1
|
Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [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] [Received: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
Collapse
Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | | |
Collapse
|
2
|
Tu W, Ling G, Liu F, Hu F, Song X. GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2945-2958. [PMID: 37037234 DOI: 10.1109/tcbb.2023.3266109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's "internal clock". To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.
Collapse
|
3
|
Alves CL, Toutain TGLDO, de Carvalho Aguiar P, Pineda AM, Roster K, Thielemann C, Porto JAM, Rodrigues FA. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. Sci Rep 2023; 13:8072. [PMID: 37202411 DOI: 10.1038/s41598-023-34650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
Collapse
Affiliation(s)
- Caroline L Alves
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany.
| | | | - Patricia de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Aruane M Pineda
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | | | | | - Francisco A Rodrigues
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| |
Collapse
|
4
|
Large-scale community detection based on core node and layer-by-layer label propagation. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
|
5
|
Wu Y, Wang X, Zhao W, Lv X. A Novel Topic Clustering Algorithm Based on Graph Neural Network for Question Topic Diversity. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
|
6
|
Chen J, Xiong H, Zheng H, Zhang D, Zhang J, Jia M, Liu Y. EGC2: Enhanced Graph Classification with Easy Graph Compression. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
7
|
Qiao Z, Wu Z, Chen J, Ren P, Yu Z. A Lightweight Method for Defense Graph Neural Networks Adversarial Attacks. ENTROPY (BASEL, SWITZERLAND) 2022; 25:39. [PMID: 36673179 PMCID: PMC9858433 DOI: 10.3390/e25010039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/27/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Graph neural network has been widely used in various fields in recent years. However, the appearance of an adversarial attack makes the reliability of the existing neural networks challenging in application. Premeditated attackers, can make very small perturbations to the data to fool the neural network to produce wrong results. These incorrect results can lead to disastrous consequences. So, how to defend against adversarial attacks has become an urgent research topic. Many researchers have tried to improve the model robustness directly or by using adversarial training to reduce the negative impact of an adversarial attack. However, the majority of the defense strategies currently in use are inextricably linked to the model-training process, which incurs significant running and memory space costs. We offer a lightweight and easy-to-implement approach that is based on graph transformation. Extensive experiments demonstrate that our approach has a similar defense effect (with accuracy rate returns of nearly 80%) as existing methods and only uses 10% of their run time when defending against adversarial attacks on GCN (graph convolutional neural networks).
Collapse
Affiliation(s)
- Zhi Qiao
- School of Computer Scinece, Shaanxi Normal University, Xi’an 710119, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
| | - Zhenqiang Wu
- School of Computer Scinece, Shaanxi Normal University, Xi’an 710119, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
| | - Jiawang Chen
- School of Computer Scinece, Shaanxi Normal University, Xi’an 710119, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
| | - Ping’an Ren
- School of Computer Scinece, Shaanxi Normal University, Xi’an 710119, China
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
| | - Zhiliang Yu
- School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China
| |
Collapse
|
8
|
Alves CL, Cury RG, Roster K, Pineda AM, Rodrigues FA, Thielemann C, Ciba M. Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. PLoS One 2022; 17:e0277257. [PMID: 36525422 PMCID: PMC9757568 DOI: 10.1371/journal.pone.0277257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/23/2022] [Indexed: 12/23/2022] Open
Abstract
Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.
Collapse
Affiliation(s)
- Caroline L. Alves
- BioMEMS Lab, Aschaffenburg University of Applied Sciences (UAS), Aschaffenburg, Germany
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
- * E-mail:
| | - Rubens Gisbert Cury
- Department of Neurology, Movement Disorders Center, University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Aruane M. Pineda
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Francisco A. Rodrigues
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Christiane Thielemann
- BioMEMS Lab, Aschaffenburg University of Applied Sciences (UAS), Aschaffenburg, Germany
| | - Manuel Ciba
- BioMEMS Lab, Aschaffenburg University of Applied Sciences (UAS), Aschaffenburg, Germany
| |
Collapse
|
9
|
Liang Y, Ju Y, Dong P, Zeng XJ, Martínez L, Dong J, Wang A. A sentiment analysis-based two-stage consensus model of large-scale group with core-periphery structure. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
|
10
|
|
11
|
Heterogeneous Question Answering Community Detection Based on Graph Neural Network. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
12
|
Melo RA, Ribeiro CC, Riveaux JA. The minimum quasi-clique partitioning problem: Complexity, formulations, and a computational study. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
13
|
Introduction to the Class of Prefractal Graphs. MATHEMATICS 2022. [DOI: 10.3390/math10142500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fractals are already firmly rooted in modern science. Research continues on the fractal properties of objects in physics, chemistry, biology and many other scientific fields. Fractal graphs as a discrete representation are used to model and describe the structure of various objects and processes, both natural and artificial. The paper proposes an introduction to prefractal graphs. The main definitions and notation are proposed—the concept of a seed, the operations of processing a seed, the procedure for generating a prefractal graph. Canonical (typical) and non-canonical (special) types of prefractal graphs are considered separately. Important characteristics are proposed and described—the preservation of adjacency of edges for different ranks in the trajectory. The definition of subgraph-seeds of different ranks is given separately. Rules for weighting a prefractal graph by natural numbers and intervals are proposed. Separately, the definition of a fractal graph as infinite is given, and the differences between the concepts of fractal and prefractal graphs are described. At the end of the work, already published works of the authors are proposed, indicating the main backlogs, as well as a list of directions for new research. This work is the beginning of a cycle of works on the study of the properties and characteristics of fractal and prefractal graphs.
