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Kounchev O, Boyadzhiev G, Simeonov G, Kunchev M, Kuncheva Z. SEIR Model Analysis of the Omicron Variant Spread in Bulgaria – an Empirical Study. C R Acad Bulg Sci 2023. [DOI: 10.7546/crabs.2023.01.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
We provide a study of the Covid-19 spread in Bulgaria in the period starting December 15, 2021 until early February, 2022. In particular, we provide predictive scenarios for the peak of the pandemic. Based on these scenarios, we estimate the risks in terms of fatalities in the case no restrictive measures are imposed. The main challenge is distinguishing the influence of the Delta variant which is still dominating in December, 2021, while Omicron becomes dominant in early January, 2022.
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Jackson MA, Bonder MJ, Kuncheva Z, Zierer J, Fu J, Kurilshikov A, Wijmenga C, Zhernakova A, Bell JT, Spector TD, Steves CJ. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ 2018; 6:e4303. [PMID: 29441232 PMCID: PMC5807925 DOI: 10.7717/peerj.4303] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 01/10/2018] [Indexed: 12/20/2022] Open
Abstract
Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.
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Affiliation(s)
- Matthew A Jackson
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
| | - Marc Jan Bonder
- University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, Netherlands
| | - Zhana Kuncheva
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Jonas Zierer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom.,Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jingyuan Fu
- University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, Netherlands.,University Medical Center Groningen, Department of Pediatrics, University of Groningen, Groningen, Netherlands
| | - Alexander Kurilshikov
- University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, Netherlands
| | - Cisca Wijmenga
- University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, Netherlands.,K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Oslo, Norway
| | - Alexandra Zhernakova
- University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, Netherlands
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
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Kuncheva Z, Krishnan ML, Montana G. EXPLORING BRAIN TRANSCRIPTOMIC PATTERNS: A TOPOLOGICAL ANALYSIS USING SPATIAL EXPRESSION NETWORKS. Pac Symp Biocomput 2017; 22:70-81. [PMID: 27896963 DOI: 10.1142/9789813207813_0008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Characterizing the transcriptome architecture of the human brain is fundamental in gaining an understanding of brain function and disease. A number of recent studies have investigated patterns of brain gene expression obtained from an extensive anatomical coverage across the entire human brain using experimental data generated by the Allen Human Brain Atlas (AHBA) project. In this paper, we propose a new representation of a gene's transcription activity that explicitly captures the pattern of spatial co-expression across different anatomical brain regions. For each gene, we define a Spatial Expression Network (SEN), a network quantifying co-expression patterns amongst several anatomical locations. Network similarity measures are then employed to quantify the topological resemblance between pairs of SENs and identify naturally occurring clusters. Using network-theoretical measures, three large clusters have been detected featuring distinct topological properties. We then evaluate whether topological diversity of the SENs reects significant differences in biological function through a gene ontology analysis. We report on evidence suggesting that one of the three SEN clusters consists of genes specifically involved in the nervous system, including genes related to brain disorders, while the remaining two clusters are representative of immunity, transcription and translation. These findings are consistent with previous studies showing that brain gene clusters are generally associated with one of these three major biological processes.
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