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Dong T, Sun Y, Liang F. Deep network embedding with dimension selection. Neural Netw 2024; 179:106512. [PMID: 39032394 PMCID: PMC11408115 DOI: 10.1016/j.neunet.2024.106512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 06/25/2024] [Accepted: 07/04/2024] [Indexed: 07/23/2024]
Abstract
Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-based, with the embedding dimension determined in a heuristic or ad hoc way, which can cause potential bias in downstream statistical inference. Additionally, existing deep embedding methods can suffer from a nonidentifiability issue due to the universal approximation power of deep neural networks. We address these issues within a rigorous statistical framework. We treat the embedding vectors as missing data, reconstruct the network features using a sparse decoder, and simultaneously impute the embedding vectors and train the sparse decoder using an adaptive stochastic gradient Markov chain Monte Carlo (MCMC) algorithm. Under mild conditions, we show that the sparse decoder provides a parsimonious mapping from the embedding space to network features, enabling effective selection of the embedding dimension and overcoming the nonidentifiability issue encountered by existing deep embedding methods. Furthermore, we show that the embedding vectors converge weakly to a desired posterior distribution in the 2-Wasserstein distance, addressing the potential bias issue experienced by existing embedding methods. This work lays down the first theoretical foundation for network embedding within the framework of missing data imputation.
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Affiliation(s)
- Tianning Dong
- Department of Statistics, Purdue University, West Lafayette, IN 47907, United States of America
| | - Yan Sun
- Department of Statistics, Purdue University, West Lafayette, IN 47907, United States of America
| | - Faming Liang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, United States of America.
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2
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Peng S, Yang M, Yang Z, Chen T, Xie J, Ma G. A weighted prior tensor train decomposition method for community detection in multi-layer networks. Neural Netw 2024; 179:106523. [PMID: 39053300 DOI: 10.1016/j.neunet.2024.106523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/12/2024] [Accepted: 07/06/2024] [Indexed: 07/27/2024]
Abstract
Community detection in multi-layer networks stands as a prominent subject within network analysis research. However, the majority of existing techniques for identifying communities encounter two primary constraints: they lack suitability for high-dimensional data within multi-layer networks and fail to fully leverage additional auxiliary information among communities to enhance detection accuracy. To address these limitations, a novel approach named weighted prior tensor training decomposition (WPTTD) is proposed for multi-layer network community detection. Specifically, the WPTTD method harnesses the tensor feature optimization techniques to effectively manage high-dimensional data in multi-layer networks. Additionally, it employs a weighted flattened network to construct prior information for each dimension of the multi-layer network, thereby continuously exploring inter-community connections. To preserve the cohesive structure of communities and to harness comprehensive information within the multi-layer network for more effective community detection, the common community manifold learning (CCML) is integrated into the WPTTD framework for enhancing the performance. Experimental evaluations conducted on both artificial and real-world networks have verified that this algorithm outperforms several mainstream multi-layer network community detection algorithms.
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Affiliation(s)
- Siyuan Peng
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Mingliang Yang
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Zhijing Yang
- School of Information Engineering, Guangdong University of Technology, 510006, China.
| | - Tianshui Chen
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Jieming Xie
- School of Information Engineering, Guangdong University of Technology, 510006, China
| | - Guang Ma
- Department of Computer Science, University of York, YO105DD, England, United Kingdom
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3
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Cheong S, Shin DH, Lee SH, Jang YH, Han J, Shim SK, Han JK, Ghenzi N, Hwang CS. Hyperplane tree-based data mining with a multi-functional memristive crossbar array. MATERIALS HORIZONS 2024. [PMID: 39354778 DOI: 10.1039/d4mh00942h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
This study explores the stochastic and binary switching behaviors of a Ta/HfO2/RuO2 memristor to implement a combined data mining approach for outlier detection and data clustering algorithms in a multi-functional memristive crossbar array. The memristor switches stochastically with high state dispersion in the stochastic mode and deterministically between two states with low dispersion in the binary mode, while they can be controlled by varying operating voltages. The stochastic mode facilitates the parallel generation of random hyperplanes in a tree structure, used to compress spatial information of the dataset in the Euclidian space into binary format, still retaining sufficient spatial features. The ensemble effect from multiple trees improved the classification performance. The binary mode facilitates parallel Hamming distance calculation of the binary codes containing spatial information, which measures similarity. These two modes enable efficient implementation of the newly proposed minority-based outlier detection method and modified K-means method on the same hardware. Array measurements and hardware simulations investigate various hyperparameters' impact and validate the proposed methods with practical datasets. The proposed methods show linear O(n) time complexity and high energy efficiency, consuming <1% of the energy compared to digital computing with conventional algorithms while demonstrating software-comparable performance in both tasks.
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Affiliation(s)
- Sunwoo Cheong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Dong Hoon Shin
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Soo Hyung Lee
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sung Keun Shim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Joon-Kyu Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- System Semiconductor Engineering and the Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
| | - Néstor Ghenzi
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Universidad de Avellaneda UNDAV and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mario Bravo 1460, Avellaneda, Buenos Aires 1872, Argentina.
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
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Thakuria S, Paul S. Salt-bridge mediated conformational dynamics in the figure-of-eight knotted ketol acid reductoisomerase (KARI). Phys Chem Chem Phys 2024; 26:24963-24974. [PMID: 39297222 DOI: 10.1039/d4cp02677b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
The utility of knotted proteins in biological activities has been ambiguous since their discovery. From their evolutionary significance to their functionality in stabilizing the native protein structure, a unilateral conclusion hasn't been achieved yet. While most studies have been performed to understand the stabilizing effect of the knotted fold on the protein chain, more ideas are yet to emerge regarding the interactions in stabilizing the knot. Using classical molecular dynamics (MD) simulations, we have explored the dynamics of the figure-of-eight knotted domain present in ketol acid reductoisomerase (KARI). Our main focus was on the presence of a salt bridge network evident within the knotted region and its role in shaping the conformational dynamics of the knotted chain. Through the potential of mean forces (PMFs) calculation, we have also marked the specific salt bridges that are pivotal in stabilizing the knotted structure. The correlated motions have been further monitored with the help of principal component analysis (PCA) and dynamic cross-correlation maps (DCCM). Furthermore, mutation of the specific salt bridges led to a change in their conformational stability, vindicating their importance.
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Affiliation(s)
- Sanjib Thakuria
- Department of Chemistry, Indian Institute of Technology, Guwahati, Assam, 781039, India.
| | - Sandip Paul
- Department of Chemistry, Indian Institute of Technology, Guwahati, Assam, 781039, India.
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5
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Salbanya B, Carrasco-Farré C, Nin J. Structure matters: Assessing the statistical significance of network topologies. PLoS One 2024; 19:e0309005. [PMID: 39356706 PMCID: PMC11446434 DOI: 10.1371/journal.pone.0309005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 08/04/2024] [Indexed: 10/04/2024] Open
Abstract
Network analysis has found widespread utility in many research areas. However, assessing the statistical significance of observed relationships within networks remains a complex challenge. Traditional node permutation tests are often insufficient in capturing the effect of changing network topology by creating reliable null distributions. We propose two randomization alternatives to address this gap: random rewiring and controlled rewiring. These methods incorporate changes in the network topology through edge swaps. However, controlled rewiring allows for more nuanced alterations of the original network than random rewiring. In this sense, this paper introduces a novel evaluation tool, the Expanded Quadratic Assignment Procedure (EQAP), designed to calculate a specific p-value and interpret statistical tests with enhanced precision. The combination of EQAP and controlled rewiring provides a robust network comparison and statistical analysis framework. The methodology is exemplified through two real-world examples: the analysis of an organizational network structure, illustrated by the Enron-Email dataset, and a social network case, represented by the UK Faculty friendship network. The utility of these statistical tests is underscored by their capacity to safeguard researchers against Type I errors when exploring network metrics dependent on intricate topologies.
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Affiliation(s)
- Bernat Salbanya
- Universitat Ramon Llull, Esade, Avinguda de la Torre Blanca, Catalonia, Spain
| | - Carlos Carrasco-Farré
- Information Systems Department, Toulouse Business School, Toulouse, Occitanie, France
| | - Jordi Nin
- Universitat Ramon Llull, Esade, Avinguda de la Torre Blanca, Catalonia, Spain
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6
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Yu J, Yan C, Paul T, Brewer L, Tsutakawa SE, Tsai CL, Hamdan SM, Tainer JA, Ivanov I. Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair. Nat Commun 2024; 15:8511. [PMID: 39353945 DOI: 10.1038/s41467-024-52860-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 09/19/2024] [Indexed: 10/04/2024] Open
Abstract
Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC's assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD's DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC's dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5' ssDNA, exposing XPA's crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.
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Affiliation(s)
- Jina Yu
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Chunli Yan
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Tanmoy Paul
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Lucas Brewer
- Department of Chemistry, Georgia State University, Atlanta, GA, USA
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
| | - Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Chi-Lin Tsai
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Samir M Hamdan
- Bioscience Program, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - John A Tainer
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Ivaylo Ivanov
- Department of Chemistry, Georgia State University, Atlanta, GA, USA.
- Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA.
