1
|
Samadi Z, Askary A. Spatial motifs reveal patterns in cellular architecture of complex tissues. bioRxiv 2024:2024.04.08.588586. [PMID: 38645046 PMCID: PMC11030378 DOI: 10.1101/2024.04.08.588586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Spatial organization of cells is crucial to both proper physiological function of tissues and pathological conditions like cancer. Recent advances in spatial transcriptomics have enabled joint profiling of gene expression and spatial context of the cells. The outcome is an information rich map of the tissue where individual cells, or small regions, can be labeled based on their gene expression state. While spatial transcriptomics excels in its capacity to profile numerous genes within the same sample, most existing methods for analysis of spatial data only examine distribution of one or two labels at a time. These approaches overlook the potential for identifying higher-order associations between cell types - associations that can play a pivotal role in understanding development and function of complex tissues. In this context, we introduce a novel method for detecting motifs in spatial neighborhood graphs. Each motif represents a spatial arrangement of cell types that occurs in the tissue more frequently than expected by chance. To identify spatial motifs, we developed an algorithm for uniform sampling of paths from neighborhood graphs and combined it with a motif finding algorithm on graphs inspired by previous methods for finding motifs in DNA sequences. Using synthetic data with known ground truth, we show that our method can identify spatial motifs with high accuracy and sensitivity. Applied to spatial maps of mouse retinal bipolar cells and hypothalamic preoptic region, our method reveals previously unrecognized patterns in cell type arrangements. In some cases, cells within these spatial patterns differ in their gene expression from other cells of the same type, providing insights into the functional significance of the spatial motifs. These results suggest that our method can illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses.
Collapse
Affiliation(s)
- Zainalabedin Samadi
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
| |
Collapse
|
2
|
Wang C, Xiao Z, Xu Y, Zhang Q, Chen J. A novel approach for ASD recognition based on graph attention networks. Front Comput Neurosci 2024; 18:1388083. [PMID: 38659616 PMCID: PMC11039788 DOI: 10.3389/fncom.2024.1388083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects' fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the BOLD signal in each ROI, extract wavelet features, and utilize them along with the mean and variance of the BOLD signal as node features, and the optimized FC matrix as the adjacency matrix, respectively. We then employ the self-attention mechanism to capture long-range dependencies among features. To enhance interpretability, the node-selection pooling layers are designed to determine the importance of ROI for prediction. The proposed framework are applied to fMRI data of children (younger than 12 years old) from the Autism Brain Imaging Data Exchange datasets. Promising results demonstrate superior performance compared to recent similar studies. The obtained ROI detection results exhibit high correspondence with previous studies and offer good interpretability.
Collapse
Affiliation(s)
- Canhua Wang
- School of Computer, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Zhiyong Xiao
- School of Electronic & Information Engineering, Jiangxi Institute of Economic Administrators, Nanchang, China
| | - Yilu Xu
- School of Software, Jiangxi Agricultural University, Nanchang, China
| | - Qi Zhang
- Department of Medical Imaging, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, China
| | - Jingfang Chen
- Department of Medical Imaging, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| |
Collapse
|
3
|
Ma P, Dong C, Lin R, Liu H, Lei D, Chen X, Liu H. A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks. Front Neurosci 2024; 18:1306283. [PMID: 38586195 PMCID: PMC10996401 DOI: 10.3389/fnins.2024.1306283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Background The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. Objective This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. Methods The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. Results For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. Conclusion The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.
Collapse
Affiliation(s)
- Pengfei Ma
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
| | - Chaoyi Dong
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Ruijing Lin
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Huanzi Liu
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Dongyang Lei
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Xiaoyan Chen
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Huan Liu
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
| |
Collapse
|
4
|
Liu J, Zhang Y, Jia F, Zhang H, Luo L, Liao Y, Ouyang M, Yi X, Zhu R, Bai W, Ning G, Li X, Qu H. Sex differences in fetal brain functional network topology. Cereb Cortex 2024; 34:bhae111. [PMID: 38517172 DOI: 10.1093/cercor/bhae111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/23/2024] Open
Abstract
The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.
Collapse
Affiliation(s)
- Jing Liu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Yujin Zhang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Hongding Zhang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Lekai Luo
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Minglei Ouyang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Xiaoxue Yi
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Ruixi Zhu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Wanjing Bai
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| |
Collapse
|
5
|
Wang Y. Algorithms for the Uniqueness of the Longest Common Subsequence. J Bioinform Comput Biol 2023; 21:2350027. [PMID: 38212873 DOI: 10.1142/s0219720023500270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are multiple longest common subsequences, we also determine which number appears in all/some/none of the longest common subsequences. We focus on four scenarios: (1) linear sequences without duplicated numbers; (2) circular sequences without duplicated numbers; (3) linear sequences with duplicated numbers; (4) circular sequences with duplicated numbers. We develop corresponding algorithms and apply them to gene sequencing data.
Collapse
Affiliation(s)
- Yue Wang
- Department of Computational Medicine, University of California, Los Angeles, California, USA
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, New York, USA
| |
Collapse
|
6
|
Zhang L, Chen Z, Lu CT, Zhao L. Fast and adaptive dynamics-on- graphs to dynamics-of-graphs translation. Front Big Data 2023; 6:1274135. [PMID: 38045094 PMCID: PMC10691542 DOI: 10.3389/fdata.2023.1274135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/20/2023] [Indexed: 12/05/2023] Open
Abstract
Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the "dynamics on graphs" (e.g., changing node attribute values) are visible, and they may be connected to and suggestive of the "dynamics of graphs" (e.g., evolution of the graph topology). Due to two fundamental obstacles, modeling and mapping between them have not been thoroughly explored: (1) the difficulty of developing a highly adaptable model without solid hypotheses and (2) the ineffectiveness and slowness of processing data with varying granularity. To solve these issues, we offer a novel scalable deep echo-state graph dynamics encoder for networks with significant temporal duration and dimensions. A novel neural architecture search (NAS) technique is then proposed and tailored for the deep echo-state encoder to ensure strong learnability. Extensive experiments on synthetic and actual application data illustrate the proposed method's exceptional effectiveness and efficiency.
Collapse
Affiliation(s)
- Lei Zhang
- Department of Computer Science, Virginia Tech, Falls Church, VA, United States
| | - Zhiqian Chen
- Department of Computer Science and Engineering, Mississippi State University, Mississippi, MS, United States
| | - Chang-Tien Lu
- Department of Computer Science, Virginia Tech, Falls Church, VA, United States
| | - Liang Zhao
- Department of Computer Science, Emory University, Atlanta, GA, United States
| |
Collapse
|
7
|
Fogg J, Allman ES, Ané C. PhyloCoalSimulations: A Simulator for Network Multispecies Coalescent Models, Including a New Extension for the Inheritance of Gene Flow. Syst Biol 2023; 72:1171-1179. [PMID: 37254872 DOI: 10.1093/sysbio/syad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/03/2023] [Accepted: 05/15/2023] [Indexed: 06/01/2023] Open
Abstract
We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example. We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at https://github.com/cecileane/PhyloCoalSimulations.jl.
Collapse
Affiliation(s)
- John Fogg
- Department of Statistics, University of Wisconsin - Madison, WI, 53706, USA
| | - Elizabeth S Allman
- Department of Mathematics and Statistics, University of Alaska - Fairbanks, AK, 99775, USA
| | - Cécile Ané
- Department of Statistics, University of Wisconsin - Madison, WI, 53706, USA
- Department of Botany, University of Wisconsin - Madison, WI, 53706, USA
| |
Collapse
|
8
|
Kowalski TW, Feira MF, Lord VO, Gomes JDA, Giudicelli GC, Fraga LR, Sanseverino MTV, Recamonde-Mendoza M, Schuler-Faccini L, Vianna FSL. A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors. Int J Mol Sci 2023; 24:11515. [PMID: 37511270 PMCID: PMC10380514 DOI: 10.3390/ijms241411515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.
