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Muta K, Takata S, Utsumi Y, Matsumura A, Iwamura M, Kise K. TAIM: Tool for Analyzing Root Images to Calculate the Infection Rate of Arbuscular Mycorrhizal Fungi. FRONTIERS IN PLANT SCIENCE 2022; 13:881382. [PMID: 35592584 PMCID: PMC9111841 DOI: 10.3389/fpls.2022.881382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 03/31/2022] [Indexed: 06/15/2023]
Abstract
Arbuscular mycorrhizal fungi (AMF) infect plant roots and are hypothesized to improve plant growth. Recently, AMF is now available for axenic culture. Therefore, AMF is expected to be used as a microbial fertilizer. To evaluate the usefulness of AMF as a microbial fertilizer, we need to investigate the relationship between the degree of root colonization of AMF and plant growth. The method popularly used for calculation of the degree of root colonization, termed the magnified intersections method, is performed manually and is too labor-intensive to enable an extensive survey to be undertaken. Therefore, we automated the magnified intersections method by developing an application named "Tool for Analyzing root images to calculate the Infection rate of arbuscular Mycorrhizal fungi: TAIM." TAIM is a web-based application that calculates the degree of AMF colonization from images using automated computer vision and pattern recognition techniques. Experimental results showed that TAIM correctly detected sampling areas for calculation of the degree of infection and classified the sampling areas with 87.4% accuracy. TAIM is publicly accessible at http://taim.imlab.jp/.
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Affiliation(s)
- Kaoru Muta
- Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan
| | - Shiho Takata
- Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka, Japan
| | - Yuzuko Utsumi
- Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan
| | - Atsushi Matsumura
- Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka, Japan
| | - Masakazu Iwamura
- Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan
| | - Koichi Kise
- Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan
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Jiang H, Li S, Li H. Parallel ‘same’ and ‘valid’ convolutional block and input-collaboration strategy for histopathological image classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Olveres J, González G, Torres F, Moreno-Tagle JC, Carbajal-Degante E, Valencia-Rodríguez A, Méndez-Sánchez N, Escalante-Ramírez B. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 2021; 11:3830-3853. [PMID: 34341753 DOI: 10.21037/qims-20-1151] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 04/20/2021] [Indexed: 12/15/2022]
Abstract
Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.
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Affiliation(s)
- Jimena Olveres
- Centro de Estudios en Computación Avanzada, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico.,Departamento de Procesamiento de Señales, Facultad de Ingeniería, UNAM, Mexico City, Mexico
| | - Germán González
- Departamento de Procesamiento de Señales, Facultad de Ingeniería, UNAM, Mexico City, Mexico
| | - Fabian Torres
- Centro de Estudios en Computación Avanzada, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico.,Departamento de Procesamiento de Señales, Facultad de Ingeniería, UNAM, Mexico City, Mexico
| | | | | | | | - Nahum Méndez-Sánchez
- Unidad de Investigación en Hígado, Fundación Clínica Médica Sur, Mexico City, Mexico.,Facultad de Medicina, UNAM, Mexico City, Mexico
| | - Boris Escalante-Ramírez
- Centro de Estudios en Computación Avanzada, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico.,Departamento de Procesamiento de Señales, Facultad de Ingeniería, UNAM, Mexico City, Mexico
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Rana P, Sowmya A, Meijering E, Song Y. Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation. Sci Rep 2021; 11:3364. [PMID: 33564040 PMCID: PMC7873284 DOI: 10.1038/s41598-021-82985-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/22/2021] [Indexed: 12/22/2022] Open
Abstract
Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature and is evaluated on publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines obtained from the Statistics Online Computational Resource. Results show that 3D SRP and 3D Local Binary Pattern provide better classification results than other feature descriptors. In addition, the proposed metrics based on 3D SRP validate the change in intensity and aggregation of heterochromatin on transition to another state and characterise the intermediate and ultimate phenotypic states.
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Affiliation(s)
- Priyanka Rana
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.,Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
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Połap D. An adaptive genetic algorithm as a supporting mechanism for microscopy image analysis in a cascade of convolution neural networks. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106824] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Martins AS, Neves LA, de Faria PR, Tosta TAA, Longo LC, Silva AB, Roberto GF, do Nascimento MZ. A Hermite polynomial algorithm for detection of lesions in lymphoma images. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00927-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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7
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Analysis of cancer in histological images: employing an approach based on genetic algorithm. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00931-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Dif N, Elberrichi Z. Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2020. [DOI: 10.4018/ijcini.2020100104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.