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7
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Castro C, Leiva V, Garrido D, Huerta M, Minatogawa V. Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108321. [PMID: 39053350 DOI: 10.1016/j.cmpb.2024.108321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/14/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024]
Abstract
This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that integrates bibliometric analysis, network analysis, thematic mapping, and latent Dirichlet allocation, the study explores the terrain and prospective developments within this usage based on data analytics. Through a meticulous examination of the analyzed articles, the present study identifies seven key thematic areas, highlighting the diverse applications and interdisciplinary nature of blockchain in clinical trials. Our findings reveal blockchain capability to enhance data management, participant consent processes, as well as overall trial transparency, efficiency, and security. Additionally, the investigation discloses the emerging synergy between blockchain and advanced technologies, such as artificial intelligence and federated learning, proposing innovative directions for improving clinical research methodologies. Our study underscores the collaborative efforts in dealing with the complexities of integrating blockchain into the areas of clinical trials and healthcare, delineating the transformative potential of blockchain technology in revolutionizing these areas by addressing challenges and promoting practices of efficient, secure, and transparent research. The delineated themes and networks of collaboration provide a blueprint for future inquiry, showing the importance of empirical research to narrow the gap between theoretical promise and practical implementation.
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Affiliation(s)
- Cecilia Castro
- Centre of Mathematics, Universidade do Minho, Braga, Portugal
| | - Víctor Leiva
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
| | - Diego Garrido
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Mauricio Huerta
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Vinicius Minatogawa
- Escuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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8
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Zhou H, Yan S. Deciphering Internal Regulatory Patterns within the p53 Core Tetramer: Insights from Community Network Analysis. J Phys Chem Lett 2024; 15:9652-9658. [PMID: 39283177 DOI: 10.1021/acs.jpclett.4c02382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Gene therapy is one of the most effective strategies for cancer treatment. The p53 protein, commonly known as the "guardian of the genome", plays a critical role in gene activation and tumor suppression. Tetramerization of the p53 core domain is an essential allosteric process that supports its suppression functions. This letter presents a framework to analyze the structure, function, and dynamic connectivity of the p53 tetramer, using community network analysis based on all-atom molecular dynamics simulations. The communities within the p53 monomer exhibit distinct functional roles, while interactions between molecules establish a symmetrical network structure. We identified direct evidence of single, double, and multiple pathway regulations within the p53 tetramer and crucial residue pairs involved in these connections. Our study provides a comprehensive framework to understand the community network of the p53 tetramer, offering new insights into the stable formation of the p53 core tetramer.
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Affiliation(s)
- Han Zhou
- School of Physics and Astronomy, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Shiwei Yan
- School of Physics and Astronomy, Beijing Normal University, Beijing 100875, People's Republic of China
- Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai, Guangdong 519087, People's Republic of China
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9
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Yang JX, Wang H, Li X, Tan Y, Ma Y, Zeng M. A control measure for epidemic spread based on the susceptible-infectious-susceptible (SIS) model. Biosystems 2024; 246:105341. [PMID: 39332804 DOI: 10.1016/j.biosystems.2024.105341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 09/14/2024] [Accepted: 09/22/2024] [Indexed: 09/29/2024]
Abstract
When an epidemic occurs in a network, finding the important links and cutting them off is an effective measure for preventing the spread of the epidemic. Traditional methods that remove important links easily lead to a disconnected network, inevitably incurring high costs arising from quarantining individuals or communities in a real-world network. In this study, we combine the clustering coefficient and the eigenvector to identify the important links using the susceptible-infectious-susceptible (SIS) model. The results show that our approach can improve the epidemic threshold while maintaining the connectivity of the network to control the spread of the epidemic. Experiments on multiple real-world and synthetic networks of varying sizes, demonstrate the effectiveness and scalability of our approach.
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Affiliation(s)
- Jin-Xuan Yang
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, PR China.
| | - Haiyan Wang
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, PR China
| | - Xin Li
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, PR China
| | - Ying Tan
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, PR China
| | - Yongjuan Ma
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, PR China
| | - Min Zeng
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, PR China
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10
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Agostoni P, Chiesa M, Salvioni E, Emdin M, Piepoli M, Sinagra G, Senni M, Bonomi A, Adamopoulos S, Miliopoulos D, Mapelli M, Campodonico J, Attanasio U, Apostolo A, Pestrin E, Rossoni A, Magrì D, Paolillo S, Corrà U, Raimondo R, Cittadini A, Iorio A, Salzano A, Lagioia R, Vignati C, Badagliacca R, Filardi PP, Correale M, Perna E, Metra M, Cattadori G, Guazzi M, Limongelli G, Parati G, De Martino F, Matassini MV, Bandera F, Bussotti M, Re F, Lombardi CM, Scardovi AB, Sciomer S, Passantino A, Santolamazza C, Girola D, Passino C, Karsten M, Nodari S, Pompilio G. The chronic heart failure evolutions: Different fates and routes. ESC Heart Fail 2024. [PMID: 39318188 DOI: 10.1002/ehf2.14966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/09/2024] [Accepted: 06/24/2024] [Indexed: 09/26/2024] Open
Abstract
AIMS Individual prognostic assessment and disease evolution pathways are undefined in chronic heart failure (HF). The application of unsupervised learning methodologies could help to identify patient phenotypes and the progression in each phenotype as well as to assess adverse event risk. METHODS AND RESULTS From a bulk of 7948 HF patients included in the MECKI registry, we selected patients with a minimum 2-year follow-up. We implemented a topological data analysis (TDA), based on 43 variables derived from clinical, biochemical, cardiac ultrasound, and exercise evaluations, to identify several patients' clusters. Thereafter, we used the trajectory analysis to describe the evolution of HF states, which is able to identify bifurcation points, characterized by different follow-up paths, as well as specific end-stages conditions of the disease. Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant). Findings were validated on internal (n = 527) and external (n = 777) populations. We analyzed 4876 patients (age = 63 [53-71], male gender n = 3973 (81.5%), NYHA class I-II n = 3576 (73.3%), III-IV n = 1300 (26.7%), LVEF = 33 [25.5-39.9], atrial fibrillation n = 791 (16.2%), peak VO2% pred = 54.8 [43.8-67.2]), with a minimum 2-year follow-up. Nineteen patient clusters were identified by TDA. Trajectory analysis revealed a path characterized by 3 bifurcation and 4 end-stage points. Clusters survival rate varied from 44% to 100% at 2 years and from 20% to 100% at 5 years, respectively. The event frequency at 5-year follow-up for each study cohort cluster was successfully compared with those in the validation cohorts (R = 0.94 and R = 0.84, P < 0.001, for internal and external cohort, respectively). Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant observed in 22% of cases). CONCLUSIONS Each HF phenotype has a specific disease progression and prognosis. These findings allow to individualize HF patient evolutions and to tailor assessment.
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Affiliation(s)
- Piergiuseppe Agostoni
- Centro Cardiologico Monzino, IRCCs, Milan, Italy
- Department of Clinical Sciences and Community Health, Section of Cardiology, University of Milan, Milan, Italy
| | - Mattia Chiesa
- Centro Cardiologico Monzino, IRCCs, Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | | | - Michele Emdin
- Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy
- Cardio-Thoracic Department, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Massimo Piepoli
- Department of Clinical Cardiology, IRCCS Policlinico San Donato, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Gianfranco Sinagra
- Department of Cardiology, 'Azienda Sanitaria Universitaria Giuliano-Isontina', Trieste, Italy
| | - Michele Senni
- Department of Cardiology, Unit of Cardiology, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Alice Bonomi
- Centro Cardiologico Monzino, IRCCs, Milan, Italy
| | - Stamatis Adamopoulos
- Heart Failure and Heart Transplant Units, Onassis Cardiac Surgery Centre, Attica, Greece
| | - Dimitris Miliopoulos
- Heart Failure and Heart Transplant Units, Onassis Cardiac Surgery Centre, Attica, Greece
| | - Massimo Mapelli
- Centro Cardiologico Monzino, IRCCs, Milan, Italy
- Department of Clinical Sciences and Community Health, Section of Cardiology, University of Milan, Milan, Italy
| | | | | | | | | | | | - Damiano Magrì
- Department of Clinical and Molecular Medicine, Azienda Ospedaliera Sant'Andrea, 'Sapienza' Università degli Studi di Roma, Rome, Italy
| | - Stefania Paolillo
- Dipartimento di scienze biomediche avanzate, Federico II University, Naples, Italy
| | - Ugo Corrà
- Department of Cardiology, Istituti Clinici Scientifici Maugeri, IRCCS, Veruno Institute, Veruno, Italy
| | - Rosa Raimondo
- Divisione di Cardiologia Riabilitativa, Istituti Clinici Scientifici Maugeri, Varese, Italy
| | - Antonio Cittadini
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
- Interdepartmental Center for Gender Medicine Research 'GENESIS', Naples, Italy
| | - Annamaria Iorio
- Department of Cardiology, Unit of Cardiology, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Andrea Salzano
- Cardiac Unit, AORN A Cardarelli, Naples, Italy
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Rocco Lagioia
- UOC Cardiologia di Riabilitativa, Mater Dei Hospital, Bari, Italy
| | | | - Roberto Badagliacca
- Dipartimento di Scienze Cliniche, Internistiche, Anestesiologiche e Cardiovascolari, 'Sapienza', Rome University, Rome, Italy
| | - Pasquale Perrone Filardi
- Department of Advanced Biomedical Sciences, Federico II University of Naples and Mediterranea CardioCentro, Naples, Italy
| | | | - Enrico Perna
- Dipartimento cardio-toraco-vascolare, Ospedale Cà Granda- A.O. Niguarda, Milan, Italy
| | - Marco Metra