Collapse
Affiliation(s)
- Thayne Woycinck Kowalski
- Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
- Teratogen Information System (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Laboratory of Genomic Medicine, Center of Experimental Research, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Biomedical Sciences Course, Centro Universitário CESUCA, Cachoeirinha 94935-630, Brazil
| | - Mariléa Furtado Feira
- Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
- Laboratory of Genomic Medicine, Center of Experimental Research, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
| | - Vinícius Oliveira Lord
- Laboratory of Genomic Medicine, Center of Experimental Research, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Biomedical Sciences Course, Centro Universitário CESUCA, Cachoeirinha 94935-630, Brazil
| | - Julia do Amaral Gomes
- Laboratory of Genomic Medicine, Center of Experimental Research, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
| | - Giovanna Câmara Giudicelli
- Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
- Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
| | - Lucas Rosa Fraga
- Teratogen Information System (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Laboratory of Genomic Medicine, Center of Experimental Research, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Post-Graduation Program in Medicine, Medical Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
- Department of Morphological Sciences, Institute of Health Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90010-150, Brazil
| | - Maria Teresa Vieira Sanseverino
- Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
- Teratogen Information System (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre 90619-900, Brazil
| | - Mariana Recamonde-Mendoza
- Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Post-Graduation Program in Computer Science, Institute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
| | - Lavinia Schuler-Faccini
- Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
- Teratogen Information System (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
| | - Fernanda Sales Luiz Vianna
- Graduate Program in Genetics and Molecular Biology, Genetics Department, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
- Teratogen Information System (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Laboratory of Genomic Medicine, Center of Experimental Research, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-903, Brazil
- Post-Graduation Program in Medicine, Medical Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
| |
Collapse
|
9
|
Roy S, Guzzi PH, Kalita J. Editorial: Graph representation learning in biological network. Front Bioinform 2023; 3:1222711. [PMID: 37359069 PMCID: PMC10289182 DOI: 10.3389/fbinf.2023.1222711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023] Open
Affiliation(s)
- Swarup Roy
- Network Reconstruction & Analysis (NETRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Data Analytics Research Centre, Magna Graecia University, Catanzaro, Italy
| | - Jugal Kalita
- Department of Science, University of Colorado, Colorado Springs, CO, United States
| |
Collapse
|
10
|
Jiang J, Goebel M, Borba C, Smith W, Manjunath B. 3D Neuron Morphology Analysis. Res Sq 2023:rs.3.rs-2698751. [PMID: 37215037 PMCID: PMC10197748 DOI: 10.21203/rs.3.rs-2698751/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing"curve"skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.
Collapse
Affiliation(s)
- Jiaxiang Jiang
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, US
| | - Michael Goebel
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, US
| | - Cezar Borba
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Barbara, US
| | - William Smith
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Barbara, US
| | - B.S. Manjunath
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, US
| |
Collapse
|
11
|
Kumar N, Mukhtar MS. Ranking Plant Network Nodes Based on Their Centrality Measures. Entropy (Basel) 2023; 25:e25040676. [PMID: 37190464 PMCID: PMC10137616 DOI: 10.3390/e25040676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 05/17/2023]
Abstract
Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein-protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant-pathogen interactions and related functions and pathways compared to the other algorithms.
Collapse
Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| |
Collapse
|
12
|
Nielson FF, Kay B, Young SJ, Colby SM, Renslow RS, Metz TO. Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics. Metabolites 2023; 13:105. [PMID: 36677030 PMCID: PMC9864474 DOI: 10.3390/metabo13010105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/14/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called "reference-free metabolomics" methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3-7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.
Collapse
Affiliation(s)
- Felicity F. Nielson
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| | - Bill Kay
- Pacific Northwest National Laboratory, Advanced Computing, Mathematics, and Data Division, Richland, WA 99354, USA
| | - Stephen J. Young
- Pacific Northwest National Laboratory, Advanced Computing, Mathematics, and Data Division, Richland, WA 99354, USA
| | - Sean M. Colby
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| | - Ryan S. Renslow
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| | - Thomas O. Metz
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| |
Collapse
|
13
|
Ivanov A, Tonchev K, Poulkov V, Manolova A, Neshov NN. Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications. Sensors (Basel) 2022; 22:s22155778. [PMID: 35957333 PMCID: PMC9371046 DOI: 10.3390/s22155778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/01/2022] [Accepted: 07/26/2022] [Indexed: 05/14/2023]
Abstract
Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms.
Collapse
|
14
|
Luo J, Zhang Y, Song Y. Design for Pandemic Information: Examining the Effect of Graphs on Anxiety and Social Distancing Intentions in the COVID-19. Front Public Health 2022; 10:800789. [PMID: 35664092 PMCID: PMC9158495 DOI: 10.3389/fpubh.2022.800789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 03/03/2022] [Indexed: 01/02/2023] Open
Abstract
To increase public awareness and disseminate health information, the WHO and health departments worldwide have been visualizing the latest statistics on the spread of COVID-19 to increase awareness and thus reduce its spread. Within various sources, graphs are frequently used to illustrate COVID-19 datasets. Limited research has provided insights into the effect of different graphs on emotional stress and ineffective behavioral strategies from a cross-cultural perspective. The result of current research suggests a graph with a high proportion size of the colored area (e.g., stacked area graph) might increase people's anxiety and social distancing intentions; people in collectivist culture might have a high level of anxiety and social distancing intentions; the effect of different graphs on social distancing intentions is mediated by anxiety experienced. Theoretical contribution and practical implications on health communication were also discussed in this study.
Collapse
Affiliation(s)
- Jing Luo
- Department of Industrial Design, College of Art and Design, Shenzhen University, Shenzhen, China
| | - Yaqi Zhang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yao Song
- Department of Advertising, College of Literature and Journalism, Sichuan University, Chengdu, China.,Digital Convergence Laboratory of Chinese Cultural Inheritance and Global Communication, Sichuan University, Chengdu, China
| |
Collapse
|
15
|
Cheng L, Senathirajah Y. Testing Medical Student Diagnostic Reasoning Using Clinical Data Visualizations. Stud Health Technol Inform 2022; 294:819-820. [PMID: 35612216 DOI: 10.3233/shti220596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This experiment aimed to (1) induce System-1-type diagnostic reasoning in medical students through the acquisition of cognitive user interface (UI) heuristics and (2) understand qualitatively how clinical data visualizations could enhance medical education. Third- and fourth-year medical students were presented patient cases through a novel electronic health record (EHR) design then asked to diagnose patients after being shown the cases either briefly and repeatedly (Group A) or twice over a longer period (Group B). Group A had higher accuracy than Group B. Findings support the possibility of inducing System-1 reasoning via UI heuristics and potential of integrating data visualizations in medical education.
Collapse
|
16
|
Yao B, Ma A, Feng R, Shen X, Zhang M, Yao Y. A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System. Front Public Health 2022; 9:804298. [PMID: 35155353 PMCID: PMC8825479 DOI: 10.3389/fpubh.2021.804298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 12/10/2021] [Indexed: 11/13/2022] Open
Abstract
As urban traffic pollution continues to increase, there is an urgent need to build traffic emission monitoring and forecasting system for the urban traffic construction. The traffic emission monitoring and forecasting system's core is the prediction of traffic emission's evolution. And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution. Due to the complex non-Euclidean topological structure of traffic networks and dynamic heterogeneous spatial-temporal correlations of traffic conditions, it is difficult to obtain satisfactory prediction results with less computation cost. To figure these issues out, a novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks (EAGTCN). More specifically, each component of our model contains two major blocks: (1) the global spatial patterns are captured by the spatial blocks which are fused by the Graph Convolution Network (GCN) and spatial ensemble attention layer; (2) the temporal patterns are captured by the temporal blocks which are composed by the Time Convolution Net (TCN) and temporal ensemble attention layers. Experiments on two real-world datasets demonstrate that our model obtains more accurate prediction results than the state-of-the-art baselines at less computation expense especially in the long-term prediction situation.
Collapse
Affiliation(s)
- Baozhen Yao
- State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian, China
| | - Ankun Ma
- State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian, China
| | - Rui Feng
- State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian, China
| | | | - Mingheng Zhang
- State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian, China
| | - Yansheng Yao
- School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, China
| |
Collapse
|
17
|
Chai Y, Liu M, Duffy BA, Kim H. LEARNING TO SYNTHESIZE CORTICAL MORPHOLOGICAL CHANGES USING GRAPH CONDITIONAL VARIATIONAL AUTOENCODER. Proc IEEE Int Symp Biomed Imaging 2022; 2021:1495-1499. [PMID: 35330877 DOI: 10.1109/isbi48211.2021.9433837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Changes in brain morphology, such as cortical thinning are of great value for understanding the trajectory of brain aging and various neurodegenerative diseases. In this work, we employed a generative neural network variational autoencoder (VAE) that is conditional on age and is able to generate cortical thickness maps at various ages given an input cortical thickness map. To take into account the mesh topology in the model, we proposed a loss function based on weighted adjacency to integrate the surface topography defined as edge connections with the cortical thickness mapped as vertices. Compared to traditional conditional VAE that did not use the surface topological information, our method better predicted "future" cortical thickness maps, especially when the age gap became wider. Our model has the potential to predict the distinctive temporospatial pattern of individual cortical morphology in relation to aging and neurodegenerative diseases.
