behavior between Me-3,2-HOPO and Gd(III) in solution and solid states was characterized with the assistance of potentiometric titration and X-ray diffraction techniques, respectively. Solution-based thermodynamic studies illustrate that the dominant species of complex between Gd(III) and Me-3,2-HOPO (HL) is GdL2+ (log β120 = 11.8 (3)) at pH 7.4. The structure of the Gd-Me-3,2-HOPO crystal obtained from a room temperature reaction reveals the formation of a Gd(III) dimer that is chelated by four ligands as a result of metal ion hydration and ligand complexation. Cellular Gd(III) removal assays illustrate that Me-3,2-HOPO could effectively reduce final amounts of gadolinium by 77.6% and 66.1% from rat renal proximal tubular epithelial (NRK-52E) cells and alpha mouse liver 12 (AML-12) cells, respectively. Our current results suggest the potential of bidentate HOPO ligands as an effective approach to treat patients suffering from Gd(III) toxicity.
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Affiliation(s)
- Qiwen Sun
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Xiaomei Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Cen Shi
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Jingwen Guan
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Lanhua Chen
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Yumin Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Shuao Wang
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
| | - Juan Diwu
- State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China.
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Manuello J, Verdejo-Román J, Torres Espínola F, Escudero-Marín M, Catena A, Cauda F, Campoy C. Influence of Gestational Diabetes and Pregestational Maternal BMI on the Brain of Six-Year-Old Offspring. Pediatr Neurol 2022; 133:55-62. [PMID: 35759804 DOI: 10.1016/j.pediatrneurol.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/02/2022] [Accepted: 05/10/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Gestational diabetes (GD) and maternal excess weight are common pregnancy conditions that increase the risk of future complications for both the mother and her offspring. Their consequences on neurodevelopment are widely described in the literature, but less is known concerning the potential transgenerational influence on the brain structure. METHODS We used a combination of support vectors machine and hierarchical clustering to investigate the potential presence of anatomical brain differences in a sample of 109 children aged six years, born to mothers with overweight or obesity, or to mothers diagnosed with GD during pregnancy. RESULTS Significant effects are visible in the brain of children born to mothers with GD associated with pregestational excess weight, especially overweight instead of obesity. No differences in children's brain were observed when considering those born to normal-weight mothers. CONCLUSIONS Our study highlights the need for clinical attention of pregnant women at risk to develop GD, and especially those with pregestational excess weight, since this status was found to be associated with detectable transgenerational brain changes. These effects may be due to the absence of specific and individualized intervention in these mothers during pregnancy.
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Affiliation(s)
- Jordi Manuello
- Gcs-Fmri, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Focus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Juan Verdejo-Román
- Mind, Brain and Behavior Research Centre, University of Granada, Granada, Spain
| | - Francisco Torres Espínola
- Euristikos Excellence Centre For Pediatric Research, University of Granada, Granada, Spain; Department of Pediatrics, School of Medicine, University of Granada, Granada, Spain; DR. Federico Oloriz Neurosciences Institute, University of Granada, Granada, Spain
| | - Mireia Escudero-Marín
- Euristikos Excellence Centre For Pediatric Research, University of Granada, Granada, Spain; Department of Pediatrics, School of Medicine, University of Granada, Granada, Spain; DR. Federico Oloriz Neurosciences Institute, University of Granada, Granada, Spain
| | - Andrés Catena
- Mind, Brain and Behavior Research Centre, University of Granada, Granada, Spain
| | - Franco Cauda
- Gcs-Fmri, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Focus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Cristina Campoy
- Euristikos Excellence Centre For Pediatric Research, University of Granada, Granada, Spain; Department of Pediatrics, School of Medicine, University of Granada, Granada, Spain; DR. Federico Oloriz Neurosciences Institute, University of Granada, Granada, Spain; Spanish Network of Biomedical Research In Epidemiology and Public Health (Ciberesp), Granada's Node, Institute of Health Carlos III, Madrid, Spain; Biohealth Research Institute (IBS), Granada, Health Sciences Technological Park, Granada, Spain.