- Department of Cardiology, Department of Medical and Surgical Specialities, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Gaia Cattadori
- Unità Operativa Cardiologia Riabilitativa, IRCCS Multimedica, Milan, Italy
| | | | - Giuseppe Limongelli
- Cardiologia SUN, Ospedale Monaldi (Azienda dei Colli), Seconda Università di Napoli, Naples, Italy
| | - Gianfranco Parati
- Department of Cardiovascular, Neural and Metabolic Sciences, San Luca Hospital, Istituto Auxologico Italiano, IRCCS, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Fabiana De Martino
- Unità funzionale di cardiologia, Casa di Cura Tortorella, Salerno, Italy
| | - Maria Vittoria Matassini
- Department of Cardiology, Division of Cardiac Intensive Care Unit-Cardiology, Ospedali Riuniti di Ancona, Ancona, Italy
| | - Francesco Bandera
- Department of Biomedical Sciences for Health, University of Milano, Milan, Italy
- Department of Cardiology, IRCCS Policlinico San Donato, Milan, Italy
| | - Maurizio Bussotti
- Cardiac Rehabilitation Unit, Istituti Clinici Scientifici Maugeri, IRCCS, Scientific Institute of Milan, Milan, Italy
| | - Federica Re
- Division of Cardiology, Cardiac Arrhythmia Center and Cardiomyopathies Unit, San Camillo-Forlanini Hospital, Rome, Italy
| | - Carlo M Lombardi
- Department of Cardiology, Department of Medical and Surgical Specialities, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | | | - Susanna Sciomer
- Dipartimento di Scienze Cliniche, Internistiche, Anestesiologiche e Cardiovascolari, 'Sapienza', Rome University, Rome, Italy
| | - Andrea Passantino
- Division of Cardiology, Istituti Clinici Scientifici Maugeri, Institute of Bari, Bari, Italy
| | - Caterina Santolamazza
- Dipartimento cardio-toraco-vascolare, Ospedale Cà Granda- A.O. Niguarda, Milan, Italy
| | - Davide Girola
- Clinica Hildebrand, Centro di Riabilitazione Brissago, Brissago, Switzerland
| | - Claudio Passino
- Health Science Interdisciplinary Center, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Marlus Karsten
- Centro Cardiologico Monzino, IRCCs, Milan, Italy
- Programa de Pós-Graduação em Fisioterapia, UDESC, Florianópolis, Brazil
| | - Savina Nodari
- Department of Medical and Surgical Specialities, Radiological Sciences and Public Health, University of Brescia Medical School, Brescia, Italy
| | - Giulio Pompilio
- Centro Cardiologico Monzino, IRCCs, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy
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de Araújo WS, Bergamini LL, Almeida-Neto M. Global effects of land-use intensity and exotic plants on the structure and phylogenetic signal of plant-herbivore networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173949. [PMID: 38876343 DOI: 10.1016/j.scitotenv.2024.173949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/23/2024] [Accepted: 06/10/2024] [Indexed: 06/16/2024]
Abstract
Interactions between plants and herbivorous insects are often phylogenetically structured, with closely related insect species using similar sets of species or lineages of plants, while phylogenetically closer plants tend to share high proportions of their herbivore insect species. Notably, these phylogenetic constraints in plant-herbivore interactions tend to be more pronounced among internal plant-feeding herbivores (i.e., endophages) than among external feeders (i.e., exophages). In the context of growing human-induced habitat conversion and the global proliferation of exotic species, it is crucial to understand how ecological networks respond to land-use intensification and the increasing presence of exotic plants. In this study, we analyzed plant-herbivore network data from various locations of the World to ascertain the degree to which land-use intensity and the prevalence of exotic plants induce predictable changes in their network topology - measured by levels of nestedness and modularity - and phylogenetic structures. Additionally, we investigated whether the intimacy of plant-herbivore interactions, contrasting endophagous with exophagous networks, modulate changes in network structure. Our findings reveal that most plant-herbivore networks are characterized by significant phylogenetic and topological structures. However, neither these structures did not show consistent changes in response to increased levels of land-use intensify. On the other hand, for the networks composed of endophagous herbivores, the level of nestedness was higher in the presence of a high proportion of exotic plants. Additionally, for networks of exophagous herbivores, we observed an increase in the phylogenetic structure of interactions due to exotic host dominance. These results underscore the differential impacts of exotic species and land-use intensity on the phylogenetic and topological structures of plant-herbivore networks.
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Affiliation(s)
- Walter Santos de Araújo
- Departamento de Biologia Geral, Centro de Ciências Biológicas e da Saúde, Universidade Estadual de Montes Claros, Montes Claros, MG 39401-089, Brazil..
| | - Leonardo Lima Bergamini
- Centro de Estudos Ambientais do Cerrado, Instituto Brasileiro de Geografia e Estatística, Reserva Ecológica do IBGE, Brasília, DF 70312-970, Brazil
| | - Mário Almeida-Neto
- Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO 74001-970, Brazil
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12
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Raisinghani N, Alshahrani M, Gupta G, Verkhivker G. Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning. Int J Mol Sci 2024; 25:10082. [PMID: 39337567 PMCID: PMC11432724 DOI: 10.3390/ijms251810082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 09/18/2024] [Accepted: 09/18/2024] [Indexed: 09/30/2024] Open
Abstract
Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.
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Affiliation(s)
- Nishank Raisinghani
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Grace Gupta
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
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13
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Wang Y, Miao Y, Fu G, Lu P, Yang Y, Gu W, Fang Z, Niu L. Norm Emergence through Conflict-Blocking Interactions in Industrial Internet of Things Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:6047. [PMID: 39338792 PMCID: PMC11435474 DOI: 10.3390/s24186047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/31/2024] [Accepted: 09/02/2024] [Indexed: 09/30/2024]
Abstract
Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called "conflict-blocking", where agents' conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence.
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Affiliation(s)
- Yuchen Wang
- School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yanqin Miao
- Shaoxing Electric Power Equipment Co., Ltd., Shaoxing 312025, China
| | - Gang Fu
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Peng Lu
- Institute of Computing Innovation, Zhejiang University, Hangzhou 311200, China
| | - Yikun Yang
- School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Wen Gu
- Japan Advanced Institute of Science and Technology, Nomi 923-1292, Japan
| | - Zijie Fang
- School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Lei Niu
- Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
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14
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Yang J, Chen C, Liu Z, Fan Z, Ouyang X, Tao H, Yang J. Subtyping drug-free first-episode major depressive disorder based on cortical surface area alterations. J Affect Disord 2024; 368:100-106. [PMID: 39265867 DOI: 10.1016/j.jad.2024.09.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 09/03/2024] [Accepted: 09/08/2024] [Indexed: 09/14/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is recognized as a complex and heterogeneous metal illness, characterized by diverse clinical symptoms and variable treatment outcomes. Previous studies have repeatedly reported alterations in brain morphology in MDD, but findings vary across sample characteristics. Whether this neurobiological substrate could stratify MDD into more homogeneous clinical subgroups thus improving personalized medicine remains unknown. METHODS We included 65 drug-free patients with first-episode MDD and 66 healthy controls (HCs) and collected their structural MRI data. We performed the surface reconstruction and calculated cortical surface area using Freesurfer. The surface area of 34 Gy matter regions in each hemisphere based on the Desikan-Killiany atlas were extracted for each participant and subtyping results were obtained with the Louvain community detection algorithm. The demographic and clinical characteristics were then compared between MDD subgroups. RESULTS Two subgroups defined by distinct patterns of cortical surface area were identified in first-episode MDD. Subgroup 1 exhibited a significant reduction in surface area across nearly the entire cortex compared to subgroup 2 and HCs, whereas subgroup 2 demonstrated increased surface area than HCs. Further, subgroup 1 exhibited a higher proportion of females, and higher severity of anxiety symptoms compared to subgroup 2. LIMITATIONS The relatively small sample size. CONCLUSIONS This study identified two neurobiologically subgroups with distinct alterations in cortical surface area among drug-free patients with first-episode MDD. Our results highlight the promise of in delineating morphological heterogeneity within MDD, particularly in relation to the severity of anxiety symptoms.
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Affiliation(s)
- Jun Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Chujun Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zebin Fan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xuan Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Haojuan Tao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jie Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China; Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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15
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Khodaei M, McIntyre CC, Kirse HA, Laurienti P. Why graph theory deserves more focus. Comment on "Connectivity analyses for task-based fMRI" by Huang et al. Phys Life Rev 2024; 51:22-23. [PMID: 39260271 DOI: 10.1016/j.plrev.2024.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Accepted: 09/04/2024] [Indexed: 09/13/2024]
Abstract
Huang et al. have conducted a thorough examination of methodologies used for identifying and analyzing functional connectivity using task-based fMRI. Their review adeptly describes current approaches without bias or preference. In this commentary, we explain why we believe that graph theory is the optimal approach for studying neural mechanisms associated with complex behaviors and cognitive processes that are engaged during task-based fMRI.
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Affiliation(s)
- Mohammadreza Khodaei
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, United States.
| | - Clayton C McIntyre
- Neuroscience Graduate Program, Wake Forest University Graduate School of Arts and Sciences, United States
| | - Haley A Kirse
- Integrative Physiology and Pharmacology Graduate Program, Wake Forest University Graduate School of Arts and Sciences, United States
| | - Paul Laurienti
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, United States; Neuroscience Graduate Program, Wake Forest University Graduate School of Arts and Sciences, United States; Department of Radiology, Wake Forest University School of Medicine, United States.