Collapse
Affiliation(s)
- Yaqiong Chai
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Mengting Liu
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Ben A Duffy
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Hosung Kim
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
18
|
Sultana T, Lee YK. gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair. Sensors (Basel) 2022; 22:s22072545. [PMID: 35408160 PMCID: PMC9003471 DOI: 10.3390/s22072545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 12/04/2022]
Abstract
The explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions. However, these compressors rarely exploit the graph patterns and structural regularities of real-world datasets. Moreover, there are a variety of existing approaches that reduce the size of a graph by using a grammar-based graph compression algorithm. In this study, we introduce a novel approach named gRDF (graph repair for RDF) that uses gRePair, one of the most efficient grammar-based graph compression schemes, to compress the RDF dataset. In addition to that, we have improved the performance of HDT (header-dictionary-triple), an efficient approach for compressing the RDF datasets based on structural properties, by introducing modified HDT (M-HDT). It can detect the frequent graph pattern by employing the data-structure-oriented approach in a single pass from the dataset. In our proposed system, we use M-HDT for indexing the nodes and edge labels. Then, we employ gRePair algorithm for identifying the grammar from the RDF graph. Afterward, the system improves the performance of k2-trees by introducing a more efficient algorithm to create the trees and serialize the RDF datasets. Our experiments affirm that the proposed gRDF scheme can substantially achieve at approximately 26.12%, 13.68%, 6.81%, 2.38%, and 12.76% better compression ratio when compared with the most prominent state-of-the-art schemes such as HDT, HDT++, k2-trees, RDF-TR, and gRePair in the case of real-world datasets. Moreover, the processing efficiency of our proposed scheme also outperforms others.
Collapse
|
19
|
Bazaluk O, Struchaiev N, Halko S, Miroshnyk O, Bondarenko L, Karaiev O, Nitsenko V. Ways to Improve the Efficiency of Devices for Freezing of Small Products. Materials (Basel) 2022; 15:ma15072412. [PMID: 35407745 PMCID: PMC9000166 DOI: 10.3390/ma15072412] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/17/2022] [Accepted: 03/23/2022] [Indexed: 11/26/2022]
Abstract
It has been established that one of the main problems in the technology of the production of loose food products is the sticking of vegetables or fruits into one block. It has been proven that one of the steps to solve this problem is the use of berries, fruits, or vegetables during freezing in the form of a fluidized bed in air. However, a significant part of the energy is spent precisely when creating a fluidized bed with the help of fans. By improving the separation efficiency of small products in the freezing process, it would be possible to significantly reduce the energy costs of freezing worldwide. The purpose of this work was to determine ways to increase the efficiency of devices for freezing small products. The goal was achieved through the use of a modified method for studying energy costs, taking into account energy costs for fluidization and mechanical shaking. For comparison, two options for the efficient separation of small products during freezing were considered. Namely the separation of small products in the process of freezing with the help of fluidization, and with the help of mechanical shaking. Comparison of these variants showed that it was advisable to separate small products during freezing by mechanical shaking. It was established that their energy parameters, as well as fractional properties, are significantly different. The product temperature was determined for the case of a constant temperature of the cooling air and equipment elements. The results obtained confirmed the possibility of achieving significant energy savings of 1.5–3.5 times by using the mechanized device we proposed for freezing fruits and vegetables. The main result of this paper is the proposed method, or algorithm, for calculating energy costs for fluidization and mechanical shaking, which could be used in the design of devices for the freezing of small products; as well as the obtained data confirming the correspondence of the theoretical calculations to reality. The novelty of the research consists in presenting a model or algorithm for calculating the energy costs for fluidization and mechanical shaking. The importance of the results of the work lies in the possibility of using this technique to assess the energy effectiveness of devices for the freezing of small products.
Collapse
Affiliation(s)
- Oleg Bazaluk
- Belt and Road Initiative Institute for Chinese-European Studies, Guangdong University of Petrochemical Technology, Maoming 525000, China;
| | - Nikolai Struchaiev
- Department of Information Technologies of Design, Dmytro Motornyi Tavria State Agrotechnological University, 18 B.Khmelnytsky Ave, 72310 Melitopol, Ukraine; (N.S.); (S.H.); (L.B.); (O.K.)
| | - Serhii Halko
- Department of Information Technologies of Design, Dmytro Motornyi Tavria State Agrotechnological University, 18 B.Khmelnytsky Ave, 72310 Melitopol, Ukraine; (N.S.); (S.H.); (L.B.); (O.K.)
| | - Oleksandr Miroshnyk
- Department of Electricity and Energy Management, State Biotechnological University, Str. Rizdviana, 19, 62341 Kharkiv, Ukraine;
| | - Larysa Bondarenko
- Department of Information Technologies of Design, Dmytro Motornyi Tavria State Agrotechnological University, 18 B.Khmelnytsky Ave, 72310 Melitopol, Ukraine; (N.S.); (S.H.); (L.B.); (O.K.)
| | - Oleksandr Karaiev
- Department of Information Technologies of Design, Dmytro Motornyi Tavria State Agrotechnological University, 18 B.Khmelnytsky Ave, 72310 Melitopol, Ukraine; (N.S.); (S.H.); (L.B.); (O.K.)
| | - Vitalii Nitsenko
- SCIRE Foundation, 00867 Warsaw, Poland
- Correspondence: ; Tel.: +380-939983073
| |
Collapse
|
20
|
Mehraram R, Peraza LR, Murphy NRE, Cromarty RA, Graziadio S, O'Brien JT, Killen A, Colloby SJ, Firbank M, Su L, Collerton D, Taylor JP, Kaiser M. Functional and structural brain network correlates of visual hallucinations in Lewy body dementia. Brain 2022; 145:2190-2205. [PMID: 35262667 PMCID: PMC9246710 DOI: 10.1093/brain/awac094] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 02/15/2022] [Accepted: 02/20/2022] [Indexed: 12/02/2022] Open
Abstract
Visual hallucinations are a common feature of Lewy body dementia. Previous studies have shown that visual hallucinations are highly specific in differentiating Lewy body dementia from Alzheimer’s disease dementia and Alzheimer–Lewy body mixed pathology cases. Computational models propose that impairment of visual and attentional networks is aetiologically key to the manifestation of visual hallucinations symptomatology. However, there is still a lack of experimental evidence on functional and structural brain network abnormalities associated with visual hallucinations in Lewy body dementia. We used EEG source localization and network based statistics to assess differential topographical patterns in Lewy body dementia between 25 participants with visual hallucinations and 17 participants without hallucinations. Diffusion tensor imaging was used to assess structural connectivity between thalamus, basal forebrain and cortical regions belonging to the functionally affected network component in the hallucinating group, as assessed with network based statistics. The number of white matter streamlines within the cortex and between subcortical and cortical regions was compared between hallucinating and not hallucinating groups and correlated with average EEG source connectivity of the affected subnetwork. Moreover, modular organization of the EEG source network was obtained, compared between groups and tested for correlation with structural connectivity. Network analysis showed that compared to non-hallucinating patients, those with hallucinations feature consistent weakened connectivity within the visual ventral network, and between this network and default mode and ventral attentional networks, but not between or within attentional networks. The occipital lobe was the most functionally disconnected region. Structural analysis yielded significantly affected white matter streamlines connecting the cortical regions to the nucleus basalis of Meynert and the thalamus in hallucinating compared to not hallucinating patients. The number of streamlines in the tract between the basal forebrain and the cortex correlated with cortical functional connectivity in non-hallucinating patients, while a correlation emerged for the white matter streamlines connecting the functionally affected cortical regions in the hallucinating group. This study proposes, for the first time, differential functional networks between hallucinating and not hallucinating Lewy body dementia patients, and provides empirical evidence for existing models of visual hallucinations. Specifically, the outcome of the present study shows that the hallucinating condition is associated with functional network segregation in Lewy body dementia and supports the involvement of the cholinergic system as proposed in the current literature.