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12
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Wang K, Wang Y, Zhan B, Yang Y, Zu C, Wu X, Zhou J, Nie D, Zhou L. An Efficient Semi-Supervised Framework with Multi-Task and Curriculum Learning for Medical Image Segmentation. Int J Neural Syst 2022; 32:2250043. [DOI: 10.1142/s0129065722500435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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13
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Li S, Tang Z, Jin N, Yang Q, Liu G, Liu T, Hu J, Liu S, Wang P, Hao J, Zhang Z, Zhang X, Li J, Wang X, Li Z, Wang Y, Yang B, Ma L. Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder using Deep Learning. Int J Neural Syst 2022; 32:2250044. [DOI: 10.1142/s0129065722500447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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14
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Jiang L, Li F, Chen B, Yi C, Peng Y, Zhang T, Yao D, Xu P. The task-dependent modular covariance networks unveiled by multiple-way fusion-based analysis. Int J Neural Syst 2022; 32:2250035. [PMID: 35719086 DOI: 10.1142/s0129065722500356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Tao Zhang
- School of Science, Xihua University, Chengdu 610039, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, P. R. China
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15
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Hua Y, Shu X, Wang Z, Zhang L. Uncertainty-Guided Voxel-Level Supervised Contrastive Learning for Semi-Supervised Medical Image Segmentation. Int J Neural Syst 2022; 32:2250016. [DOI: 10.1142/s0129065722500162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by regularizing the learning of limited well-annotated data with the knowledge provided by a large amount of unlabeled data. However, there are many misuses and underutilization of data in conventional semi-supervised methods. On the one hand, the model will deviate from the empirical distribution under the training of numerous unlabeled data. On the other hand, the model treats labeled and unlabeled data differently and does not consider inter-data information. In this paper, a semi-supervised method is proposed to exploit unlabeled data to further narrow the gap between the semi-supervised model and its fully-supervised counterpart. Specifically, the architecture of the proposed method is based on the mean-teacher framework, and the uncertainty estimation module is improved to impose constraints of consistency and guide the selection of feature representation vectors. Notably, a voxel-level supervised contrastive learning module is devised to establish a contrastive relationship between feature representation vectors, whether from labeled or unlabeled data. The supervised manner ensures that the network learns the correct knowledge, and the dense contrastive relationship further extracts information from unlabeled data. The above overcomes data misuse and underutilization in semi-supervised frameworks. Moreover, it favors the feature representation with intra-class compactness and inter-class separability and gains extra performance. Extensive experimental results on the left atrium dataset from Atrial Segmentation Challenge demonstrate that the proposed method has superior performance over the state-of-the-art methods.
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Affiliation(s)
- Yu Hua
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Xin Shu
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Zizhou Wang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
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16
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Zhang W, Li Y, Chen G, Yang X, Hu J, Zhang X, Feng G, Wang H. Integrin α6-Targeted Molecular Imaging of Central Nervous System Leukemia in Mice. Front Bioeng Biotechnol 2022; 10:812277. [PMID: 35284414 PMCID: PMC8905628 DOI: 10.3389/fbioe.2022.812277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/17/2022] [Indexed: 11/14/2022] Open
Abstract
Central nervous system leukemia (CNS-L) is caused by leukemic cells infiltrating into the meninges or brain parenchyma and remains the main reason for disease relapse. Currently, it is hard to detect CNS-L accurately by clinically available imaging models due to the relatively low amount of tumor cells, confined blood supply, and the inferior glucose metabolism intensity. Recently, integrin α6-laminin interactions have been identified to mediate CNS-L, which suggests that integrin α6 may be a promising molecular imaging target for the detection of CNS-L. The acute lymphoblastic leukemia (ALL) cell line NALM6 stabled and transfected with luciferase was used to establish the CNS-L mouse model. CNS-L-bearing mice were monitored and confirmed by bioluminescence imaging. Three of our previously developed integrin α6-targeted peptide-based molecular imaging agents, Cy5-S5 for near-infrared fluorescence (NIRF), Gd-S5 for magnetic resonance (MR), and 18F-S5 for positron emission tomography (PET) imaging, were employed for the molecular imaging of these CNS-L-bearing mice. Bioluminescence imaging showed a local intensive signal in the heads among CNS-L-bearing mice; meanwhile, Cy5-S5/NIRF imaging produced intensive fluorescence intensity in the same head regions. Moreover, Gd-S5/MR imaging generated superior MR signal enhancement at the site of meninges, which were located between the skull bone and brain parenchyma. Comparatively, MR imaging with the clinically available MR enhancer Gd-DTPA did not produce the distinguishable MR signal in the same head regions. Additionally, 18F-S5/PET imaging also generated focal radio-concentration at the same head regions, which generated nearly 5-times tumor-to-background ratio compared to the clinically available PET radiotracer 18F-FDG. Finally, pathological examination identified layer-displayed leukemic cells in the superficial part of the brain parenchyma tissue, and immunohistochemical staining confirmed the overexpression of the integrin α6 within the lesion. These findings suggest the potential application of these integrin α6-targeted molecular imaging agents for the accurate detection of CNS-L.