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16
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K M K, N U, S K. Conformational dynamics and ribosomal interactions of Bacillus subtilis Obg in various nucleotide-bound states: Insights from molecular dynamics simulation. Int J Biol Macromol 2024; 279:135337. [PMID: 39241998 DOI: 10.1016/j.ijbiomac.2024.135337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/24/2024] [Accepted: 09/03/2024] [Indexed: 09/09/2024]
Abstract
Obg, a GTPase, binds to the premature 50S ribosomal subunit and facilitates recruitment of rproteins and rRNA processing to form the mature 50S subunit. This binding depends on nucleotide-induced conformational changes (GDP/GTP). However, the mechanism by which Obg undergoes conformational changes to associate with the premature 50S subunit is unknown. Therefore, 1000 ns molecular dynamics simulations were conducted to investigate this mechanism. Visualization of the simulated trajectory showed that in GDP and GTP-bound states, the C-domain moved towards the SwI region, while in GTP-Mg2+ and ppGpp-bound states, the C-domain shifted towards the N-tails. Further, positioning these conformations of Obg on the 50S subunit suggests possible mechanisms by which the GTP-Mg2+ bound state is responsible for recruiting rprotein, as well as the impact of the absence of Mg2+ in the GTP-bound state. Furthermore, the study provides insights into the conformational changes that may lead to the dissociation of the GDP-bound state from the 50S subunit and explores the potential role of the ppGpp-bound state in inhibiting 70S ribosome formation. Additionally, RMSF and community network analyses reveal how internal dynamics and intricate connections within Obg affect C-domain motion.
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Affiliation(s)
- Kavya K M
- Department of Studies in Physics, University of Mysore, Mysuru, India.
| | - Upendra N
- Center for Research and Innovations, Faculty of Natural Sciences, Adichunchanagiri University, B.G.Nagara, India.
| | - Krishnaveni S
- Department of Studies in Physics, University of Mysore, Mysuru, India.
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17
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Sassenberg TA, Safron A, DeYoung CG. Stable individual differences from dynamic patterns of function: brain network flexibility predicts openness/intellect, intelligence, and psychoticism. Cereb Cortex 2024; 34:bhae391. [PMID: 39329360 DOI: 10.1093/cercor/bhae391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 09/06/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
A growing understanding of the nature of brain function has led to increased interest in interpreting the properties of large-scale brain networks. Methodological advances in network neuroscience provide means to decompose these networks into smaller functional communities and measure how they reconfigure over time as an index of their dynamic and flexible properties. Recent evidence has identified associations between flexibility and a variety of traits pertaining to complex cognition including creativity and working memory. The present study used measures of dynamic resting-state functional connectivity in data from the Human Connectome Project (n = 994) to test associations with Openness/Intellect, general intelligence, and psychoticism, three traits that involve flexible cognition. Using a machine-learning cross-validation approach, we identified reliable associations of intelligence with cohesive flexibility of parcels in large communities across the cortex, of psychoticism with disjoint flexibility, and of Openness/Intellect with overall flexibility among parcels in smaller communities. These findings are reasonably consistent with previous theories of the neural correlates of these traits and help to expand on previous associations of behavior with dynamic functional connectivity, in the context of broad personality dimensions.
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Affiliation(s)
- Tyler A Sassenberg
- Department of Psychology, University of Minnesota, 75 East River Parkway, Minneapolis, MN 55455, United States
| | - Adam Safron
- Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD 21224, United States
- Institute for Advanced Consciousness Studies, 2811 Wilshire Boulevard, Santa Monica, CA 90403, United States
- Cognitive Science Program, Indiana University, 1001 East 10th Street, Bloomington, IN 47405, United States
- Kinsey Institute, Indiana University, 150 South Woodlawn Avenue, Bloomington, IN 47405, United States
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, 75 East River Parkway, Minneapolis, MN 55455, United States
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18
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Barcenas M, Bocci F, Nie Q. Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data. Biophys J 2024; 123:2849-2859. [PMID: 38504523 PMCID: PMC11393678 DOI: 10.1016/j.bpj.2024.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/09/2024] [Accepted: 03/15/2024] [Indexed: 03/21/2024] Open
Abstract
Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high-resolution single-cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to "tipping points" whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single-cell transcriptomics data to infer the stability and gene regulatory networks (GRNs) along cell lineages. Our method uses the unspliced and spliced counts from single-cell RNA sequencing data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell-state stability along the lineage is inferred based on spectral analysis of the model's Jacobian matrix. Moreover, the model infers the RNA cross-species interactions resulting in GRNs and their variation along the cell lineage. When applied to epithelial-mesenchymal transition in ovarian and lung cancer-derived cell lines, our model predicts a saddle-node transition between the epithelial and mesenchymal states passing through an unstable, intermediate cell state. Furthermore, we show that the underlying GRN controlling epithelial-mesenchymal transition rearranges during the transition, resulting in denser and less modular networks in the intermediate state. Overall, our method represents a flexible tool to study cell lineages with a combination of theory-driven modeling and single-cell transcriptomics data.
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Affiliation(s)
- Manuel Barcenas
- Department of Mathematics, University of California Irvine, Irvine, California
| | - Federico Bocci
- Department of Mathematics, University of California Irvine, Irvine, California; NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, California.
| | - Qing Nie
- Department of Mathematics, University of California Irvine, Irvine, California; NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, California.
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Luo L, Nian F, Cui Y, Li F. Fractal information dissemination and clustering evolution on social hypernetwork. CHAOS (WOODBURY, N.Y.) 2024; 34:093128. [PMID: 39298338 DOI: 10.1063/5.0228903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 08/29/2024] [Indexed: 09/21/2024]
Abstract
The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of "key groups" with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks.
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Affiliation(s)
- Li Luo
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
| | - Fuzhong Nian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
| | - Yuanlin Cui
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
| | - Fangfang Li
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
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20
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Rao RM, El Dhaybi I, Cadet F, Etchebest C, Diharce J. The mutual and dynamic role of TSPO and ligands in their binding process: An example with PK-11195. Biochimie 2024; 224:29-40. [PMID: 38494108 DOI: 10.1016/j.biochi.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/12/2024] [Accepted: 03/15/2024] [Indexed: 03/19/2024]
Abstract
Translocator protein (TSPO) is an 18 kDa transmembrane protein, localized primarily on the outer mitochondrial membrane. It has been found to be involved in various physiological processes and pathophysiological conditions. Though studies on its structure have been performed only recently, there is little information on the nature of dynamics and doubts about some structures referenced in the literature, especially the NMR structure of mouse TSPO. In the present work, we thoroughly study the dynamics of mouse TSPO protein by means of atomistic molecular dynamics simulations, in presence as well as in absence of the diagnostic ligand PKA. We considered two starting structures: the NMR structure and a homology model (HM) generated on the basis of X-ray structures from bacterial TSPO. We examine the conformational landscape in both the modes for both starting points, in presence and absence of the ligand, in order to measure its impact for both structures. The analysis highlights high flexibility of the protein globally, but NMR simulations show a surprisingly flexibility even in the presence of the ligand. Interestingly, this is not the case for HM calculations, to the point that the ligand seems not so stable as in the NMR system and an unbinding event process is partially sampled. All those results tend to show that the NMR structure of mTSPO seems not deficient but is just in another portion of the global conformation space of TSPO.
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Affiliation(s)
- Rajas M Rao
- Data Analytics, Bioinformatics and Structural Biology Division, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India; Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB UMR_S1134, F-74014, Paris, France
| | - Ibaa El Dhaybi
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB UMR_S1134, F-74014, Paris, France
| | - Frédéric Cadet
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB UMR_S1134, F-74014, Paris, France; Laboratory of Excellence GR-Ex, Paris, France; Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, F-97715, Saint Denis Messag, France; PEACCEL, Artificial Intelligence Department, Paris, 75013 France
| | - Catherine Etchebest
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB UMR_S1134, F-74014, Paris, France; Laboratory of Excellence GR-Ex, Paris, France
| | - Julien Diharce
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB UMR_S1134, F-74014, Paris, France; Laboratory of Excellence GR-Ex, Paris, France.
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21
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Wang Y, Li A, Wang L. Networked dynamic systems with higher-order interactions: stability versus complexity. Natl Sci Rev 2024; 11:nwae103. [PMID: 39144749 PMCID: PMC11321256 DOI: 10.1093/nsr/nwae103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/20/2024] [Accepted: 03/07/2024] [Indexed: 08/16/2024] Open
Abstract
The stability of complex systems is profoundly affected by underlying structures, which are often modeled as networks where nodes indicate system components and edges indicate pairwise interactions between nodes. However, such networks cannot encode the overall complexity of networked systems with higher-order interactions among more than two nodes. Set structures provide a natural description of pairwise and higher-order interactions where nodes are grouped into multiple sets based on their shared traits. Here we derive the stability criteria for networked systems with higher-order interactions by employing set structures. In particular, we provide a simple rule showing that the higher-order interactions play a double-sided role in community stability-networked systems with set structures are stabilized if the expected number of common sets for any two nodes is less than one. Moreover, although previous knowledge suggests that more interactions (i.e. complexity) destabilize networked systems, we report that, with higher-order interactions, networked systems can be stabilized by forming more local sets. Our findings are robust with respect to degree heterogeneous structures, diverse equilibrium states and interaction types.
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Affiliation(s)
- Ye Wang
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing 100871, China
| | - Long Wang
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing 100871, China
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22
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Jardim LL, Schieber TA, Santana MP, Cerqueira MH, Lorenzato CS, Franco VKB, Zuccherato LW, da Silva Santos BA, Chaves DG, Ravetti MG, Rezende SM. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network. J Thromb Haemost 2024; 22:2426-2437. [PMID: 38810700 DOI: 10.1016/j.jtha.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/02/2024] [Accepted: 05/12/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. OBJECTIVES To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.