Collapse
Affiliation(s)
- Ramtin Mehraram
- Experimental Oto-rhino-laryngology (ExpORL) Research Group, Department of Neurosciences, KU Leuven, Leuven, Belgium.,NIHR Newcastle Biomedical Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.,Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.,Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | | | - Nicholas R E Murphy
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX 77030, USA.,The Menninger Clinic, Houston, TX, 77035, USA.,Michael E. DeBakey VA Medical Center, 2002 Holcombe Boulevard, Houston, TX 77030, USA
| | - Ruth A Cromarty
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Sara Graziadio
- NIHR Newcastle in vitro Diagnostics Cooperative, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, UK
| | - Alison Killen
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Sean J Colloby
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Michael Firbank
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Li Su
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, UK.,Department of Neuroscience, The University of Sheffield, Sheffield, UK
| | - Daniel Collerton
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, UK.,NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
21
|
Shin SY, Lee S, Summers RM. GRAPH-BASED SMALL BOWEL PATH TRACKING WITH CYLINDRICAL CONSTRAINTS. Proc IEEE Int Symp Biomed Imaging 2022; 2022:10.1109/isbi52829.2022.9761423. [PMID: 37124457 PMCID: PMC10134031 DOI: 10.1109/isbi52829.2022.9761423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the contact between parts of itself along its course, which makes the path tracking difficult together with the indistinct appearance of the wall. It causes the tracked path to easily cross over the walls when relying on low-level features like the wall detection. To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions. It is implemented as soft constraints using a new cost function. The proposed method is evaluated against ground-truth paths that are all connected from start to end of the small bowel for 10 abdominal CT scans. The proposed method showed clear improvements compared to the baseline method in tracking the path without making an error. Improvements of 6.6% and 17.0%, in terms of the tracked length, were observed for two different settings related to the small bowel segmentation.
Collapse
Affiliation(s)
- Seung Yeon Shin
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory Radiology and Imaging Sciences, National Institutes of Health Clinical Center, USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory Radiology and Imaging Sciences, National Institutes of Health Clinical Center, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory Radiology and Imaging Sciences, National Institutes of Health Clinical Center, USA
| |
Collapse
|
22
|
Chi Y, Guo L, Cong J. Accelerating SSSP for Power-Law Graphs. FPGA 2022; 2022:190-200. [PMID: 35300320 PMCID: PMC8926441 DOI: 10.1145/3490422.3502358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The single-source shortest path (SSSP) problem is one of the most important and well-studied graph problems widely used in many application domains, such as road navigation, neural image reconstruction, and social network analysis. Although we have known various SSSP algorithms for decades, implementing one for large-scale power-law graphs efficiently is still highly challenging today, because ① a work-efficient SSSP algorithm requires priority-order traversal of graph data, ② the priority queue needs to be scalable both in throughput and capacity, and ③ priority-order traversal requires extensive random memory accesses on graph data. In this paper, we present SPLAG to accelerate SSSP for power-law graphs on FPGAs. SPLAG uses a coarse-grained priority queue (CGPQ) to enable high-throughput priority-order graph traversal with a large frontier. To mitigate the high-volume random accesses, SPLAG employs a customized vertex cache (CVC) to reduce off-chip memory access and improve the throughput to read and update vertex data. Experimental results on various synthetic and real-world datasets show up to a 4.9× speedup over state-of-the-art SSSP accelerators, a 2.6× speedup over 32-thread CPU running at 4.4 GHz, and a 0.9× speedup over an A100 GPU that has 4.1× power budget and 3.4× HBM bandwidth. Such a high performance would place SPLAG in the 14th position of the Graph 500 benchmark for data intensive applications (the highest using a single FPGA) with only a 45 W power budget. SPLAG is written in high-level synthesis C++ and is fully parameterized, which means it can be easily ported to various different FPGAs with different configurations. SPLAG is open-source at https://github.com/UCLA-VAST/splag.
Collapse
Affiliation(s)
- Yuze Chi
- University of California, Los Angeles
| | | | | |
Collapse
|
23
|
Lee CH, Bae JW, Paik E. GO-DEVS: Storage and Retrieval System for DEVS Models Using Graph and Ontology Representation. Sensors (Basel) 2021; 21:s21206771. [PMID: 34695984 PMCID: PMC8541595 DOI: 10.3390/s21206771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 09/29/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022]
Abstract
DEVS is a powerful formal language to describe discrete event systems in modeling and simulation areas and useful for component-based design. One of the advantages of component-based design is reusability. To reuse or share DEVS models developed by many other modelers, a system to systematically store and retrieve many DEVS models should be supported. However, to the best of our knowledge, there does not exist such a system. In this paper, we propose GO-DEVS (Graph/Ontology-represented DEVS storage and retrieval system) to store and retrieve DEVS models using graph and ontology representation. For effective model sharing, an ontology is introduced when a DEVS model is developed. To search for DEVS models in an effective and efficient way, we propose two types of queries, IO query and structure query, and provide a method to store and query DEVS models on an RDBMS. Finally, we experimentally show GO-DEVS can process the queries efficiently.
Collapse
Affiliation(s)
- Chun-Hee Lee
- Intelligence Information Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (C.-H.L.); (E.P.)
| | - Jang Won Bae
- School of Industrial Management, Korea University of Technology and Education, Cheonan 31253, Korea
- Correspondence:
| | - Euihyun Paik
- Intelligence Information Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; (C.-H.L.); (E.P.)
| |
Collapse
|
24
|
Hu L, Zhang M, Li S, Shi J, Shi C, Yang C, Liu Z. Text- Graph Enhanced Knowledge Graph Representation Learning. Front Artif Intell 2021; 4:697856. [PMID: 34490421 PMCID: PMC8418144 DOI: 10.3389/frai.2021.697856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/02/2021] [Indexed: 11/13/2022] Open
Abstract
Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space. Conventional KG embedding methods (such as TransE and ConvE) utilize only KG triplets and thus suffer from structure sparsity. Some recent works address this issue by incorporating auxiliary texts of entities, typically entity descriptions. However, these methods usually focus only on local consecutive word sequences, but seldom explicitly use global word co-occurrence information in a corpus. In this paper, we propose to model the whole auxiliary text corpus with a graph and present an end-to-end text-graph enhanced KG embedding model, named Teger. Specifically, we model the auxiliary texts with a heterogeneous entity-word graph (called text-graph), which entails both local and global semantic relationships among entities and words. We then apply graph convolutional networks to learn informative entity embeddings that aggregate high-order neighborhood information. These embeddings are further integrated with the KG triplet embeddings via a gating mechanism, thus enriching the KG representations and alleviating the inherent structure sparsity. Experiments on benchmark datasets show that our method significantly outperforms several state-of-the-art methods.
Collapse
Affiliation(s)
- Linmei Hu
- Department Computer Science, Organization Beijing University of Posts and Telecommunications, Beijing, China
| | - Mengmei Zhang
- Department Computer Science, Organization Beijing University of Posts and Telecommunications, Beijing, China
| | - Shaohua Li
- Department High Performance Computing, Organization ASTAR, Singapore, Singapore
| | - Jinghan Shi
- Department Computer Science, Organization Beijing University of Posts and Telecommunications, Beijing, China
| | - Chuan Shi
- Department Computer Science, Organization Beijing University of Posts and Telecommunications, Beijing, China
| | - Cheng Yang
- Department Computer Science, Organization Beijing University of Posts and Telecommunications, Beijing, China
| | - Zhiyuan Liu
- Department Computer Science, Organization Tsinghua University, Beijing, China
| |
Collapse
|
25
|
Chen RM. Whether County Lockdown Could Deter the Contagion of COVID-19 in the USA. Risk Manag Healthc Policy 2021; 14:2665-2673. [PMID: 34194248 PMCID: PMC8236672 DOI: 10.2147/rmhp.s314750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 05/23/2021] [Indexed: 11/26/2022] Open
Abstract
Aim Whether to lock down a country or not during COVID-19 pandemic becomes a vital issue, since it affects people’s daily life. The objective of this research is to design a measurement that could be utilised to predict the efficacy of a lockdown decision. Methods One would expect that the effectiveness of lockdown lies in the assumption that the virus spreads from one area to another area in a rippling way. If the virus spreads in a radiating way, then lockdown should be an effective countermeasure to contain the pandemic. On the other hand, if it spreads indiscernibly or randomly, then a lockdown decision would have lesser or little effect on the containment. We mainly combine graphs and metric to compute correlation matrices, which would measure whether the virus spreads in a rippling way. The metric used is to measure the boundary (or county) distances between counties. We take 3073 counties and equivalents in the USA and explore the property of contagion with respect to distance. The distance between any two counties is measured by the number of neighbours (or counties) between them. Then, we study the relation between contagion and distances. The relation between distance (complexity of neighbouring) and confirmed cases (contagion) is further explored. Results Then, we study the relation between contagion and distances. The relation between distance (complexity of neighbouring) and confirmed cases (contagion) could be explored. Our research shows county lockdown in the USA plays no important role in containing the spread of coronavirus for the time being. Conclusion Rippling effect in the USA regarding COVID-19 is not significant. This indicates other robust approaches or policies should be taken into consideration, rather than a simple lockdown policy.