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Affiliation(s)
- Nassima Dif
- EEDIS Laboratory ,Djillali Liabes University, Sidi Bel Abbes, Algeria
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Gao F, Yoon H, Xu Y, Goradia D, Luo J, Wu T, Su Y. AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction. Neuroimage Clin 2020; 27:102290. [PMID: 32570205 PMCID: PMC7306626 DOI: 10.1016/j.nicl.2020.102290] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 04/11/2020] [Accepted: 05/15/2020] [Indexed: 12/26/2022]
Abstract
The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have been developed to serve the purpose. With extensive methodology development efforts on neuroimaging, an emerging field is deep learning research. One great challenge facing deep learning is the limited medical imaging data available. To address the issue, researchers explore the use of transfer learning to extend the applicability of deep models on neuroimaging research for AD diagnosis and prognosis. Existing transfer learning models mostly focus on transferring the features from the pre-training into the fine-tuning stage. Recognizing the advantages of the knowledge gained during the pre-training, we propose an AD-NET (Age-adjust neural network) with the pre-training model serving two purposes: extracting and transferring features; and obtaining and transferring knowledge. Specifically, the knowledge being transferred in this research is an age-related surrogate biomarker. To evaluate the effectiveness of the proposed approach, AD-NET is compared with 8 classification models from literature using the same public neuroimaging dataset. Experimental results show that the proposed AD-NET outperforms the competing models in predicting the MCI patients at risk for conversion to the AD stage.
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Affiliation(s)
- Fei Gao
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, United States; ASU-Mayo Center for Innovative Imaging, Arizona State University, United States
| | - Hyunsoo Yoon
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, United States; ASU-Mayo Center for Innovative Imaging, Arizona State University, United States
| | - Yanzhe Xu
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, United States; ASU-Mayo Center for Innovative Imaging, Arizona State University, United States
| | - Dhruman Goradia
- Banner Alzheimer Institute, United States; Arizona Alzheimer's Consortium, United States
| | - Ji Luo
- Banner Alzheimer Institute, United States; Arizona Alzheimer's Consortium, United States
| | - Teresa Wu
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, United States; ASU-Mayo Center for Innovative Imaging, Arizona State University, United States.
| | - Yi Su
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, United States; Banner Alzheimer Institute, United States; Arizona Alzheimer's Consortium, United States.
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BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.044] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Bui AT, Apley DW. An exploratory analysis approach for understanding variation in stochastic textured surfaces. Comput Stat Data Anal 2019. [DOI: 10.1016/j.csda.2019.01.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Li Y, Xie X, Shen L, Liu S. Reverse active learning based atrous DenseNet for pathological image classification. BMC Bioinformatics 2019; 20:445. [PMID: 31455228 PMCID: PMC6712615 DOI: 10.1186/s12859-019-2979-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 07/01/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack of annotation capabilities. To address these challenges, we propose a training strategy called deep-reverse active learning (DRAL) and atrous DenseNet (ADN) for pathological image classification. The proposed DRAL can improve the classification accuracy of widely used deep learning networks such as VGG-16 and ResNet by removing mislabeled patches in the training set. As the size of a cancer area varies widely in pathological images, the proposed ADN integrates the atrous convolutions with the dense block for multiscale feature extraction. RESULTS The proposed DRAL and ADN are evaluated using the following three pathological datasets: BACH, CCG, and UCSB. The experiment results demonstrate the excellent performance of the proposed DRAL + ADN framework, achieving patch-level average classification accuracies (ACA) of 94.10%, 92.05% and 97.63% on the BACH, CCG, and UCSB validation sets, respectively. CONCLUSIONS The DRAL + ADN framework is a potential candidate for boosting the performance of deep learning models for partially mislabeled training datasets.
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Affiliation(s)
- Yuexiang Li
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
- Youtu Lab, Tencent, Shenzhen, China
| | - Xinpeng Xie
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Linlin Shen
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
- Guangdong Key Laboratory of IntelligentInformation Processing, Shenzhen University, Shenzhen, China
- The National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
| | - Shaoxiong Liu
- The Sixth People’s Hospital of Shenzhen, Shenzhen, China
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Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S, Lei B, Wang T. Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features. IEEE Trans Biomed Eng 2019; 66:1006-1016. [DOI: 10.1109/tbme.2018.2866166] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations. BIOMED RESEARCH INTERNATIONAL 2019; 2019:1065652. [PMID: 31016181 PMCID: PMC6448331 DOI: 10.1155/2019/1065652] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 03/04/2019] [Indexed: 01/31/2023]
Abstract
Background Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). Methods There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result. Results The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998. Conclusion The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.
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Deep Instance-Level Hard Negative Mining Model for Histopathology Images. LECTURE NOTES IN COMPUTER SCIENCE 2019. [DOI: 10.1007/978-3-030-32239-7_57] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Peng S, Ser W, Chen B, Sun L, Lin Z. Correntropy based graph regularized concept factorization for clustering. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.049] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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do Nascimento MZ, Martins AS, Azevedo Tosta TA, Neves LA. Lymphoma images analysis using morphological and non-morphological descriptors for classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:65-77. [PMID: 30119858 DOI: 10.1016/j.cmpb.2018.05.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 06/08/2023]
Abstract
Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.
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Affiliation(s)
- Marcelo Zanchetta do Nascimento
- UFU - FACOM, av. João Neves de Ávila 2121, Bl.B, Uberlândia-MG 38400-902, Brazil; UFABC - CMCC, av. dos Estados 5001, Bl.B, St. André-SP 09210-580, Brazil.
| | | | | | - Leandro Alves Neves
- UNESP - DCCE, r. Cristóvão Colombo 2265, S.J. Rio Preto-SP 15054-000, Brazil
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