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Affiliation(s)
- Letícia Lemos Jardim
- Instituto René Rachou (Fiocruz Minas), Belo Horizonte, Minas Gerais, Brazil; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Tiago A Schieber
- Faculdade de Ciências Econômicas, School of Economics, Universidade Federal de Minas Gerais, Brazil
| | | | | | | | | | | | | | | | - Martín Gomez Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Suely Meireles Rezende
- Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
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Qian J, Yang B, Wang S, Yuan S, Zhu W, Zhou Z, Zhang Y, Hu G. Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders. Int J Mol Sci 2024; 25:8917. [PMID: 39201608 PMCID: PMC11354300 DOI: 10.3390/ijms25168917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/06/2024] [Accepted: 08/14/2024] [Indexed: 09/02/2024] Open
Abstract
In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.
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Affiliation(s)
- Jing Qian
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
| | - Bin Yang
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
| | - Shuo Wang
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
| | - Su Yuan
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
| | - Wenjing Zhu
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
| | - Ziyun Zhou
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
| | - Yujuan Zhang
- Experimental Center of Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Guang Hu
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China; (J.Q.); (S.W.)
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
- Key Laboratory of Alkene-Carbon Fibres-Based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou 215123, China
- Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou 215123, China
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24
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DeGroat W, Inoue F, Ashuach T, Yosef N, Ahituv N, Kreimer A. Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation. Genome Biol 2024; 25:221. [PMID: 39143563 PMCID: PMC11323586 DOI: 10.1186/s13059-024-03365-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 08/01/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases. RESULTS We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays. CONCLUSIONS Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.
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Affiliation(s)
- William DeGroat
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ, 08854, USA
| | - Fumitaka Inoue
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Tal Ashuach
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, 387 Soda Hall, Berkeley, CA, 94720, USA
| | - Nir Yosef
- Department of Systems Immunology, Weizmann Institute of Science, 234 Herzl Street, Rehovot, 7610001, Israel
- Chan-Zuckerberg Biohub, 499 Illinois St, San Francisco, CA, 94158, USA
- Department of Systems Immunology, Ragon Institute of MGH, MIT, and Harvard Institute of Science, 400 Technology Square, Cambridge, MA, 02139, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, 513 Parnassus Ave, San Francisco, CA, 94143, USA
- Institute for Human Genetics, University of California, 513 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Anat Kreimer
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ, 08854, USA.
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ, 08854, USA.
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25
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Pickard J, Stansbury C, Surana A, Muir L, Bloch A, Rajapakse I. Biomarker Selection for Adaptive Systems. ARXIV 2024:arXiv:2405.09809v3. [PMID: 38827457 PMCID: PMC11142321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Biomarkers enable objective monitoring of a given cell or state in a biological system and are widely used in research, biomanufacturing, and clinical practice. However, identifying appropriate biomarkers that are both robustly measurable and capture a state accurately remains challenging. We present a framework for biomarker identification based upon observability guided sensor selection. Our methods, Dynamic Sensor Selection (DSS) and Structure-Guided Sensor Selection (SGSS), utilize temporal models and experimental data, offering a template for applying observability theory to data from biological systems. Unlike conventional methods that assume well-known, fixed dynamics, DSS adaptively select biomarkers or sensors that maximize observability while accounting for the time-varying nature of biological systems. Additionally, SGSS incorporates structural information and diverse data to identify sensors which are resilient against inaccuracies in our model of the underlying system. We validate our approaches by performing estimation on high dimensional systems derived from temporal gene expression data from partial observations. Our algorithms reliably identify known biomarkers and uncover new ones within our datasets. Additionally, integrating chromosome conformation and gene expression data addresses noise and uncertainty, enhancing the reliability of our biomarker selection approach for the genome.
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Affiliation(s)
- Joshua Pickard
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109
| | - Cooper Stansbury
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109
| | - Amit Surana
- RTX Technology Research Center, East Hartford, CT 06108
| | - Lindsey Muir
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109
| | - Anthony Bloch
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
| | - Indika Rajapakse
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109
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26
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Wang X, Yen K, Hu Y, Shen HW. SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:5666-5678. [PMID: 37594870 DOI: 10.1109/tvcg.2023.3306356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/20/2023]
Abstract
While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-optimal results. There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently. These methods have demonstrated the advantages of deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called SmartGD, which can optimize different quantitative aesthetic goals, regardless of their differentiability. To demonstrate the effectiveness and efficiency of SmartGD, we conducted experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, maximizing shape-based metrics, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that SmartGD achieves good performance both quantitatively and qualitatively.
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27
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Pires DL, Broom M. The rules of multiplayer cooperation in networks of communities. PLoS Comput Biol 2024; 20:e1012388. [PMID: 39159235 PMCID: PMC11361752 DOI: 10.1371/journal.pcbi.1012388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 08/29/2024] [Accepted: 08/05/2024] [Indexed: 08/21/2024] Open
Abstract
Community organisation permeates both social and biological complex systems. To study its interplay with behaviour emergence, we model mobile structured populations with multiplayer interactions. We derive general analytical methods for evolutionary dynamics under high home fidelity when populations self-organise into networks of asymptotically isolated communities. In this limit, community organisation dominates over the network structure and emerging behaviour is independent of network topology. We obtain the rules of multiplayer cooperation in networks of communities for different types of social dilemmas. The success of cooperation is a result of the benefits shared among communal cooperators outperforming the benefits reaped by defectors in mixed communities. Under weak selection, cooperation can evolve and be stable for any size (Q) and number (M) of communities if the reward-to-cost ratio (V/K) of public goods is higher than a critical value. Community organisation is a solid mechanism for sustaining the evolution of cooperation under public goods dilemmas, particularly when populations are organised into a higher number of smaller communities. Contrary to public goods dilemmas relating to production, the multiplayer Hawk-Dove (HD) dilemma is a commons dilemma focusing on the fair consumption of preexisting resources. This game yields mixed results but tends to favour cooperation under larger communities, highlighting that the two types of social dilemmas might lead to solid differences in the behaviour adopted under community structure.
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Affiliation(s)
- Diogo L. Pires
- Department of Mathematics, City, University of London, Northampton Square, London, United Kingdom
| | - Mark Broom
- Department of Mathematics, City, University of London, Northampton Square, London, United Kingdom
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28
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Wang B, He J, Meng Q. Detection of minimal extended driver nodes in energetic costs reduction. CHAOS (WOODBURY, N.Y.) 2024; 34:083122. [PMID: 39146454 DOI: 10.1063/5.0214746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 07/29/2024] [Indexed: 08/17/2024]
Abstract
Structures of complex networks are fundamental to system dynamics, where node state and connectivity patterns determine the cost of a control system, a key aspect in unraveling complexity. However, minimizing the energy required to control a system with the fewest input nodes remains an open problem. This study investigates the relationship between the structure of closed-connected function modules and control energy. We discovered that small structural adjustments, such as adding a few extended driver nodes, can significantly reduce control energy. Thus, we propose MInimal extended driver nodes in Energetic costs Reduction (MIER). Next, we transform the detection of MIER into a multi-objective optimization problem and choose an NSGA-II algorithm to solve it. Compared with the baseline methods, NSGA-II can approximate the optimal solution to the greatest extent. Through experiments using synthetic and real data, we validate that MIER can exponentially decrease control energy. Furthermore, random perturbation tests confirm the stability of MIER. Subsequently, we applied MIER to three representative scenarios: regulation of differential expression genes affected by cancer mutations in the human protein-protein interaction network, trade relations among developed countries in the world trade network, and regulation of body-wall muscle cells by motor neurons in Caenorhabditis elegans nervous network. The results reveal that the involvement of MIER significantly reduces control energy required for these original modules from a topological perspective. Additionally, MIER nodes enhance functionality, supplement key nodes, and uncover potential mechanisms. Overall, our work provides practical computational tools for understanding and presenting control strategies in biological, social, and neural systems.
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Affiliation(s)
- Bingbo Wang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Jiaojiao He
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Qingdou Meng
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
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29
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Griffith LE, Brini A, Muniz-Terrera G, St John PD, Stirland LE, Mayhew A, Oyarzún D, van den Heuvel E. A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging. J Clin Epidemiol 2024; 172:111435. [PMID: 38901709 DOI: 10.1016/j.jclinepi.2024.111435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVES To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters. STUDY DESIGN AND SETTING Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians. RESULTS Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity. CONCLUSION These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.
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Affiliation(s)
- Lauren E Griffith
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada.
| | - Alberto Brini
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Philip D St John
- Section of Geriatric Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lucy E Stirland
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, UK; Global Brain Health Institute, University of California, San Francisco, CA, USA
| | - Alexandra Mayhew
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Diego Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, UK; School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Edwin van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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30
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Kovács B, Kojaku S, Palla G, Fortunato S. Iterative embedding and reweighting of complex networks reveals community structure. Sci Rep 2024; 14:17184. [PMID: 39060433 PMCID: PMC11282304 DOI: 10.1038/s41598-024-68152-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024] Open
Abstract
Graph embeddings learn the structure of networks and represent it in low-dimensional vector spaces. Community structure is one of the features that are recognized and reproduced by embeddings. We show that an iterative procedure, in which a graph is repeatedly embedded and its links are reweighted based on the geometric proximity between the nodes, reinforces intra-community links and weakens inter-community links, making the clusters of the initial network more visible and more easily detectable. The geometric separation between the communities can become so strong that even a very simple parsing of the links may recover the communities as isolated components with surprisingly high precision. Furthermore, when used as a pre-processing step, our embedding and reweighting procedure can improve the performance of traditional community detection algorithms.