Collapse
Affiliation(s)
- Ray-Ming Chen
- School of Mathematics and Statistics, Baise University, Baise CIty, Guangxi Province, People's Republic of China
| |
Collapse
|
26
|
Dabrowski-Tumanski P, Rubach P, Niemyska W, Gren BA, Sulkowska JI. Topoly: Python package to analyze topology of polymers. Brief Bioinform 2021; 22:bbaa196. [PMID: 32935829 PMCID: PMC8138882 DOI: 10.1093/bib/bbaa196] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/15/2020] [Accepted: 07/29/2020] [Indexed: 12/27/2022] Open
Abstract
The increasing role of topology in (bio)physical properties of matter creates a need for an efficient method of detecting the topology of a (bio)polymer. However, the existing tools allow one to classify only the simplest knots and cannot be used in automated sample analysis. To answer this need, we created the Topoly Python package. This package enables the distinguishing of knots, slipknots, links and spatial graphs through the calculation of different topological polynomial invariants. It also enables one to create the minimal spanning surface on a given loop, e.g. to detect a lasso motif or to generate random closed polymers. It is capable of reading various file formats, including PDB. The extensive documentation along with test cases and the simplicity of the Python programming language make it a very simple to use yet powerful tool, suitable even for inexperienced users. Topoly can be obtained from https://topoly.cent.uw.edu.pl.
Collapse
Affiliation(s)
| | | | | | | | - Joanna Ida Sulkowska
- Corresponding author: Joanna Ida Sulkowska, Centre of New Technologies, University of Warsaw, Warsaw, 02-097, Poland; Faculty of Chemistry, University of Warsaw, 02-093, Warsaw, Poland. Tel.: +48-22-55-43678 E-mail:
| |
Collapse
|
27
|
Son H, Pham VT, Jang Y, Kim SE. Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network. Sensors (Basel) 2021; 21:s21093118. [PMID: 33946232 PMCID: PMC8125630 DOI: 10.3390/s21093118] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 04/18/2021] [Accepted: 04/26/2021] [Indexed: 11/27/2022]
Abstract
Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect the adjacent cables and worsen the bridge structure condition. Therefore, we must accurately determine the condition of the cable with a technology-based evaluation strategy. In this paper, we propose a deep learning model that allows us to locate the damaged cable and estimate its cross-sectional area. To obtain the data required for the deep learning training, we use the tension data of the reduced area cable, which are simulated in the Practical Advanced Analysis Program (PAAP), a robust structural analysis program. We represent the sensor data of the damaged cable-stayed bridge as a graph composed of vertices and edges using tension and spatial information of the sensors. We apply the sensor geometry by mapping the tension data to the graph vertices and the connection relationship between sensors to the graph edges. We employ a Graph Neural Network (GNN) to use the graph representation of the sensor data directly. GNN, which has been actively studied recently, can treat graph-structured data with the most advanced performance. We train the GNN framework, the Message Passing Neural Network (MPNN), to perform two tasks to identify damaged cables and estimate the cable areas. We adopt a multi-task learning method for more efficient optimization. We show that the proposed technique achieves high performance with the cable-stayed bridge data generated from PAAP.
Collapse
Affiliation(s)
- Hyesook Son
- Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea;
| | - Van-Thanh Pham
- Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea; (V.-T.P.); (S.-E.K.)
| | - Yun Jang
- Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea;
- Correspondence:
| | - Seung-Eock Kim
- Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea; (V.-T.P.); (S.-E.K.)
| |
Collapse
|
28
|
Ji X, Ferreira T, Friedman B, Liu R, Liechty H, Bas E, Chandrashekar J, Kleinfeld D. Brain microvasculature has a common topology with local differences in geometry that match metabolic load. Neuron 2021; 109:1168-1187.e13. [PMID: 33657412 PMCID: PMC8525211 DOI: 10.1016/j.neuron.2021.02.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 12/09/2020] [Accepted: 02/03/2021] [Indexed: 01/03/2023]
Abstract
The microvasculature underlies the supply networks that support neuronal activity within heterogeneous brain regions. What are common versus heterogeneous aspects of the connectivity, density, and orientation of capillary networks? To address this, we imaged, reconstructed, and analyzed the microvasculature connectome in whole adult mice brains with sub-micrometer resolution. Graph analysis revealed common network topology across the brain that leads to a shared structural robustness against the rarefaction of vessels. Geometrical analysis, based on anatomically accurate reconstructions, uncovered a scaling law that links length density, i.e., the length of vessel per volume, with tissue-to-vessel distances. We then derive a formula that connects regional differences in metabolism to differences in length density and, further, predicts a common value of maximum tissue oxygen tension across the brain. Last, the orientation of capillaries is weakly anisotropic with the exception of a few strongly anisotropic regions; this variation can impact the interpretation of fMRI data.
Collapse
Affiliation(s)
- Xiang Ji
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tiago Ferreira
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
| | - Beth Friedman
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Rui Liu
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hannah Liechty
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Erhan Bas
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
| | | | - David Kleinfeld
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA; Section of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
29
|
Ma Y, Li Q, Hu N, Li L. SeBio Graph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer. Front Neurorobot 2021; 15:665055. [PMID: 33867966 PMCID: PMC8047129 DOI: 10.3389/fnbot.2021.665055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/09/2021] [Indexed: 11/17/2022] Open
Abstract
Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks are mainly evaluated on social and information networks, which improve the quality of network representation y integrating neighbor node descriptions. However, previous methods have not yet been comprehensively studied on biomedical networks. Traditional techniques fail to achieve satisfying results, especially when labeled nodes are deficient in number. In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed. In SeBioGraph, both node embedding and graph-specific prototype embedding are utilized as transferable metric space characterized. By incorporating prior knowledge learned from auxiliary graphs, SeBioGraph further promotes the performance of the target graph. Experimental results on the two-class node classification tasks and three-class link prediction tasks demonstrate that the SeBioGraph realizes state-of-the-art results. Finally, the method is thoroughly evaluated.
Collapse
Affiliation(s)
- Yugang Ma
- School of Architecture and Urban Planning, Chongqing University, Chongqing, China
| | - Qing Li
- School of Computer Science, Northwestern Polytechnical University, Shaanxi, China
| | - Nan Hu
- School of Management Science and Real Estate, Chongqing University, Chongqing, China
| | - Lili Li
- China Construction Science & Technology Group Co., Ltd. Shenzhen, China.,College of Civil and Environmental Engineering, Harbin Institute of Technology, Harbin, China
| |
Collapse
|
30
|
Shin DY, Hussain S, Afzal F, Park C, Afzal D, Farahani MR. Closed Formulas for Some New Degree Based Topological Descriptors Using M-polynomial and Boron Triangular Nanotube. Front Chem 2021; 8:613873. [PMID: 33614594 PMCID: PMC7886695 DOI: 10.3389/fchem.2020.613873] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/09/2020] [Indexed: 11/18/2022] Open
Abstract
In this article, we provide new formulas to compute the reduced reciprocal randić index, Arithmetic geometric1 index, SK index, SK 1 index, SK 2 index, edge version of the first zagreb index, sum connectivity index, general sum connectivity index, and the forgotten index using the M-polynomial and finding these topological indices for a boron triangular nanotube. We also elaborate the results with graphical representations.
