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Speer KA, Teixeira TSM, Brown AM, Perkins SL, Dittmar K, Ingala MR, Wultsch C, Krampis K, Dick CW, Galen SC, Simmons NB, Clare EL. Cascading effects of habitat loss on ectoparasite-associated bacterial microbiomes. ISME COMMUNICATIONS 2022; 2:67. [PMID: 37938296 PMCID: PMC9723575 DOI: 10.1038/s43705-022-00153-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/05/2022] [Accepted: 07/18/2022] [Indexed: 11/04/2023]
Abstract
Suitable habitat fragment size, isolation, and distance from a source are important variables influencing community composition of plants and animals, but the role of these environmental factors in determining composition and variation of host-associated microbial communities is poorly known. In parasite-associated microbial communities, it is hypothesized that evolution and ecology of an arthropod parasite will influence its microbiome more than broader environmental factors, but this hypothesis has not been extensively tested. To examine the influence of the broader environment on the parasite microbiome, we applied high-throughput sequencing of the V4 region of 16S rRNA to characterize the microbiome of 222 obligate ectoparasitic bat flies (Streblidae and Nycteribiidae) collected from 155 bats (representing six species) from ten habitat fragments in the Atlantic Forest of Brazil. Parasite species identity is the strongest driver of microbiome composition. To a lesser extent, reduction in habitat fragment area, but not isolation, is associated with an increase in connectance and betweenness centrality of bacterial association networks driven by changes in the diversity of the parasite community. Controlling for the parasite community, bacterial network topology covaries with habitat patch area and exhibits parasite-species specific responses to environmental change. Taken together, habitat loss may have cascading consequences for communities of interacting macro- and microorgansims.
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Affiliation(s)
- Kelly A Speer
- Richard Gilder Graduate School, American Museum of Natural History, New York, NY, USA.
- Center for Conservation Genomics, Smithsonian National Zoological Park and Conservation Biology Institute, Washington, D.C, USA.
- Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, D.C, USA.
| | | | - Alexis M Brown
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, USA
| | - Susan L Perkins
- Richard Gilder Graduate School, American Museum of Natural History, New York, NY, USA
- Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY, USA
- Division of Science, City College of New York, New York, NY, USA
| | - Katharina Dittmar
- Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Melissa R Ingala
- Richard Gilder Graduate School, American Museum of Natural History, New York, NY, USA
- Center for Conservation Genomics, Smithsonian National Zoological Park and Conservation Biology Institute, Washington, D.C, USA
- Department of Biological Sciences, Fairleigh Dickinson University, Madison, NJ, USA
| | - Claudia Wultsch
- Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY, USA
- Bioinformatics and Computational Genomics Laboratory, Department of Biological Sciences, Hunter College, City University of New York, New York, NY, USA
| | - Konstantinos Krampis
- Bioinformatics and Computational Genomics Laboratory, Department of Biological Sciences, Hunter College, City University of New York, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Carl W Dick
- Department of Biology, Western Kentucky University, Bowling Green, KY, USA
- Integrative Research Center, Field Museum of Natural History, Chicago, IL, USA
| | - Spencer C Galen
- Richard Gilder Graduate School, American Museum of Natural History, New York, NY, USA
- Biology Department, University of Scranton, Scranton, PA, USA
| | - Nancy B Simmons
- Richard Gilder Graduate School, American Museum of Natural History, New York, NY, USA
- Department of Mammalogy, Division of Vertebrate Zoology, American Museum of Natural History, New York, NY, USA
| | - Elizabeth L Clare
- School of Biological and Chemical Sciences, Queen Mary University of London, London, GBR, UK
- Department of Biology, York University, Toronto, ON, Canada
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2
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Herzog R, Morales A, Mora S, Araya J, Escobar MJ, Palacios AG, Cofré R. Scalable and accurate method for neuronal ensemble detection in spiking neural networks. PLoS One 2021; 16:e0251647. [PMID: 34329314 PMCID: PMC8323916 DOI: 10.1371/journal.pone.0251647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 04/29/2021] [Indexed: 11/19/2022] Open
Abstract
We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.