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Affiliation(s)
- Bianka Kovács
- Department of Biological Physics, Eötvös Loránd University, Budapest, Pázmány P. stny. 1/A, 1117, Hungary
| | - Sadamori Kojaku
- Luddy School of Informatics, Computing, and Engineering, Indiana University, 1015 East 11th Street, Bloomington, IN, 47408, USA
- Department of Systems Science and Industrial Engineering, SUNY Binghamton, P.O. Box 6000, Binghamton, NY, 13902, USA
| | - Gergely Palla
- Department of Biological Physics, Eötvös Loránd University, Budapest, Pázmány P. stny. 1/A, 1117, Hungary.
- Health Services Management Training Centre, Semmelweis University, Budapest, Kútvölgyi út 2., 1125, Hungary.
| | - Santo Fortunato
- Luddy School of Informatics, Computing, and Engineering, Indiana University, 1015 East 11th Street, Bloomington, IN, 47408, USA
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31
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Richter M, Penny MA, Shattock AJ. Intervention effect of targeted workplace closures may be approximated by single-layered networks in an individual-based model of COVID-19 control. Sci Rep 2024; 14:17202. [PMID: 39060272 PMCID: PMC11282285 DOI: 10.1038/s41598-024-66741-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
Individual-based models of infectious disease dynamics commonly use network structures to represent human interactions. Network structures can vary in complexity, from single-layered with homogeneous mixing to multi-layered with clustering and layer-specific contact weights. Here we assessed policy-relevant consequences of network choice by simulating different network structures within an established individual-based model of SARS-CoV-2 dynamics. We determined the clustering coefficient of each network structure and compared this to several epidemiological outcomes, such as cumulative and peak infections. High-clustered networks estimate fewer cumulative infections and peak infections than less-clustered networks when transmission probabilities are equal. However, by altering transmission probabilities, we find that high-clustered networks can essentially recover the dynamics of low-clustered networks. We further assessed the effect of workplace closures as a layer-targeted intervention on epidemiological outcomes and found in this scenario a single-layered network provides a sufficient approximation of intervention effect relative to a multi-layered network when layer-specific contact weightings are equal. Overall, network structure choice within models should consider the knowledge of contact weights in different environments and pathogen mode of transmission to avoid over- or under-estimating disease burden and impact of interventions.
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Affiliation(s)
- Maximilian Richter
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Melissa A Penny
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Telethon Kids Institute, Nedlands, WA, Australia
- Centre for Child Health Research, University of Western Australia, Crawley, WA, Australia
| | - Andrew J Shattock
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
- Telethon Kids Institute, Nedlands, WA, Australia.
- Centre for Child Health Research, University of Western Australia, Crawley, WA, Australia.
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32
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Liu Q, Yu Y, Wei G. Oncogenic R248W mutation induced conformational perturbation of the p53 core domain and the structural protection by proteomimetic amyloid inhibitor ADH-6. Phys Chem Chem Phys 2024; 26:20068-20086. [PMID: 39007865 DOI: 10.1039/d4cp02046d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
The involvement of p53 aggregation in cancer pathogenesis emphasizes the importance of unraveling the mechanisms underlying mutation-induced p53 destabilization. And understanding how small molecule inhibitors prevent the conversion of p53 into aggregation-primed conformations is pivotal for the development of therapeutics targeting p53-aggregation-associated cancers. A recent experimental study highlights the efficacy of the proteomimetic amyloid inhibitor ADH-6 in stabilizing R248W p53 and inhibiting its aggregation in cancer cells by interacting with the p53 core domain (p53C). However, it remains mostly unclear how R248W mutation induces destabilization of p53C and how ADH-6 stabilizes this p53C mutant and inhibits its aggregation. Herein, we conducted all-atom molecular dynamics simulations of R248W p53C in the absence and presence of ADH-6, as well as that of wild-type (WT) p53C. Our simulations reveal that the R248W mutation results in a shift of helix H2 and β-hairpin S2-S2' towards the mutation site, leading to the destruction of their neighboring β-sheet structure. This further facilitates the formation of a cavity in the hydrophobic core, and reduces the stability of the β-sandwich. Importantly, two crucial aggregation-prone regions (APRs) S9 and S10 are disturbed and more exposed to solvent in R248W p53C, which is conducive to p53C aggregation. Intriguingly, ADH-6 dynamically binds to the mutation site and multiple destabilized regions in R248W p53C, partially inhibiting the shift of helix H2 and β-hairpin S2-S2', thus preventing the disruption of the β-sheets and the formation of the cavity. ADH-6 also reduces the solvent exposure of APRs S9 and S10, which disfavors the aggregation of R248W p53C. Moreover, ADH-6 can preserve the WT-like dynamical network of R248W p53C. Our study elucidates the mechanisms underlying the oncogenic R248W mutation induced p53C destabilization and the structural protection of p53C by ADH-6.
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Affiliation(s)
- Qian Liu
- Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Fudan University, Shanghai 200438, People's Republic of China.
| | - Yawei Yu
- Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Fudan University, Shanghai 200438, People's Republic of China.
| | - Guanghong Wei
- Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Fudan University, Shanghai 200438, People's Republic of China.
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33
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Ojwang’ AME, Lloyd AL, Bhattacharyya S, Chatterjee S, Gent DH, Ojiambo PS. Identifying highly connected sites for risk-based surveillance and control of cucurbit downy mildew in the eastern United States. PeerJ 2024; 12:e17649. [PMID: 39056053 PMCID: PMC11271662 DOI: 10.7717/peerj.17649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 06/06/2024] [Indexed: 07/28/2024] Open
Abstract
Objective Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread. Methods Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years. Results Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model. Conclusions Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States.
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Affiliation(s)
- Awino M. E. Ojwang’
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, United States
| | - Alun L. Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC, United States
| | | | - Shirshendu Chatterjee
- Department of Mathematics, City University of New York, City College, New York, NY, United States
| | - David H. Gent
- Agricultural Research Service, U.S. Department of Agriculture, Corvallis, OR, United States
| | - Peter S. Ojiambo
- Center for Integrated Fungal Research, Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, United States
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34
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Sheng Y, Wang Y, Wang X, Zhang Z, Zhu D, Zheng W. No sex difference in maturation of brain morphology during the perinatal period. Brain Struct Funct 2024:10.1007/s00429-024-02828-x. [PMID: 39020216 DOI: 10.1007/s00429-024-02828-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024]
Abstract
Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains.
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Affiliation(s)
- Yucen Sheng
- School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou, People's Republic of China
| | - Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, People's Republic of China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital Lanzhou, Lanzhou, People's Republic of China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
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Woo KS, Park H, Ghenzi N, Talin AA, Jeong T, Choi JH, Oh S, Jang YH, Han J, Williams RS, Kumar S, Hwang CS. Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing. ACS NANO 2024; 18:17007-17017. [PMID: 38952324 DOI: 10.1021/acsnano.4c03238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.
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Affiliation(s)
- Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Sandia National Laboratories, Livermore, California 94551, United States
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Hyungjun Park
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Nestor Ghenzi
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Universidad de Avellaneda UNDAV and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mario Bravo 1460, Avellaneda, Buenos Aires 1872, Argentina
| | - A Alec Talin
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Taeyoung Jeong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Electronic Materials Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Jung-Hae Choi
- Electronic Materials Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Sangheon Oh
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - R Stanley Williams
- Sandia National Laboratories, Livermore, California 94551, United States
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Suhas Kumar
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
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Giacomo ED, Didimo W, Liotta G, Montecchiani F, Tappini A. Comparative Study and Evaluation of Hybrid Visualizations of Graphs. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:3503-3515. [PMID: 37018276 DOI: 10.1109/tvcg.2022.3233389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Hybrid visualizations combine different metaphors into a single network layout, in order to help humans in finding the "right way" of displaying the different portions of the network, especially when it is globally sparse and locally dense. We investigate hybrid visualizations in two complementary directions: (i) On the one hand, we evaluate the effectiveness of different hybrid visualization models through a comparative user study; (ii) On the other hand, we estimate the usefulness of an interactive visualization that integrates all the considered hybrid models together. The results of our study provide some hints about the usefulness of the different hybrid visualizations for specific tasks of analysis and indicates that integrating different hybrid models into a single visualization may offer a valuable tool of analysis.
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Pechlivanis N, Karakatsoulis G, Kyritsis K, Tsagiopoulou M, Sgardelis S, Kappas I, Psomopoulos F. Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity. Sci Data 2024; 11:672. [PMID: 38909071 PMCID: PMC11193810 DOI: 10.1038/s41597-024-03528-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/14/2024] [Indexed: 06/24/2024] Open
Abstract
Despite recent research efforts to explore the co-occurrence patterns of diverse microbes within soil microbial communities, a substantial knowledge-gap persists regarding global climate influences on soil microbiota behaviour. Comprehending co-occurrence patterns within distinct geoclimatic groups is pivotal for unravelling the ecological structure of microbial communities, that are crucial for preserving ecosystem functions and services. Our study addresses this gap by examining global climatic patterns of microbial diversity. Using data from the Earth Microbiome Project, we analyse a meta-community co-occurrence network for bacterial communities. This method unveils substantial shifts in topological features, highlighting regional and climatic trends. Arid, Polar, and Tropical zones show lower diversity but maintain denser networks, whereas Temperate and Cold zones display higher diversity alongside more modular networks. Furthermore, it identifies significant co-occurrence patterns across diverse climatic regions. Central taxa associated with different climates are pinpointed, highlighting climate's pivotal role in community structure. In conclusion, our study identifies significant correlations between microbial interactions in diverse climatic regions, contributing valuable insights into the intricate dynamics of soil microbiota.