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Affiliation(s)
- Wenbiao Zhang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yongjiang Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guanjun Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Hematological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaochun Yang
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Junfeng Hu
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaofei Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Xiaofei Zhang, ; Guokai Feng, ; Hua Wang,
| | - Guokai Feng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Xiaofei Zhang, ; Guokai Feng, ; Hua Wang,
| | - Hua Wang
- Department of Hematological Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Xiaofei Zhang, ; Guokai Feng, ; Hua Wang,
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17
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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18
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Xie P, Hao S, Zhao J, Liang Z, Li X. A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task. Int J Neural Syst 2022; 32:2250010. [PMID: 35049411 DOI: 10.1142/s0129065722500101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in [Formula: see text] cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.
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Affiliation(s)
- Ping Xie
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Shencai Hao
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Jing Zhao
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Zhenhu Liang
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, P. R. China
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19
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Nature-inspired dynamic gene-loaded nanoassemblies for the treatment of brain diseases. Adv Drug Deliv Rev 2022; 180:114029. [PMID: 34752841 DOI: 10.1016/j.addr.2021.114029] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 09/03/2021] [Accepted: 10/27/2021] [Indexed: 12/14/2022]
Abstract
Gene therapy has great potential to treat brain diseases. However, genetic drugs need to overcome a cascade of barriers for their full potential. The conventional delivery systems often struggle to meet expectations. Natural biological particles that are highly optimized for specific functions in body, can inspire optimization of dynamic gene-loaded nanoassemblies (DGN). The DGN refer to gene loaded nanoassemblies whose functions and structures are changeable in response to the biological microenvironments or can dynamically interact with tissues or cells. The nature-inspired DGN can meet the needs in brain diseases treatment, including i) Non-elimination in blood (N), ii) Across the blood-brain barrier (A), iii) Targeting cells (T), iv) Efficient uptake (U), v) Controllable release (R), vi) Eyeable (E)-abbreviated as the "NATURE". In this Review, from nature to "NATURE", we mainly summarize the specific application of nature-inspired DGN in the "NATURE" cascade process. Furthermore, the Review provides an outlook for this field.
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20
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Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas. Neuroinformatics 2021; 20:405-426. [PMID: 34825350 PMCID: PMC9546954 DOI: 10.1007/s12021-021-09555-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2021] [Indexed: 11/29/2022]
Abstract
Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.
