Base de dados : MEDLINE
Pesquisa : G17.485 [Categoria DeCS]
Referências encontradas : 19825 [refinar]
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  1 / 19825 MEDLINE  
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[PMID]:29385183
[Au] Autor:Gao Z; Wang Y
[Ad] Endereço:School of Automation, Guangdong University of Technology, Guangzhou, Guangdong Province, China.
[Ti] Título:The structural balance analysis of complex dynamical networks based on nodes' dynamical couplings.
[So] Source:PLoS One;13(1):e0191941, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:The nodes and their connection relationships are the two main bodies for dynamic complex networks. In existing theoretical researches, the phenomena of stabilization and synchronization for complex dynamical networks are generally regarded as the dynamic characteristic behaviors of the nodes, which are mainly caused by coupling effect of connection relationships between nodes. However, the connection relationships between nodes are also one main body of a time-varying dynamic complex network, and thus they may evolve with time and maybe show certain characteristic phenomena. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. Therefore, it is important to investigate theoretically the reasons in dynamics for the occurrence. Especially, from the angle of large-scale systems, how the dynamic behaviors of nodes (such as the individuals, neurons) contribute to the connection relationships is one of worthy research directions. In this paper, according to the structural balance theory of triad proposed by F. Heider, we mainly focus on the connection relationships body, which is regarded as one of the two subsystems (another is the nodes body), and try to find the dynamic mechanism of the structural balance with the internal state behaviors of the nodes. By using the Riccati linear matrix differential equation as the dynamic model of connection relationships subsystem, it is proved under some mathematic conditions that the connection relationships subsystem is asymptotical structural balance via the effects of the coupling roles with the internal state of nodes. Finally, the simulation example is given to show the validity of the method in this paper.
[Mh] Termos MeSH primário: Redes Neurais (Computação)
[Mh] Termos MeSH secundário: Simulação por Computador
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180201
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0191941


  2 / 19825 MEDLINE  
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[PMID]:29346451
[Au] Autor:Chen H; Qian C; Liang C; Kang W
[Ad] Endereço:School of Materials Science and Engineering, Southeast University, Nanjing, Jiangsu province, China.
[Ti] Título:An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack.
[So] Source:PLoS One;13(1):e0191370, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:In this paper, a support vector machine (SVM) model which can be used to predict the compressive strength of mortars exposed to sulfate attack was established. An accelerated corrosion test was applied to collect compressive strength data. For predicting the compressive strength of mortars, a total of 638 data samples obtained from experiment was chosen as a dataset to establish a SVM model. The values of the coefficient of determination, the mean absolute error, the mean absolute percentage error and the root mean square error were used for evaluating the predictive accuracy. The main factors affecting the predicted compressive strength were obtained by sensitivity analysis. A SVM model was calibrated, validated, and finally established. Moreover, the performance of the SVM model was compared to an artificial neural network (ANN) model. Results show that the prediction values from the SVM model were close to the experimental values; the main factors sensitive to concrete compressive strength were exposure time, water-cement ratio and sulfate ions; the performance of the SVM model was better than the ANN model. The SVM model developed in this study can be potentially used for predicting the compressive strength of cement-based materials servicing in harsh environments.
[Mh] Termos MeSH primário: Força Compressiva
Materiais de Construção
Teste de Materiais
Sulfatos
[Mh] Termos MeSH secundário: Redes Neurais (Computação)
Máquina de Vetores de Suporte
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Sulfates)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180119
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0191370


  3 / 19825 MEDLINE  
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[PMID]:29273569
[Au] Autor:Zeinstra CG; Meuwly D; Ruifrok AC; Veldhuis RN; Spreeuwers LJ
[Ad] Endereço:Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands.
[Ti] Título:Forensic face recognition as a means to determine strength of evidence: A survey.
[So] Source:Forensic Sci Rev;30(1):21-32, 2018 Jan.
[Is] ISSN:1042-7201
[Cp] País de publicação:China (Republic : 1949- )
[La] Idioma:eng
[Ab] Resumo:This paper surveys the literature on forensic face recognition (FFR), with a particular focus on the strength of evidence as used in a court of law. FFR is the use of biometric face recognition for several applications in forensic science. It includes scenarios of ID verification and open-set identification, investigation and intelligence, and evaluation of the strength of evidence. We present FFR from operational, tactical, and strategic perspectives. We discuss criticism of FFR and we provide an overview of research efforts from multiple perspectives that relate to the domain of FFR. Finally, we sketch possible future directions for FFR.
[Mh] Termos MeSH primário: Identificação Biométrica
Face/anatomia & histologia
[Mh] Termos MeSH secundário: Conjuntos de Dados como Assunto
Prova Pericial
Ciências Forenses
Seres Humanos
Processamento de Imagem Assistida por Computador
Redes Neurais (Computação)
Competência Profissional
Pesquisa/tendências
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171224
[St] Status:MEDLINE