Collapse
Affiliation(s)
- Dong Yun Shin
- Department of Mathematics, University of Seoul, Seoul, South Korea
| | - Sabir Hussain
- Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan
| | - Farkhanda Afzal
- Department of Humanities and Basic Sciences, Military College of Signals, National University of Science and Technology, Islamabad, Pakistan
| | - Choonkil Park
- Research Institute for Natural Sciences, Hanyang University, Seoul, South Korea
| | - Deeba Afzal
- Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan
| | - Mohammad R. Farahani
- Department of Mathematics, Iran University of Science and Technology, Tehran, Iran
| |
Collapse
|
31
|
Abstract
Recent wildfire events, in the United States (USA) and around the world, have resulted in thousands of homes destroyed and many lives lost, leaving communities and policy makers, once again, with the question as to how to manage wildfire risk. This is particularly important given the prevalent trend of increased fire frequency and intensity. Current approaches to managing wildfires focus on fire suppression and managing fuel build-up in wildlands. However, reliance on these strategies alone has clearly proven inadequate. As such, focus should be shifted towards minimizing potential losses to communities. Achieving this goal, however, requires detailed understanding of the factors that contribute to community vulnerability and the interplay between probability of ignition, vulnerability and calculated risk. In this study, we evaluate wildfire risk for four different communities across the USA for the duration of May to September to communicate a different perspective of risk assessment. We show, for the first time, that community risk is closely related to wind speed and direction, pattern of surrounding wildland vegetation, and buildings layout. The importance of the findings lies in the need for exploring unique viable solutions to reduce risk for every community independently as opposed to embracing a generalized approach as is currently the case.
Collapse
|
32
|
Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Korevaar E, Cheng AC, Bero L, McKenzie JE. Creating effective interrupted time series graphs: Review and recommendations. Res Synth Methods 2020; 12:106-117. [PMID: 32657532 PMCID: PMC7818488 DOI: 10.1002/jrsm.1435] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/18/2020] [Accepted: 07/09/2020] [Indexed: 11/30/2022]
Abstract
Introduction Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short‐ and long‐term impact of an interruption. Further, well‐constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta‐analyses. Aim We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013‐2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re‐use of the data in systematic reviews and meta‐analyses. Application of data visualization recommendations can improve quality of interrupted time series graphs. Well‐designed graphs accurately depict time series data, any impact of the interruption, and the results of the analysis. Well‐designed graphs facilitate data extraction for use in systematic reviews and reproducibility. An assessment of graphs included in interrupted time series studies (published between 2013 and 2017) found that graphs often do not meet core graphing recommendations.
Collapse
Affiliation(s)
- Simon L Turner
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Amalia Karahalios
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrew B Forbes
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.,Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Elizabeth Korevaar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Victoria, Australia
| | - Lisa Bero
- Faculty of Medicine and Health, School of Pharmacy and Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Joanne E McKenzie
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
33
|
Zhang W, Wang Y. DEEP MULTIMODAL BRAIN NETWORK LEARNING FOR JOINT ANALYSIS OF STRUCTURAL MORPHOMETRY AND FUNCTIONAL CONNECTIVITY. Proc IEEE Int Symp Biomed Imaging 2020; 2020. [PMID: 34012504 DOI: 10.1109/isbi45749.2020.9098624] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Learning from the multimodal brain imaging data attracts a large amount of attention in medical image analysis due to the proliferation of multimodal data collection. It is widely accepted that multimodal data can provide complementary information than mining from a single modality. However, unifying the image-based knowledge from the multimodal data is very challenging due to different image signals, resolution, data structure, etc.. In this study, we design a supervised deep model to jointly analyze brain morphometry and functional connectivity on the cortical surface and we name it deep multimodal brain network learning (DMBNL). Two graph-based kernels, i.e., geometry-aware surface kernel (GSK) and topology-aware network kernel (TNK), are proposed for processing the cortical surface morphometry and brain functional network. The vertex features on the cortical surface from GSK is pooled and feed into TNK as its initial regional features. In the end, the graph-level feature is computed for each individual and thus can be applied for classification tasks. We test our model on a large autism imaging dataset. The experimental results prove the effectiveness of our model.
Collapse
Affiliation(s)
- Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| |
Collapse
|
34
|
Sverchkov Y, Ho YH, Gasch A, Craven M. Context-Specific Nested Effects Models. J Comput Biol 2020; 27:403-417. [PMID: 32053004 DOI: 10.1089/cmb.2019.0459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Advances in systems biology have made clear the importance of network models for capturing knowledge about complex relationships in gene regulation, metabolism, and cellular signaling. A common approach to uncovering biological networks involves performing perturbations on elements of the network, such as gene knockdown experiments, and measuring how the perturbation affects some reporter of the process under study. In this article, we develop context-specific nested effects models (CSNEMs), an approach to inferring such networks that generalizes nested effects models (NEMs). The main contribution of this work is that CSNEMs explicitly model the participation of a gene in multiple contexts, meaning that a gene can appear in multiple places in the network. Biologically, the representation of regulators in multiple contexts may indicate that these regulators have distinct roles in different cellular compartments or cell cycle phases. We present an evaluation of the method on simulated data as well as on data from a study of the sodium chloride stress response in Saccharomyces cerevisiae.
Collapse
Affiliation(s)
- Yuriy Sverchkov
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Yi-Hsuan Ho
- Department of Genetics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Audrey Gasch
- Department of Genetics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Mark Craven
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
| |
Collapse
|
35
|
Lara-Navarra P, Falciani H, Sánchez-Pérez EA, Ferrer-Sapena A. Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection. Int J Environ Res Public Health 2020; 17:E1066. [PMID: 32046238 DOI: 10.3390/ijerph17031066] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/31/2020] [Accepted: 02/04/2020] [Indexed: 12/04/2022]
Abstract
Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens’ health and affects medical professionals, who find themselves having to defend their diagnoses as well as the treatments they propose against ill-informed patients. The propagation of these opinions follows the same pattern as the dissemination of fake news about other important topics, such as the environment, via social media networks, which we use as a testing ground for checking our procedure. In this article, we present an algorithm to analyse the behaviour of users of Twitter, the most important social network with respect to this issue, as well as a dynamic knowledge graph construction method based on information gathered from Twitter and other open data sources such as web pages. To show our methodology, we present a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 is used to develop a heuristic analysis of the validity of the information. The proposed analytical scheme is based on the interaction between the computer tool—a database implemented with Neo4j—and the analyst, who must ask the right questions to the tool, allowing to follow the line of any doubtful data. We also show how this method can be used. We also present some methodological guidelines on how our system could allow, in the future, an automation of the procedures for the construction of an autonomous algorithm for the detection of false news on the internet related to health.
Collapse
|
36
|
Ambriola Oku AY, Zimeo Morais GA, Arantes Bueno AP, Fujita A, Sato JR. Potential Confounders in the Analysis of Brazilian Adolescent's Health: A Combination of Machine Learning and Graph Theory. Int J Environ Res Public Health 2019; 17:ijerph17010090. [PMID: 31877700 PMCID: PMC6981403 DOI: 10.3390/ijerph17010090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/09/2019] [Accepted: 12/16/2019] [Indexed: 12/20/2022]
Abstract
The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to build predictive networks applied to the Brazilian National Student Health Survey (PenSE 2015) data, a large dataset that consists of questionnaires filled by the students. By using a combination of gradient boosting machines and centrality hub metric, it was possible to identify potential confounders to be considered when conducting association analyses among variables. The variables were ranked according to their hub centrality to predict the other variables from a directed weighted-graph perspective. The top five ranked confounder variables were “gender”, “oral health care”, “intended education level”, and two variables associated with nutrition habits—“eat while watching TV” and “never eat fast-food”. In conclusion, although causal effects cannot be inferred from the data, we believe that the proposed approach might be a useful tool to obtain novel insights on the association between variables and to identify general factors related to health conditions.
Collapse
Affiliation(s)
- Amanda Yumi Ambriola Oku
- Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
| | | | - Ana Paula Arantes Bueno
- Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
| | - André Fujita
- Institute of Mathematics and Statistics—University of São Paulo, São Paulo CEP 05508-090, Brazil
| | - João Ricardo Sato
- Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
- Correspondence:
| |
Collapse
|
37
|
Ramos PIP, Arge LWP, Lima NCB, Fukutani KF, de Queiroz ATL. Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets. Front Genet 2019; 10:1120. [PMID: 31798629 PMCID: PMC6863976 DOI: 10.3389/fgene.2019.01120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 10/16/2019] [Indexed: 11/13/2022] Open
Abstract
Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.