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21
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Optimized Clustering Algorithm for Comparative Analysis of Different Prenatal Corticosteroid Neurological Deficits in Premature Infants through Magnetic Reasoning Imaging (MRI). CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:6179177. [PMID: 34385897 PMCID: PMC8331282 DOI: 10.1155/2021/6179177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/13/2021] [Accepted: 07/22/2021] [Indexed: 11/24/2022]
Abstract
Objective This study aimed to explore the application of different prenatal corticosteroids in the assessment of neurological deficits and prognosis in premature infants through Magnetic Reasoning Imaging (MRI) under optimized cluster algorithm. Methods 100 pregnant women with threatened preterm labor were retrospectively analyzed, in which 38 pregnant women with lasting threatened preterm labor (group A) were treated with multiple courses of antenatal corticosteroids (dexamethasone treatment) and 62 cases of pregnant women with threatened preterm labor (group B) were treated with single course of dexamethasone treatment. Craniocerebral MRI images based on optimal clustering algorithm were used to examine neonates. Neonatal hypoxic-ischemic encephalopathy (HIE) rate, serum neuron-specific enolase (NSE) concentration, neonatal behavioral neurological score (NBNA), respiratory distress syndrome (RDS) rate, perinatal mortality, neonatal birth weight, and maternal complications rate of two groups were compared. Results Compared with other traditional image segmentation algorithms, this algorithm had the best segmentation effect, the shortest running time (1.43 s), the least number of iterations (5 times), and the highest segmentation accuracy (97.98%). There was no significant difference in the HIE rate, serum NSE concentration, NBNA score, RDS score, and perinatal mortality in group A and group B (P > 0.05). Compared with group B, neonates' body weight in group A was decreased, while the maternal complication rate in group A was increased (P < 0.05). Conclusion MRI images based on optimized clustering algorithm can be used in the diagnosis of neonatal hypoxic-ischemic encephalopathy. There is no significant difference in the application of different antenatal corticosteroids affecting premature nerve function defect and prognosis, but multiple courses of antenatal corticosteroids can affect neonatal body mass and increased maternal complications to a certain extent; therefore, before threatened premature delivery treatment, the pros and cons of multiple courses of antenatal corticosteroids should fully be considered and in the treatment, measures should be actively taken to alleviate the side effect.
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Fuzzy C-Means Clustering Algorithm-Based Magnetic Resonance Imaging Image Segmentation for Analyzing the Effect of Edaravone on the Vascular Endothelial Function in Patients with Acute Cerebral Infarction. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:4080305. [PMID: 34354551 PMCID: PMC8295001 DOI: 10.1155/2021/4080305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 07/05/2021] [Indexed: 12/18/2022]
Abstract
This paper aimed to discuss the denoising ability of magnetic resonance imaging (MRI) images based on fuzzy C-means clustering (FCM) algorithm and the influence of Butylphthalide combined with Edaravone treatment on nerve function and vascular endothelial function in patients with acute cerebral infarction (ACI). Based on FCM algorithm, Markov Random Field (MRF) model algorithm was introduced to obtain a novel algorithm (NFCM), which was compared with FCM and MRF algorithm in terms of misclassification rate (MCR) and difference of Kappa index (KI). 90 patients with ACI diagnosed in hospital from December 2018 to December 2019 were selected as subjects, who were divided into combined treatment group (conventional treatment + Edaravone + Butylphthalide) and Edaravone group (conventional treatment + Edaravone) randomly, each consisting of 45 cases. The National Institutes of Health Stroke Scale (NIHSS) score and endothelial function index level such as plasma nitric oxide (NO), human endothelin-1 (ET-1), and vascular endothelial cell growth factor (VEGF) were compared before and after treatment between the two groups. The results showed that the MCR of NFCM was evidently inferior to FCM and MRF, and the KI was notably higher relative to the other two algorithms. After treatment, the NIHSS score of the combined treatment group was (9.09 ± 1.86) points and that of Edaravone group was (14.97 ± 3.44) points, with evident difference between the two groups (P < 0.05). After treatment, the NO of the combined treatment was (54.63 ± 4.85), and that of Edaravone group was (41.54 ± 5.27), which was considerably different (P < 0.01), and the VEGF and ET-1 of combined treatment group were greatly inferior to Edaravone group (P < 0.01). It was revealed that the novel algorithm based on FCM can obtain more favorable quality and segmentation accuracy of MRI images. Moreover, Butylphthalide combined with Edaravone treatment can effectively improve nerve function, vascular endothelial function, and short-term prognosis in ACI, which was safe and worthy of clinical adoption.
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Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images. JOURNAL OF HEALTHCARE ENGINEERING 2021. [DOI: 10.1155/2021/5573010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm based on sequential probability ratio hypothesis testing is applied to gradually merge the generated superpixels to form dozens of statistically significant regions. Finally, these regions are postprocessed to obtain the segmentation results of each organization of GBM. Combine 2D multimodal MRI images into 3D original features and extract features through 3D-CNNs, which is more conducive to extracting the difference information between the modalities, removing redundant interference information between the modalities, and reducing the original features at the same time. The size of the neighborhood can adapt to the difference of tumor size in different image layers of the same patient and further improve the segmentation accuracy of MRI brain tumors. The experimental results prove that it can adapt to the differences and variability between the modalities of different patients to improve the segmentation accuracy of brain tumors.