  4 / 19825 MEDLINE  
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[PMID]:29231143
[Au] Autor:Vazquez-Prieto S; Paniagua E; Solana H; Ubeira FM
[Ad] Endereço:Laboratorio de Biologia Celular y Molecular, Centro de Investigacion Veterinaria de Tandil (CIVETAN), CONICET, Facultad de Ciencias Veterinarias, UNCPBA, Tandil, Argentina.
[Ti] Título:Complex Network Study of the Immune Epitope Database for Parasitic Organisms.
[So] Source:Curr Top Med Chem;17(30):3249-3255, 2018 Feb 09.
[Is] ISSN:1873-4294
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Complex network approach allows the representation and analysis of complex systems of interacting agents in an ordered and effective manner, thus increasing the probability of discovering significant properties of them. In the present study, we defined and built for the first time a complex network based on data obtained from Immune Epitope Database for parasitic organisms. We then considered the general topology, the node degree distribution, and the local structure (triadic census) of this network. In addition, we calculated 9 node centrality measures for observed network and reported a comparative study of the real network with three theoretical models to detect similarities or deviations from these ideal networks. RESULT: The results obtained corroborate the utility of the complex network approach for handling information and data mining within the database under study. CONCLUSION: They confirm that this type of approach can be considered a valuable tool for preliminary screening of the best experimental conditions to determine whether the amino acid sequences being studied are true epitopes or not.
[Mh] Termos MeSH primário: Bases de Dados Factuais
Epitopos/química
Epitopos/imunologia
Redes Neurais (Computação)
Parasitos/química
Parasitos/imunologia
[Mh] Termos MeSH secundário: Sequência de Aminoácidos
Animais
Mineração de Dados
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Epitopes)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171213
[St] Status:MEDLINE
[do] DOI:10.2174/1568026618666171211150605


  5 / 19825 MEDLINE  
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[PMID]:29295994
[Au] Autor:Mardt A; Pasquali L; Wu H; Noé F
[Ad] Endereço:Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
[Ti] Título:VAMPnets for deep learning of molecular kinetics.
[So] Source:Nat Commun;9(1):5, 2018 01 02.
[Is] ISSN:2041-1723
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.
[Mh] Termos MeSH primário: Algoritmos
Aprendizado de Máquina
Cadeias de Markov
Simulação de Dinâmica Molecular
Redes Neurais (Computação)
[Mh] Termos MeSH secundário: Cinética
Ligação Proteica
Dobramento de Proteína
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180306
[Lr] Data última revisão:
180306
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180104
[St] Status:MEDLINE
[do] DOI:10.1038/s41467-017-02388-1


  6 / 19825 MEDLINE  
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[PMID]:29370248
[Au] Autor:Zheng M; Li L; Peng H; Xiao J; Yang Y; Zhang Y; Zhao H
[Ad] Endereço:School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
[Ti] Título:Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.
[So] Source:PLoS One;13(1):e0191473, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.
[Mh] Termos MeSH primário: Redes Neurais (Computação)
[Mh] Termos MeSH secundário: Simulação por Computador
Retroalimentação
Modelos Neurológicos
Dinâmica não Linear
Fatores de Tempo
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180302
[Lr] Data última revisão:
180302
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180126
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0191473