Collapse
Affiliation(s)
- Pablo Ivan Pereira Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Luis Willian Pacheco Arge
- Laboratório de Genética Molecular e Biotecnologia Vegetal, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Kiyoshi F. Fukutani
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Fundação José Silveira, Salvador, Brazil
| | - Artur Trancoso L. de Queiroz
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| |
Collapse
|
38
|
Damseh R, Pouliot P, Gagnon L, Sakadzic S, Boas D, Cheriet F, Lesage F. Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy. IEEE J Biomed Health Inform 2019; 23:2551-2562. [PMID: 30507542 PMCID: PMC6546554 DOI: 10.1109/jbhi.2018.2884678] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a three-dimensional surface model generator, and a geometry contraction algorithm to produce graphical models with a single connected component. Based on a quantitative assessment using NetMets metrics, at a tolerance of 60 μm, false negative and false positive geometric error 19 rates are 3.8% and 4.2%, respectively, whereas false nega- 20 tive and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.
Collapse
Affiliation(s)
- Rafat Damseh
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Philippe Pouliot
- Department of Electrical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Research Centre, Montreal Hearth Institute, Montreal, QC, Canada
| | - Louis Gagnon
- Physics Department, Université Laval, Quebec, QC, Canada
| | - Sava Sakadzic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - David Boas
- Neurophotonics Center, Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Farida Cheriet
- Department of Computer and Software Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Frederic Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Department of Electrical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Research Centre, Montreal Hearth Institute, Montreal, QC, Canada
| |
Collapse
|
39
|
Li C, Liu H, Hu Q, Que J, Yao J. A Novel Computational Model for Predicting microRNA-Disease Associations Based on Heterogeneous Graph Convolutional Networks. Cells 2019; 8:cells8090977. [PMID: 31455028 PMCID: PMC6769654 DOI: 10.3390/cells8090977] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 08/22/2019] [Accepted: 08/23/2019] [Indexed: 01/13/2023] Open
Abstract
Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA-disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA-disease associations (HGCNMDA), which is based on known human protein-protein interaction (PPI) and integrates four biological networks: miRNA-disease, miRNA-gene, disease-gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA-disease interactions.
Collapse
Affiliation(s)
- Chunyan Li
- School of Informatics, Xiamen University, Xiamen 361005, China
- Graduate School, Yunnan Minzu University, Kunming 650504, China
| | - Hongju Liu
- College of Information Technology and Computer Science, University of the Cordilleras, Baguio 2600, Philippines
| | - Qian Hu
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Jinlong Que
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Junfeng Yao
- School of Informatics, Xiamen University, Xiamen 361005, China.
| |
Collapse
|
40
|
Abstract
Complex networks gathered from our online interactions provide a rich source of information that can be used to try to model and predict our behavior. While this has very tangible benefits that we have all grown accustomed to, there is a concrete privacy risk in sharing potentially sensitive data about ourselves and the people we interact with, especially when this data is publicly available online and unprotected from malicious attacks. k-anonymity is a technique aimed at reducing this risk by obfuscating the topological information of a graph that can be used to infer the nodes' identity. In this paper we propose a novel algorithm to enforce k-anonymity based on a well-known result in extremal graph theory, the Szemerédi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemerédi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave almost randomly. With this partition, we anonymize the graph by randomizing the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. We test the proposed approach on real-world networks extracted from Facebook. Our experimental results show that the proposed approach is able to anonymize a graph while retaining most of its structural information.
Collapse
Affiliation(s)
- Daniele Foffano
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca' Foscari Venezia, Venezia, Italy
| | - Luca Rossi
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Andrea Torsello
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca' Foscari Venezia, Venezia, Italy
| |
Collapse
|
41
|
Li C, Zhao J, Wang C, Yao Y. Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation. Comb Chem High Throughput Screen 2019; 21:100-110. [PMID: 29380690 PMCID: PMC5930480 DOI: 10.2174/1386207321666180130100838] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 01/24/2018] [Accepted: 01/26/2018] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE The rapid increase in the amount of protein sequence data available leads to an urgent need for novel computational algorithms to analyze and compare these sequences. This study is undertaken to develop an efficient computational approach for timely encoding protein sequences and extracting the hidden information. METHODS Based on two physicochemical properties of amino acids, a protein primary sequence was converted into a three-letter sequence, and then a graph without loops and multiple edges and its geometric line adjacency matrix were obtained. A generalized PseAAC (pseudo amino acid composition) model was thus constructed to characterize a protein sequence numerically. RESULTS By using the proposed mathematical descriptor of a protein sequence, similarity comparisons among β-globin proteins of 17 species and 72 spike proteins of coronaviruses were made, respectively. The resulting clusters agreed well with the established taxonomic groups. In addition, a generalized PseAAC based SVM (support vector machine) model was developed to identify DNA-binding proteins. Experiment results showed that our method performed better than DNAbinder, DNA-Prot, iDNA-Prot and enDNA-Prot by 3.29-10.44% in terms of ACC, 0.056-0.206 in terms of MCC, and 1.45-15.76% in terms of F1M. When the benchmark dataset was expanded with negative samples, the presented approach outperformed the four previous methods with improvement in the range of 2.49-19.12% in terms of ACC, 0.05-0.32 in terms of MCC, and 3.82- 33.85% in terms of F1M. CONCLUSION These results suggested that the generalized PseAAC model was very efficient for comparison and analysis of protein sequences, and very competitive in identifying DNA-binding proteins.
Collapse
Affiliation(s)
- Chun Li
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China.,Department of Mathematics, Bohai University, Jinzhou 121013, China.,Research Institute of Food Science, Bohai University, Jinzhou 121013, China
| | - Jialing Zhao
- Department of Mathematics, Bohai University, Jinzhou 121013, China
| | - Changzhong Wang
- Department of Mathematics, Bohai University, Jinzhou 121013, China
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| |
Collapse
|
42
|
Ponisio LC, de Valpine P, M'Gonigle LK, Kremen C. Proximity of restored hedgerows interacts with local floral diversity and species' traits to shape long-term pollinator metacommunity dynamics. Ecol Lett 2019; 22:1048-1060. [PMID: 30938483 DOI: 10.1111/ele.13257] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/28/2018] [Accepted: 02/22/2019] [Indexed: 01/09/2023]
Abstract
Disconnected habitat fragments are poor at supporting population and community persistence; restoration ecologists, therefore, advocate for the establishment of habitat networks across landscapes. Few empirical studies, however, have considered how networks of restored habitat patches affect metacommunity dynamics. Here, using a 10-year study on restored hedgerows and unrestored field margins within an intensive agricultural landscape, we integrate occupancy modelling with network theory to examine the interaction between local and landscape characteristics, habitat selection and dispersal in shaping pollinator metacommunity dynamics. We show that surrounding hedgerows and remnant habitat patches interact with the local floral diversity, bee diet breadth and bee body size to influence site occupancy, via colonisation and persistence dynamics. Florally diverse sites and generalist, small-bodied species are most important for maintaining metacommunity connectivity. By providing the first in-depth assessment of how a network of restored habitat influences long-term population dynamics, we confirm the conservation benefit of hedgerows for pollinator populations and demonstrate the importance of restoring and maintaining habitat networks within an inhospitable matrix.