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Affiliation(s)
- Nikos Pechlivanis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001, Thessaloniki, Greece
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Georgios Karakatsoulis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001, Thessaloniki, Greece
| | - Konstantinos Kyritsis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001, Thessaloniki, Greece
| | - Maria Tsagiopoulou
- Centro Nacional de Analisis Genomico (CNAG), C/Baldiri Reixac 4, 08028, Barcelona, Spain
| | - Stefanos Sgardelis
- Department of Ecology, School of Biology, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Ilias Kappas
- Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Fotis Psomopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001, Thessaloniki, Greece.
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Cherepanov S, Heitzmann L, Fontanaud P, Guillou A, Galibert E, Campos P, Mollard P, Martin AO. Prolactin blood concentration relies on the scalability of the TIDA neurons' network efficiency in vivo. iScience 2024; 27:109876. [PMID: 38799572 PMCID: PMC11126972 DOI: 10.1016/j.isci.2024.109876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/09/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Our understanding and management of reproductive health and related disorders such as infertility, menstrual irregularities, and pituitary disorders depend on understanding the intricate sex-specific mechanisms governing prolactin secretion. Using ex vivo experiments in acute slices, in parallel with in vivo calcium imaging (GRIN lens technology), we found that dopamine neurons inhibiting PRL secretion (TIDA), organize as functional networks both in and ex vivo. We defined an index of efficiency of networking (Ieff) using the duration of calcium events and the ability to form plastic economic networks. It determined TIDA neurons' ability to inhibit PRL secretion in vivo. Ieff variations in both sexes demonstrated TIDA neurons' adaptability to physiological changes. A variation in the number of active neurons contributing to the network explains the sexual dimorphism in basal [PRL]blood secretion patterns. These sex-specific differences in neuronal activity and network organization contribute to the understanding of hormone regulation.
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Affiliation(s)
- Stanislav Cherepanov
- Team for networks and rhythms in endocrine glands. Institute of Functional Genomics, CNRS, INSERM. Montpellier, 34094 Occitanie, France
| | - Louise Heitzmann
- Sex and speciation team, department of genome, phenome and environment. Montpellier Institute of Evolution Science, CNRS. Montpellier, 34090 Occitanie, France
| | - Pierre Fontanaud
- Team for networks and rhythms in endocrine glands. Institute of Functional Genomics, CNRS, INSERM. Montpellier, 34094 Occitanie, France
| | - Anne Guillou
- Team for networks and rhythms in endocrine glands. Institute of Functional Genomics, CNRS, INSERM. Montpellier, 34094 Occitanie, France
| | - Evelyne Galibert
- Team for networks and rhythms in endocrine glands. Institute of Functional Genomics, CNRS, INSERM. Montpellier, 34094 Occitanie, France
| | - Pauline Campos
- Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
| | - Patrice Mollard
- Team for networks and rhythms in endocrine glands. Institute of Functional Genomics, CNRS, INSERM. Montpellier, 34094 Occitanie, France
| | - Agnès O. Martin
- Team for networks and rhythms in endocrine glands. Institute of Functional Genomics, CNRS, INSERM. Montpellier, 34094 Occitanie, France
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Hu Q, An S, Kapucu N, Sellnow T, Yuksel M, Freihaut R, Dey PK. Emergency communication networks on Twitter during Hurricane Irma: information flow, influential actors, and top messages. DISASTERS 2024:e12628. [PMID: 38872615 DOI: 10.1111/disa.12628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/25/2024] [Indexed: 06/15/2024]
Abstract
This study combined network analysis with message-level content analysis to investigate patterns of information flow and to examine messages widely distributed on social media during Hurricane Irma of 2017. The results show that while organisational users and media professionals dominated the top 100 information sources, individual citizens played a critical role in information dissemination. Public agencies should increase their retweeting activities and share the information posted by other trustworthy sources; doing so will contribute to the timely exchange of vital information during a disaster. This study also identified the active involvement of nonprofit organisations as information brokers during the post-event stage, indicating the potential for emergency management organisations to integrate their communication efforts into those of nonprofit entities. These findings will inform emergency management practices regarding implementation of communication plans and policies, facilitate the embracement of new partner organisations, and help with establishing and sustaining effective communication ties with a wide range of stakeholders.
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Affiliation(s)
- Qian Hu
- Professor at the Schar School of Policy and Government, George Mason University, United States
| | - Seongho An
- Assistant Professor at the School of Public Administration, University of Central Florida, United States
| | - Naim Kapucu
- Pegasus Professor at the School of Public Administration and School of Politics, Security, and International Affairs, University of Central Florida, United States
| | - Timothy Sellnow
- Professor of Communication at the Department of Communication, Clemson University, United States
| | - Murat Yuksel
- Professor at the Department of Electrical Engineering and Computer Science, University of Central Florida, United States
| | - Rebecca Freihaut
- A doctoral student at the Nicholson School of Communication and Media, University of Central Florida, United States
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40
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Lalit F, Jose AM. Selecting genes for analysis using historically contingent progress: from RNA changes to protein-protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592119. [PMID: 38746289 PMCID: PMC11092662 DOI: 10.1101/2024.05.01.592119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Progress in biology has generated numerous lists of genes that share some property. But, advancing from these lists of genes to understanding their roles is slow and unsystematic. Here we use RNA silencing in C. elegans to illustrate an approach for prioritizing genes for detailed study given limited resources. The partially subjective relationships between genes forged by both deduced functional relatedness and biased progress in the field was captured as mutual information and used to cluster genes that were frequently identified yet remain understudied. Studied genes in these clusters suggest regulatory links connecting RNA silencing with other processes like the cell cycle. Many proteins encoded by the understudied genes are predicted to physically interact with known regulators of RNA silencing. These predicted influencers of RNA-regulated expression could be used for feedback regulation, which is essential for the homeostasis observed in all living systems. Thus, among the gene products altered when a process is perturbed are regulators of that process, providing a way to use RNA sequencing to identify candidate protein-protein interactions. Together, the analysis of perturbed transcripts and potential interactions of the proteins they encode could help prioritize candidate regulators of any process.
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41
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Widder S, Carmody LA, Opron K, Kalikin LM, Caverly LJ, LiPuma JJ. Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis. Nat Commun 2024; 15:4889. [PMID: 38849369 PMCID: PMC11161516 DOI: 10.1038/s41467-024-49150-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.
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Affiliation(s)
- Stefanie Widder
- Department of Medicine 1, Research Division Infection Biology, Medical University of Vienna, 1090, Vienna, Austria.
| | - Lisa A Carmody
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Kristopher Opron
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Linda M Kalikin
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Lindsay J Caverly
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - John J LiPuma
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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42
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Wang S, Li T, He H, Li Y. Dynamical changes of interaction across functional brain communities during propofol-induced sedation. Cereb Cortex 2024; 34:bhae263. [PMID: 38918077 DOI: 10.1093/cercor/bhae263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.
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Affiliation(s)
- Shengpei Wang
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
| | - Tianzuo Li
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, No. 10 Yangfangdian Tieyi Rd, Haidian District, Beijing 100038, PR China
| | - Huiguang He
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 1 Yanqihu East Road, Huairou District, Beijing 101408, PR China
| | - Yun Li
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, South Fourth Ring West Road, Fengtai District, Beijing 100070, PR China
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43
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Häusler S. Correlations reveal the hierarchical organization of biological networks with latent variables. Commun Biol 2024; 7:678. [PMID: 38831002 PMCID: PMC11148204 DOI: 10.1038/s42003-024-06342-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 05/16/2024] [Indexed: 06/05/2024] Open
Abstract
Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.
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Affiliation(s)
- Stefan Häusler
- Faculty of Biology and Bernstein Center for Computational Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany.
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44
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Wang X, Matone M, Garcia SM, Kellom KS, Marshall D, Ugarte A, Nyachogo M, Bristow S, Cronholm PF. A Social Network Analysis of a Multi-sector Service System for Intimate Partner Violence in a Large US City. JOURNAL OF PREVENTION (2022) 2024; 45:357-376. [PMID: 38431922 PMCID: PMC11033228 DOI: 10.1007/s10935-024-00774-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
About one in four women in the US report having experienced some form of intimate partner violence (IPV) during their lifetime and an estimated 15.5 million children live in families in which IPV occurred in the past year. Families of young children with IPV experiences often face complex needs and require well-coordinated efforts among service providers across social and health sectors. One promising partnership aims to support pregnant and parenting IPV survivors through coordination between IPV agencies and community-based maternal and early childhood home visiting programs. This study used social network analysis (SNA) to understand the interconnectedness of the system of IPV prevention and intervention for families with young children in a large US city. The SNA included 43 agencies serving this population across various service domains spanning IPV, legal, maternal and child health, and public benefit programs. An SNA survey collected data on four forms of collaboration between agencies, including formal administrative relationship, referral reciprocity, case consultation, and shared activities in community committees/organizing bodies. Density and centrality were the primary outcomes of interest. A community detection analysis was performed as a secondary analysis. The overall level of interconnectedness between the 43 responding agencies was low. Making referrals to each other was the most common form of collaboration, with a network density of 30%. IPV agencies had the highest average number of connections in the networks. There was a high level of variation in external collaborations among home visiting agencies, with several home visiting agencies having very few connections in the community but one home visiting program endorsing collaborative relationships with upwards of 38 partner agencies in the network. In serving families at risk for IPV, home visiting agencies were most likely to have referral relationships with mental health provider agencies and substance use disorder service agencies. A community detection analysis identified distinct communities within the network and demonstrated that certain agency types were more connected to one another while others were typically siloed within the network. Notably, the IPV and home visiting communities infrequently overlapped. Sensitivity analyses showed that survey participants' knowledge of their agencies' external collaborations varied by their work roles and agencies overall had low levels of consensus about their connectedness to one another. We identified a heterogeneous service system available to families of young children at-risk for or experiencing IPV. Overall inter-agency connectedness was low, with many siloed agencies and a lack of shared knowledge of community resources. Understanding current collaborations, silos, and centrality of agencies is an effective public health tool for allocating scarce resources across diverse service sectors to efficiently improve the system serving families experiencing IPV.