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Affiliation(s)
- Rubén Herzog
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
| | - Arturo Morales
- Departamento de Electrónica, Universidad Técnica Federico Santa María, Valparaíso, Chile
| | - Soraya Mora
- Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile
- Laboratorio de Biología Computacional, Fundación Ciencia y Vida, Santiago, Chile
| | - Joaquín Araya
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Escuela de Tecnología Médica, Facultad de Salud, Universidad Santo Tomás, Santiago, Chile
| | - María-José Escobar
- Departamento de Electrónica, Universidad Técnica Federico Santa María, Valparaíso, Chile
| | - Adrian G. Palacios
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
| | - Rodrigo Cofré
- CIMFAV Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
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Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models. PLoS One 2021; 16:e0254057. [PMID: 34214126 PMCID: PMC8253422 DOI: 10.1371/journal.pone.0254057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 06/19/2021] [Indexed: 11/19/2022] Open
Abstract
Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null model, in stark contrast to traditional community detection methods. Across all analyses, we find the choice of null model can strongly alter conclusions about the presence of network structure. Our spectral estimation approach is therefore a promising basis for detecting low-dimensional structure in real-world networks, or lack thereof.
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Radosevic M, Willumsen A, Petersen PC, Lindén H, Vestergaard M, Berg RW. Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord. Nat Commun 2019; 10:2937. [PMID: 31270315 PMCID: PMC6610135 DOI: 10.1038/s41467-019-10822-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 06/04/2019] [Indexed: 12/12/2022] Open
Abstract
During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.
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Affiliation(s)
- Marija Radosevic
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
| | - Alex Willumsen
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
| | - Peter C Petersen
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
- Neuroscience Institute, New York University, New York, NY, 10016, USA
| | - Henrik Lindén
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
| | - Mikkel Vestergaard
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
- Department of Neuroscience, Max Delbrück Center for Molecular Medicine (MDC), 13125, Berlin-Buch, Germany
| | - Rune W Berg
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark.
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Cheng J, Yin X, Li Q, Yang H, Li L, Leng M, Chen X. Voting Simulation based Agglomerative Hierarchical Method for Network Community Detection. Sci Rep 2018; 8:8064. [PMID: 29795231 PMCID: PMC5966462 DOI: 10.1038/s41598-018-26415-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 05/10/2018] [Indexed: 11/26/2022] Open
Abstract
Community detection has been paid much attention in many fields in recent years, and a great deal of community-detection methods have been proposed. But the time consumption of some of them is heavy, limiting them from being applied to large-scale networks. On the contrary, there exist some lower-time-complexity methods. But most of them are non-deterministic, meaning that running the same method many times may yield different results from the same network, which reduces their practical utility greatly in real-world applications. To solve these problems, we propose a community-detection method in this paper, which takes both the quality of the results and the efficiency of the detecting procedure into account. Moreover, it is a deterministic method which can extract definite community structures from networks. The proposed method is inspired by the voting behaviours in election activities in the social society, in which we first simulate the voting procedure on the network. Every vertex votes for the nominated candidates following the proposed voting principles, densely connected groups of vertices can quickly reach a consensus on their candidates. At the end of this procedure, candidates and their own voters form a group of clusters. Then, we take the clusters as initial communities, and agglomerate some of them into larger ones with high efficiency to obtain the resulting community structures. We conducted extensive experiments on some artificial networks and real-world networks, the experimental results show that our proposed method can efficiently extract high-quality community structures from networks, and outperform the comparison algorithms significantly.
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Affiliation(s)
- Jianjun Cheng
- Lanzhou University, School of Information Science and Engineering, Lanzhou, 730000, China.