Collapse
Affiliation(s)
- Lauren C Ponisio
- Department of Entomology, University of California, Riverside 417 Entomology Bldg., Riverside, 92521, CA, USA
| | - Perry de Valpine
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, 130 Mulford Hall, Berkeley, 94720, CA, USA
| | - Leithen K M'Gonigle
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada, V5A 1S6
| | - Claire Kremen
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, 130 Mulford Hall, Berkeley, 94720, CA, USA.,Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
| |
Collapse
|
43
|
Priebe CE, Park Y, Vogelstein JT, Conroy JM, Lyzinski V, Tang M, Athreya A, Cape J, Bridgeford E. On a two-truths phenomenon in spectral graph clustering. Proc Natl Acad Sci U S A 2019; 116:5995-6000. [PMID: 30850525 DOI: 10.1073/pnas.1814462116] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is of significant current interest, finding applications throughout the sciences. But as with clustering in general, what a particular methodology identifies as “clusters” is defined (explicitly, or, more often, implicitly) by the clustering algorithm itself. We provide a clear and concise demonstration of a “two-truths” phenomenon for spectral graph clustering in which the first step—spectral embedding—is either Laplacian spectral embedding, wherein one decomposes the normalized Laplacian of the adjacency matrix, or adjacency spectral embedding given by a decomposition of the adjacency matrix itself. The two resulting clustering methods identify fundamentally different (true and meaningful) structure. Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is commonly approached via K-means (or, more generally, Gaussian mixture model) clustering composed with either Laplacian spectral embedding (LSE) or adjacency spectral embedding (ASE). Recent theoretical results provide deeper understanding of the problem and solutions and lead us to a “two-truths” LSE vs. ASE spectral graph clustering phenomenon convincingly illustrated here via a diffusion MRI connectome dataset: The different embedding methods yield different clustering results, with LSE capturing left hemisphere/right hemisphere affinity structure and ASE capturing gray matter/white matter core–periphery structure.
Collapse
|
44
|
Solo V, Poline JB, Lindquist MA, Simpson SL, Bowman FD, Chung MK, Cassidy B. Connectivity in fMRI: Blind Spots and Breakthroughs. IEEE Trans Med Imaging 2018; 37:1537-1550. [PMID: 29969406 PMCID: PMC6291757 DOI: 10.1109/tmi.2018.2831261] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.
Collapse
|
45
|
Abstract
Differentiation within multicellular organisms is controlled by epigenetic markers transmitted across cell division. The process of differentiation will modify these epigenetic markers so that information that one cell type possesses can be lost in the transition to another. Many of the systems that encode these markers also exist in unicellular organisms but do not control differentiation. Thus, during the evolution of multicellularity, epigenetic inheritance systems were probably exapted for their current use in differentiation. We show that the simultaneous use of an information carrier for differentiation and transmission across generations can lead to the evolution of cell types that do not directly contribute to the progeny of the organism and ergo a germ-soma distinction. This shows that an intrinsic instability during a transition from unicellularity to multicellularity may contribute to widespread evolution of a germline and its maintenance, a phenomenon also relevant to the evolution of eusociality. The difference in epigenetic information contents between different cell lines in a multicellular organism is also relevant for the full-success cloning of higher animals, as well as for the maintenance of single germlines over evolutionary timescales.This article is part of the themed issue 'The major synthetic evolutionary transitions'.
Collapse
Affiliation(s)
| | - Eric Libby
- Santa Fe Institute, Santa Fe, NM 87501, USA
| |
Collapse
|
46
|
He J, Liu YM, Tian JK, Liu XH. Some new sharp bounds for the spectral radius of a nonnegative matrix and its application. J Inequal Appl 2017; 2017:260. [PMID: 29104398 PMCID: PMC5648768 DOI: 10.1186/s13660-017-1536-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 10/10/2017] [Indexed: 06/07/2023]
Abstract
In this paper, we give some new sharp upper and lower bounds for the spectral radius of a nonnegative irreducible matrix. Using these bounds, we obtain some new and improved bounds for the signless Laplacian spectral radius of a graph or a digraph.
Collapse
Affiliation(s)
- Jun He
- School of Mathematics, Zunyi Normal College, Zunyi, Guizhou 563006 P.R. China
| | - Yan-Min Liu
- School of Mathematics, Zunyi Normal College, Zunyi, Guizhou 563006 P.R. China
| | - Jun-Kang Tian
- School of Mathematics, Zunyi Normal College, Zunyi, Guizhou 563006 P.R. China
| | - Xiang-Hu Liu
- School of Mathematics, Zunyi Normal College, Zunyi, Guizhou 563006 P.R. China
| |
Collapse
|
47
|
Abstract
Univariate analysis has the purpose to describe a single variable distribution in one sample. It is the first important step of every clinical trial. In this short review, we focus on this analysis, the methods that authors should use to report this type of data, information that they should not miss and mistakes that they must avoid.
Collapse
Affiliation(s)
- Stefania Canova
- Department of Medical Oncology, San Gerardo Hospital Monza, Monza 20900, Italy
| | | | - Federico Ambrogi
- Department of Clinical Sciences and Community Health, Medical Statistics, Biometry and Bioinformatics, University of Milan, Milan 20133, Italy
| |
Collapse
|
48
|
Geng S, Liu X, Biswal BB, Niu H. Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network. Front Neurosci 2017; 11:392. [PMID: 28775676 PMCID: PMC5517460 DOI: 10.3389/fnins.2017.00392] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 06/22/2017] [Indexed: 12/18/2022] Open
Abstract
As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.
Collapse
Affiliation(s)
- Shujie Geng
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal UniversityBeijing, China
| | - Xiangyu Liu
- Department of Neurology, Shenzhen Longhua District Central HospitalGuang dong, China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University HeightNewark, NJ, United States
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal UniversityBeijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal UniversityBeijing, China
| |
Collapse
|
49
|
Biswas A, Ranjan D, Zubair M, Zeil S, Nasr KA, He J. An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images. IEEE/ACM Trans Comput Biol Bioinform 2017; 14:578-586. [PMID: 27008671 PMCID: PMC5071113 DOI: 10.1109/tcbb.2016.2543721] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from α-proteins and two Cryo-EM images of α-β proteins. We observed that the rank of the true topologies is consistently improved by using multiple secondary structure predictions instead of a single prediction. The results show that the algorithm is robust and works well even when errors/misses in the predicted secondary structures are present in the image or the sequence. The results also show that the algorithm is efficient and is able to handle proteins with as many as 33 helices.
Collapse
Affiliation(s)
- Abhishek Biswas
- Dept. of Computer Science, Old Dominion University, Norfolk, VA 23529
| | - Desh Ranjan
- Dept. of Computer Science, Old Dominion University, Norfolk, VA 23529
| | - Mohammad Zubair
- Dept. of Computer Science, Old Dominion University, Norfolk, VA 23529
| | - Stephanie Zeil
- Dept. of Computer Science, Old Dominion University, Norfolk, VA 23529
| | - Kamal Al Nasr
- Dept. of Computer Science, Tennessee State University, Nashville, TN 37209
| | - Jing He
- Dept. of Computer Science, Old Dominion University, Norfolk, VA 23529
| |
Collapse
|
50
|
Abstract
Automated and reliable segmentation of subcortical structures from human brain magnetic resonance images is of great importance for volumetric and shape analyses in quantitative neuroimaging studies. However, poor boundary contrast and variable shape of these structures make the automated segmentation a tough task. We propose a 3D graph-based machine learning method, called LOGISMOS-RF, to segment the caudate and the putamen from brain MRI scans in a robust and accurate way. An atlas-based tissue classification and bias-field correction method is applied to the images to generate an initial segmentation for each structure. Then a 3D graph framework is utilized to construct a geometric graph for each initial segmentation. A locally trained random forest classifier is used to assign a cost to each graph node. The max-flow algorithm is applied to solve the segmentation problem. Evaluation was performed on a dataset of T1-weighted MRI's of 62 subjects, with 42 images used for training and 20 images for testing. For comparison, FreeSurfer and FSL approaches were also evaluated using the same dataset. Dice overlap coefficients and surface-to-surfaces distances between the automated segmentation and expert manual segmentations indicate the results of our method are statistically significantly more accurate than the other two methods, for both the caudate (Dice: 0.89 ± 0.03) and the putamen (0.89 ± 0.03).
Collapse
Affiliation(s)
- Zhihui Guo
- Dept. of Biomedical Engineering, Univ. of Iowa, Iowa City, IA, USA 52242.,Iowa Institute for Biomedical Imaging, Univ. of Iowa, Iowa City, IA, USA 52242
| | - Satyananda Kashyap
- Dept. of Electrical & Computer Engineering, Univ. of Iowa, Iowa City, IA, USA 52242.,Iowa Institute for Biomedical Imaging, Univ. of Iowa, Iowa City, IA, USA 52242
| | - Milan Sonka
- Dept. of Electrical & Computer Engineering, Univ. of Iowa, Iowa City, IA, USA 52242.,Iowa Institute for Biomedical Imaging, Univ. of Iowa, Iowa City, IA, USA 52242
| | - Ipek Oguz
- Dept. of Radiology, University of Pennsylvania, Philadelphia, PA, USA 19104
| |
Collapse
|