Collapse
|
14
|
A review of clique-based overlapping community detection algorithms. Knowl Inf Syst 2022. [DOI: 10.1007/s10115-022-01704-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
15
|
|
16
|
|
17
|
Ghouchan Nezhad Noor Nia R, Jalali M, Mail M, Ivanisenko Y, Kübel C. Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network. ACS OMEGA 2022; 7:12978-12992. [PMID: 35474778 PMCID: PMC9026177 DOI: 10.1021/acsomega.2c00317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 03/07/2022] [Indexed: 05/09/2023]
Abstract
There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.
Collapse
Affiliation(s)
| | - Mehrdad Jalali
- Department
of Computer Engineering, Mashhad Branch,
Islamic Azad University, Mashhad, Iran
- Institute
of Functional Interfaces (IFG), Karlsruhe
Institute of Technology (KIT), Hermann-von Helmholtz-4 Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Matthias Mail
- Institute
of Nanotechnology (INT), Karlsruhe Institute
of Technology (KIT), Hermann-von Helmholtz-4 Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe
Nano Micro Facility (KNMF), Hermann-von Helmholtz-4 Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Yulia Ivanisenko
- Institute
of Nanotechnology (INT), Karlsruhe Institute
of Technology (KIT), Hermann-von Helmholtz-4 Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Christian Kübel
- Institute
of Nanotechnology (INT), Karlsruhe Institute
of Technology (KIT), Hermann-von Helmholtz-4 Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe
Nano Micro Facility (KNMF), Hermann-von Helmholtz-4 Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Department
of Materials & Geological Sciences, Technical University Darmstadt, Alarich-Weiss-Strasse 2, 64287 Darmstadt, Germany
| |
Collapse
|
18
|
Xianduo S, Xin W, Yuyuan S, Xianglin Z, Ying W. Hierarchical recurrent neural networks for graph generation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
19
|
Abstract
Even among single-criteria discrete problems, there are NP-hard ones. Multicriteria problems on graphs in many cases become intractable. Currently, priority is given to the study of applied multicriteria problems with specific criteria; there is no classification of criteria according to their type and content. There are few studies with fuzzy criteria, both weight and topological. Little attention is paid to the stability of solutions, and this is necessary when modeling real processes due to their dynamism. It is also necessary to study the behavior of solution sets for various general and individual problems. The theory of multicriteria optimization is a rather young branch of science and requires the development of not only particular methods, but also the construction of a methodological basis. This is also true in terms of discrete graph-theoretic optimization. In this paper, we propose to get acquainted with multicriteria problems for a special class of prefractal graphs. Modeling natural objects or processes using graphs often involves weighting edges with many numbers. The author proposes a general formulation of a multicriteria problem on a multi-weighted prefractal graph; defines three sets of alternatives—Pareto, complete and lexicographic; and proposes a classification of individual problems according to the set of feasible solutions. As an example, we consider an individual problem of placing a multiple center with two types of weight criteria and two types of topological ones. An algorithm with estimates of all criteria of the problem is proposed.
Collapse
|
20
|
Stable structural clustering in uncertain graphs. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
21
|
|
22
|
Mining social applications network from business perspective using modularity maximization for community detection. SOCIAL NETWORK ANALYSIS AND MINING 2021. [DOI: 10.1007/s13278-021-00798-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
23
|
Abstract
NP-complete problems in graphs, such as enumeration and the selection of subgraphs with given characteristics, become especially relevant for large graphs and networks. Herein, particular statements with constraints are proposed to solve such problems, and subclasses of graphs are distinguished. We propose a class of prefractal graphs and review particular statements of NP-complete problems. As an example, algorithms for searching for spanning trees and packing bipartite graphs are proposed. The developed algorithms are polynomial and based on well-known algorithms and are used in the form of procedures. We propose to use the class of prefractal graphs as a tool for studying NP-complete problems and identifying conditions for their solvability. Using prefractal graphs for the modeling of large graphs and networks, it is possible to obtain approximate solutions, and some exact solutions, for problems on natural objects—social networks, transport networks, etc.
Collapse
|
24
|
Su Y, Zhou K, Zhang X, Cheng R, Zheng C. A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.089] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
25
|
Enriching networks with edge insertion to improve community detection. SOCIAL NETWORK ANALYSIS AND MINING 2021. [DOI: 10.1007/s13278-021-00803-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|