- Gansu Resources and Environmental Science Data Engineering Technology Research Center, Lanzhou, 730000, China.
| | - Xinhong Yin
- Lanzhou University, School of Information Science and Engineering, Lanzhou, 730000, China
| | - Qi Li
- Lanzhou University, School of Information Science and Engineering, Lanzhou, 730000, China
| | - Haijuan Yang
- Lanzhou Vocational Technical College, Department of Electronic Information Engineering, Lanzhou, 730070, China
| | - Longjie Li
- Lanzhou University, School of Information Science and Engineering, Lanzhou, 730000, China
| | - Mingwei Leng
- Northwest Minzu University, School of Education Science and Technology, Lanzhou, 730030, China
| | - Xiaoyun Chen
- Lanzhou University, School of Information Science and Engineering, Lanzhou, 730000, China.
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Han J, Li W, Zhao L, Su Z, Zou Y, Deng W. Community detection in dynamic networks via adaptive label propagation. PLoS One 2017; 12:e0188655. [PMID: 29186160 PMCID: PMC5706735 DOI: 10.1371/journal.pone.0188655] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 09/26/2017] [Indexed: 11/18/2022] Open
Abstract
An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.
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Affiliation(s)
- Jihui Han
- Complexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China
- * E-mail: (JH); (WL); (WD)
| | - Wei Li
- Complexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
- * E-mail: (JH); (WL); (WD)
| | - Longfeng Zhao
- Complexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
| | - Zhu Su
- Complexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
| | - Yijiang Zou
- Complexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
| | - Weibing Deng
- Complexity Science Center & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
- * E-mail: (JH); (WL); (WD)
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Childhood maltreatment is associated with alteration in global network fiber-tract architecture independent of history of depression and anxiety. Neuroimage 2017; 150:50-59. [PMID: 28213111 DOI: 10.1016/j.neuroimage.2017.02.037] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 12/31/2016] [Accepted: 02/13/2017] [Indexed: 11/21/2022] Open
Abstract
Childhood maltreatment is a major risk factor for psychopathology. It is also associated with alterations in the network architecture of the brain, which we hypothesized may play a significant role in the development of psychopathology. In this study, we analyzed the global network architecture of physically healthy unmedicated 18-25 year old subjects (n=262) using diffusion tensor imaging (DTI) MRI and tractography. Anatomical networks were constructed from fiber streams interconnecting 90 cortical or subcortical regions for subjects with no-to-low (n=122) versus moderate-to-high (n=140) exposure to maltreatment. Graph theory analysis revealed lower degree, strength, global efficiency, and maximum Laplacian spectra, higher pathlength, small-worldness and Laplacian skewness, and less deviation from artificial networks in subjects with moderate-to-high exposure to maltreatment. On balance, local clustering was similar in both groups, but the different clusters were more strongly interconnected in the no-to-low exposure group. History of major depression, anxiety and attention deficit hyperactivity disorder did not have a significant impact on global network measures over and above the effect of maltreatment. Maltreatment is an important factor that needs to be taken into account in studies examining the relationship between network differences and psychopathology.
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Multi-resolution community detection in massive networks. Sci Rep 2016; 6:38998. [PMID: 27958395 PMCID: PMC5154182 DOI: 10.1038/srep38998] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 11/16/2016] [Indexed: 01/04/2023] Open
Abstract
Aiming at improving the efficiency and accuracy of community detection in complex networks, we proposed a new algorithm, which is based on the idea that communities could be detected from subnetworks by comparing the internal and external cohesion of each subnetwork. In our method, similar nodes are firstly gathered into meta-communities, which are then decided to be retained or merged through a multilevel label propagation process, until all of them meet our community criterion. Our algorithm requires neither any priori information of communities nor optimization of any objective function. Experimental results on both synthetic and real-world networks show that, our algorithm performs quite well and runs extremely fast, compared with several other popular algorithms. By tuning a resolution parameter, we can also observe communities at different scales, so this could reveal the hierarchical structure of the network. To further explore the effectiveness of our method, we applied it to the E-Coli transcriptional regulatory network, and found that all the identified modules have strong structural and functional coherence.
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