  7 / 19825 MEDLINE  
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[PMID]:28464514
[Au] Autor:Cintora P; Arikkath J; Kandel M; Popescu G; Best-Popescu C
[Ad] Endereço:Cellular Neuroscience and Imaging Laboratory, Department of Bioengineering, University of Illinois at Urbana-Champaign, 208 North Wright Street, Urbana, Illinois, 61801.
[Ti] Título:Cell density modulates intracellular mass transport in neural networks.
[So] Source:Cytometry A;91(5):503-509, 2017 May.
[Is] ISSN:1552-4930
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:In order to fully understand brain connectivity and elucidate the mechanisms involved in central nervous system disease, the field of neuroscience depends on quantitative studies of neuronal structure and function. Cell morphology and neurite (axonal and dendritic) arborization are typically studied by immunohistochemical and fluorescence techniques. However, dry mass content and intracellular mass transport rates have largely been under-investigated given the inherent difficulties in their measurement. Here, spatial light interference microscopy (SLIM) and dispersion-relation phase spectroscopy (DPS) were used to measure pathlength fluctuations that report on the dry mass and transport within cultured primary neurons across low, medium, and high cell density conditions. It was found that cell density (confluence) affects significantly both the growth rate and mass transport. The analysis method is label-free and does not require neuronal tracing, particle tracking, or neuron reconstruction. Since SLIM can upgrade any existing phase contrast microscope and the imaging and analysis are high-throughput, we anticipate that this approach will be embraced by neuroscientists for broad scale studies. © 2017 International Society for Advancement of Cytometry.
[Mh] Termos MeSH primário: Encéfalo/ultraestrutura
Contagem de Células/métodos
Microscopia de Interferência/métodos
Neurônios/ultraestrutura
[Mh] Termos MeSH secundário: Animais
Redes Neurais (Computação)
Neuritos/ultraestrutura
Análise Espectral/métodos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180302
[Lr] Data última revisão:
180302
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170503
[St] Status:MEDLINE
[do] DOI:10.1002/cyto.a.23111


  8 / 19825 MEDLINE  
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[PMID]:29352285
[Au] Autor:Han SS; Park GH; Lim W; Kim MS; Na JI; Park I; Chang SE
[Ad] Endereço:I Dermatology, Seoul, Korea.
[Ti] Título:Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.
[So] Source:PLoS One;13(1):e0191493, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.
[Mh] Termos MeSH primário: Diagnóstico por Computador
Redes Neurais (Computação)
Onicomicose/diagnóstico
[Mh] Termos MeSH secundário: Adulto
Idoso
Algoritmos
Área Sob a Curva
Inteligência Artificial
Bases de Dados Factuais
Dermatologistas
Feminino
Dermatoses do Pé/diagnóstico
Dermatoses do Pé/patologia
Dermatoses da Mão/diagnóstico
Dermatoses da Mão/patologia
Seres Humanos
Interpretação de Imagem Assistida por Computador
Aprendizado de Máquina
Masculino
Meia-Idade
Onicomicose/patologia
Adulto Jovem
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180226
[Lr] Data última revisão:
180226
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180121
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0191493