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Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021; 339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 12/13/2022]
Abstract
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
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Affiliation(s)
- Fabian Eitel
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Marc-André Schulz
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Moritz Seiler
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany.
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25
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Dadar M, Collins DL. BISON: Brain tissue segmentation pipeline using T 1 -weighted magnetic resonance images and a random forest classifier. Magn Reson Med 2020; 85:1881-1894. [PMID: 33040404 DOI: 10.1002/mrm.28547] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/16/2020] [Accepted: 09/17/2020] [Indexed: 01/18/2023]
Abstract
PURPOSE Tissue segmentation from T1 -weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI. METHODS BISON was developed and cross-validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test-retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state-of-the-art commonly used tissue classification method from advanced normalization tools (ANTs). RESULTS BISON cross-validation dice kappa values against manual segmentations of 72 MRI volumes yielded κGM = 0.88, κWM = 0.85, κCSF = 0.77, outperforming Atropos (κGM = 0.79, κWM = 0.84, κCSF = 0.64), test-retest values on 20 subjects of κGM = 0.94, κWM = 0.92, κCSF = 0.77 outperforming both manual (κGM = 0.92, κWM = 0.91, κCSF =0.74) and Atropos (κGM = 0.87, κWM = 0.92, κCSF = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs. CONCLUSION BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.
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Affiliation(s)
- Mahsa Dadar
- NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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Azimbagirad M, Simozo FH, Senra Filho ACS, Murta Junior LO. Tsallis-Entropy Segmentation through MRF and Alzheimer anatomic reference for Brain Magnetic Resonance Parcellation. Magn Reson Imaging 2019; 65:136-145. [PMID: 31726210 DOI: 10.1016/j.mri.2019.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 10/17/2019] [Accepted: 11/03/2019] [Indexed: 02/04/2023]
Abstract
Quantifying the intracranial tissue volume changes in magnetic resonance imaging (MRI) assists specialists to analyze the effects of natural or pathological changes. Since these changes can be subtle, the accuracy of the automatic compartmentalization method is always criticized by specialists. We propose and then evaluate an automatic segmentation method based on modified q-entropy (Mqe) through a modified Markov Random Field (MMRF) enhanced by Alzheimer anatomic reference (AAR) to provide a high accuracy brain tissues parcellation approach (Mqe-MMRF). We underwent two strategies to evaluate Mqe-MMRF; a simulation of different levels of noise and non-uniformity effect on MRI data (7 subjects) and a set of twenty MRI data available from MRBrainS13 as patient brain tissue segmentation challenge. We accessed eleven quality metrics compared to reference tissues delineations to evaluate Mqe-MMRF. MRI segmentation scores decreased by only 4.6% on quality metrics after noise and non-uniformity simulations of 40% and 9%, respectively. We found significant mean improvements in the metrics of the five training subjects, for whole-brain 0.86%, White Matter 3.20%, Gray Matter 3.99%, and Cerebrospinal Fluid 4.16% (p-values < 0.02) when Mqe-MMRF compared to the other reference methods. We also processed the Mqe-MMRF on 15 evaluation subjects group from MRBrainS13 online challenge, and the results held a higher rank than the reference tools; FreeSurfer, SPM, and FSL. Since the proposed method improved the precision of brain segmentation, specifically, for GM, and thus one can use it in quantitative and morphological brain studies.
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Affiliation(s)
- Mehran Azimbagirad
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Ribeirao Preto, SP, Brazil; Department of Physics, FFCLRP, University of São Paulo, Ribeirao Preto, SP, Brazil
| | - Fabrício H Simozo
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Ribeirao Preto, SP, Brazil
| | - Antonio C S Senra Filho
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Ribeirao Preto, SP, Brazil
| | - Luiz O Murta Junior
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Ribeirao Preto, SP, Brazil.
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