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Affiliation(s)
- Xi Wang
- PolicyLab, Children's Hospital of Philadelphia, 2716 South Street, 10-121, Philadelphia, PA, 19146, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Meredith Matone
- PolicyLab, Children's Hospital of Philadelphia, 2716 South Street, 10-121, Philadelphia, PA, 19146, USA.
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA.
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Stephanie M Garcia
- PolicyLab, Children's Hospital of Philadelphia, 2716 South Street, 10-121, Philadelphia, PA, 19146, USA
| | - Katherine S Kellom
- PolicyLab, Children's Hospital of Philadelphia, 2716 South Street, 10-121, Philadelphia, PA, 19146, USA
| | - Deanna Marshall
- PolicyLab, Children's Hospital of Philadelphia, 2716 South Street, 10-121, Philadelphia, PA, 19146, USA
| | - Azucena Ugarte
- Office of Domestic Violence Strategies of the City of Philadelphia, Philadelphia, PA, USA
| | | | | | - Peter F Cronholm
- Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia, PA, USA
- Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
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45
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Chen B, Hou G, Li A. Temporal local clustering coefficient uncovers the hidden pattern in temporal networks. Phys Rev E 2024; 109:064302. [PMID: 39020959 DOI: 10.1103/physreve.109.064302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 05/07/2024] [Indexed: 07/20/2024]
Abstract
Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks. We demonstrate that the temporal local clustering coefficient TC conveys the hidden information of nodes' neighboring connectance when interactions occur at various rhythms. By systematically analyzing various empirical datasets, we find that TC uncovers different interaction patterns in different types of temporal networks. Specifically, we show that TC has a strong positive correlation with C in efficiency-related networks, whereas they are uncorrelated in social activity-related networks. Moreover, TC helps to exclude interference from accidental interactions and reflect the actual clustering properties of network nodes. Our results shed light on the importance of digging into dynamical characteristics to fundamentally understand the underlying temporal structures of real complex systems.
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Affiliation(s)
| | - Guyu Hou
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, People's Republic of China
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing 100871, People's Republic of China
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Fan L, Liu J, Hu W, Chen Z, Lan J, Zhang T, Zhang Y, Wu X, Zhong Z, Zhang D, Zhang J, Qin R, Chen H, Zong Y, Zhang J, Chen B, Jiang J, Cheng J, Zhou J, Gao Z, Liu Z, Chai Y, Fan J, Wu P, Chen Y, Zhu Y, Wang K, Yuan Y, Huang P, Zhang Y, Feng H, Song K, Zeng X, Zhu W, Hu X, Yin W, Chen W, Wang J. Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans. Cell Res 2024; 34:407-427. [PMID: 38491170 PMCID: PMC11143203 DOI: 10.1038/s41422-024-00945-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/24/2024] [Indexed: 03/18/2024] Open
Abstract
Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1+ T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.
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Affiliation(s)
- Lin Fan
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Junwei Liu
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Guangzhou National Laboratory, Guangzhou, Guangdong, China
| | - Wei Hu
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zexin Chen
- Center of Clinical Epidemiology and Biostatistics and Department of Scientific Research, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Lan
- National Laboratory of Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Beijing, China
- Department of Bioinformatics, The Basic Medical School of Chongqing Medical University, Chongqing, China
| | - Tongtong Zhang
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Hepatobiliary and Pancreatic Surgery, The Center for Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xianpeng Wu
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zhiwei Zhong
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Danyang Zhang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jinlong Zhang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Rui Qin
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- The MOE Frontier Science Center for Brain Science & Brain-machine Integration, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hui Chen
- National Laboratory of Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Beijing, China
| | - Yunfeng Zong
- National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianmin Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Bing Chen
- Department of Vascular Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jun Jiang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jifang Cheng
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingyi Zhou
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhiwei Gao
- Department of Vascular Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhenjie Liu
- Department of Vascular Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Chai
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Junqiang Fan
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Pin Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinxuan Chen
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuefeng Zhu
- Department of Vascular Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kai Wang
- Department of Respiratory, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Yuan
- Department of Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huiqin Feng
- Department of Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaichen Song
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xun Zeng
- National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wei Zhu
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xinyang Hu
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China.
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China.
| | - Weiwei Yin
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wei Chen
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China.
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
- The MOE Frontier Science Center for Brain Science & Brain-machine Integration, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Jian'an Wang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China.
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China.
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Thapa G, Bhattacharya A, Bhattacharya S. Molecular dynamics investigation of DNA fragments bound to the anti-HIV protein SAMHD1 reveals alterations in allosteric communications. J Mol Graph Model 2024; 129:108748. [PMID: 38452417 DOI: 10.1016/j.jmgm.2024.108748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
The sterile alpha motif and histidine-aspartate domain-containing protein 1 (or SAMHD1), a human dNTP-triphosphohydrolase, contributes to HIV-1 restriction in select terminally differentiated cells of the immune system. While the prevailing hypothesis is that the catalytically active form of the protein is an allosterically triggered tetramer, whose HIV-1 restriction properties are attributed to its dNTP - triphosphohydrolase activity, it is also known to bind to ssRNA and ssDNA oligomers. A complete picture of the structure-function relationship of the enzyme is still elusive and the function corresponding to its nucleic acid binding ability is debated. In this in silico study, we investigate the stability, preference and allosteric effects of DNA oligomers bound to SAMHD1. In particular, we compare the binding of DNA and RNA oligomers of the same sequence and also consider the binding of DNA fragments with phosphorothioate bonds in the backbone. The results are compared with the canonical form with the monomers connected by GTP/dATP crossbridges. The simulations indicate that SAMHD1 dimers preferably bind to DNA and RNA oligomers compared to GTP/dATP. However, allosteric communication channels are altered in the nucleic acid acid bound complexes compared to the canonical form. All results are consistent with the hypothesis that the DNA bound form of the protein correspond to an unproductive off-pathway state where the protein is sequestered and not available for dNTP hydrolysis.
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Affiliation(s)
- Gauri Thapa
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India.
| | | | - Swati Bhattacharya
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India.
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Schweiger Gallo I, Görke LA, Alonso MA, Herrero López R, Gollwitzer PM. Are different countries equally green with envy? A comparison of the everyday concept of envy in the United States, Spain, and Germany. Scand J Psychol 2024; 65:452-468. [PMID: 38124407 DOI: 10.1111/sjop.12994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023]
Abstract
Using a prototype approach to emotion concepts, we mapped the internal structure and content of the everyday concept of envy (as used in the United States) and its translation equivalents of envidia in Spanish and Neid in German. In Study 1 (total N = 415), the features of the concept of envy, envidia, and Neid were generated via an open-ended questionnaire. In Study 2 (total N = 404), participants rated the degree of typicality of the constitutive features on a forced-choice questionnaire. The prototype analysis of envy, supplemented with network analyses, revealed that the largest connected set of features of envy, envidia, and Neid shared a group of central features, including features related to success or to people with a better appearance. Still, envy, envidia, and Neid did differ with respect to their constituent peripheral features as well as the density of their networks, their structure, and the betweenness centrality of the nodes. These results suggest that a prototype approach combined with network analysis is a convenient approach for studying the internal structure of everyday emotion concepts and the degree of overlap with respect to the translation equivalents in different countries.
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Affiliation(s)
- Inge Schweiger Gallo
- Departamento de Antropología Social y Psicología Social, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Lucia A Görke
- Department of Psychology and Graduate School of Decision Sciences, University of Konstanz, Konstanz, Germany
| | - Miguel A Alonso
- Departamento de Psicología Social, del Trabajo y Diferencial, Universidad Complutense de Madrid, Pozuelo de Alarcon, Spain
| | - Reyes Herrero López
- Departamento de Ciencia Política y de la Administración, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
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Bendahman N, Lotfi D. Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models. ENTROPY (BASEL, SWITZERLAND) 2024; 26:486. [PMID: 38920495 PMCID: PMC11202487 DOI: 10.3390/e26060486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/27/2024]
Abstract
Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible-infected-recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics.
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Affiliation(s)
- Nada Bendahman
- LRIT, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10000, Morocco;
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50
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DeGroat W, Inoue F, Ashuach T, Yosef N, Ahituv N, Kreimer A. Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595375. [PMID: 38826254 PMCID: PMC11142193 DOI: 10.1101/2024.05.22.595375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of regulatory programs this variation affects can shed light on the apparatuses of human diseases. Results We collected epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we constructed networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks served as the base for a rich series of analyses, through which we demonstrated their temporal dynamics and enrichment for various disease-associated variants. We applied the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrated methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays. Conclusions Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes. This includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.
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Affiliation(s)
- William DeGroat
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ 08854, UAS
| | - Fumitaka Inoue
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Tal Ashuach
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, 387 Soda Hall, Berkeley, CA 94720, USA
| | - Nir Yosef
- Department of Systems Immunology, Weizmann Institute of Science, 234 Herzl Street, Rehovot 7610001, Israel
- Chan-Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA
- Department of Systems Immunology, Ragon Institute of MGH, MIT, and Harvard Institute of Science, 400 Technology Square, Cambridge, MA 02139, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 513 Parnassus Ave, CA 94143, USA
- Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, CA 94143, USA
| | - Anat Kreimer
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ 08854, UAS
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ 08854, USA
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