  9 / 19825 MEDLINE  
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[PMID]:29261334
[Au] Autor:Lv J; Yang M; Zhang J; Wang X
[Ad] Endereço:1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China.
[Ti] Título:Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study.
[So] Source:Br J Radiol;91(1083):20170788, 2018 Feb.
[Is] ISSN:1748-880X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:OBJECTIVE: Free-breathing abdomen imaging requires non-rigid motion registration of unavoidable respiratory motion in three-dimensional undersampled data sets. In this work, we introduce an image registration method based on the convolutional neural network (CNN) to obtain motion-free abdominal images throughout the respiratory cycle. METHODS: Abdominal data were acquired from 10 volunteers using a 1.5 T MRI system. The respiratory signal was extracted from the central-space spokes, and the acquired data were reordered in three bins according to the corresponding breathing signal. Retrospective image reconstruction of the three near-motion free respiratory phases was performed using non-Cartesian iterative SENSE reconstruction. Then, we trained a CNN to analyse the spatial transform among the different bins. This network could generate the displacement vector field and be applied to perform registration on unseen image pairs. To demonstrate the feasibility of this registration method, we compared the performance of three different registration approaches for accurate image fusion of three bins: non-motion corrected (NMC), local affine registration method (LREG) and CNN. RESULTS: Visualization of coronal images indicated that LREG had caused broken blood vessels, while the vessels of the CNN were sharper and more consecutive. As shown in the sagittal view, compared to NMC and CNN, distorted and blurred liver contours were caused by LREG. At the same time, zoom-in axial images presented that the vessels were delineated more clearly by CNN than LREG. The statistical results of the signal-to-noise ratio, visual score, vessel sharpness and registration time over all volunteers were compared among the NMC, LREG and CNN approaches. The SNR indicated that the CNN acquired the best image quality (207.42 ± 96.73), which was better than NMC (116.67 ± 44.70) and LREG (187.93 ± 96.68). The image visual score agreed with SNR, marking CNN (3.85 ± 0.12) as the best, followed by LREG (3.43 ± 0.13) and NMC (2.55 ± 0.09). A vessel sharpness assessment yielded similar values between the CNN (0.81 ± 0.03) and LREG (0.80 ± 0.04), differentiating them from the NMC (0.78 ± 0.06). When compared with the LREG-based reconstruction, the CNN-based reconstruction reduces the registration time from 1 h to 1 min. CONCLUSION: Our preliminary results demonstrate the feasibility of the CNN-based approach, and this scheme outperforms the NMC- and LREG-based methods. Advances in knowledge: This method reduces the registration time from ~1 h to ~1 min, which has promising prospects for clinical use. To the best of our knowledge, this study shows the first convolutional neural network-based registration method to be applied in abdominal images.
[Mh] Termos MeSH primário: Abdome/diagnóstico por imagem
Imagem Tridimensional/métodos
Imagem por Ressonância Magnética/métodos
Redes Neurais (Computação)
Técnicas de Imagem de Sincronização Respiratória/métodos
[Mh] Termos MeSH secundário: Adulto
Estudos de Viabilidade
Feminino
Voluntários Saudáveis
Seres Humanos
Aumento da Imagem/métodos
Interpretação de Imagem Assistida por Computador/métodos
Masculino
Movimento (Física)
Razão Sinal-Ruído
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180226
[Lr] Data última revisão:
180226
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:171221
[St] Status:MEDLINE
[do] DOI:10.1259/bjr.20170788


  10 / 19825 MEDLINE  
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[PMID]:29351281
[Au] Autor:Sheikhzadeh F; Ward RK; van Niekerk D; Guillaud M
[Ad] Endereço:Department of Electrical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
[Ti] Título:Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks.
[So] Source:PLoS One;13(1):e0190783, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:This paper addresses the problem of quantifying biomarkers in multi-stained tissues based on the color and spatial information of microscopy images of the tissue. A deep learning-based method that can automatically localize and quantify the regions expressing biomarker(s) in any selected area on a whole slide image is proposed. The deep learning network, which we refer to as Whole Image (WI)-Net, is a fully convolutional network whose input is the true RGB color image of a tissue and output is a map showing the locations of each biomarker. The WI-Net relies on a different network, Nuclei (N)-Net, which is a convolutional neural network that classifies each nucleus separately according to the biomarker(s) it expresses. In this study, images of immunohistochemistry (IHC)-stained slides were collected and used. Images of nuclei (4679 RGB images) were manually labeled based on the expressing biomarkers in each nucleus (as p16 positive, Ki-67 positive, p16 and Ki-67 positive, p16 and Ki-67 negative). The labeled nuclei images were used to train the N-Net (obtaining an accuracy of 92% in a test set). The trained N-Net was then extended to WI-Net that generated a map of all biomarkers in any selected sub-image of the whole slide image acquired by the scanner (instead of classifying every nucleus image). The results of our method compare well with the manual labeling by humans (average F-score of 0.96). In addition, we carried a layer-based immunohistochemical analysis of cervical epithelium, and showed that our method can be used by pathologists to differentiate between different grades of cervical intraepithelial neoplasia by quantitatively assessing the percentage of proliferating cells in the different layers of HPV positive lesions.
[Mh] Termos MeSH primário: Automação
Biomarcadores/metabolismo
Redes Neurais (Computação)
Neoplasias do Colo do Útero/metabolismo
[Mh] Termos MeSH secundário: Biópsia
Feminino
Seres Humanos
Imuno-Histoquímica
Neoplasias do Colo do Útero/patologia
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T; VALIDATION STUDIES
[Nm] Nome de substância:
0 (Biomarkers)
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180221
[Lr] Data última revisão:
180221
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180120
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0190783



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