The Experts below are selected from a list of 177210 Experts worldwide ranked by ideXlab platform
Nazir Ahmed Ismail - One of the best experts on this subject based on the ideXlab platform.
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mdr m tuberculosis outbreak clone in eswatini missed by xpert has elevated bedaquiline resistance dated to the pre treatment era
Genome Medicine, 2020Co-Authors: Patrick Beckert, Elisabeth Sanchezpadilla, Matthias Merker, Viola Dreyer, Thomas Kohl, Christian Utpatel, Claudio U Koser, Ivan Barilar, Nazir Ahmed IsmailAbstract:Multidrug-resistant (MDR) Mycobacterium tuberculosis complex strains not detected by commercial molecular drug susceptibility testing (mDST) assays due to the RpoB I491F resistance mutation are threatening the control of MDR tuberculosis (MDR-TB) in Eswatini. We investigate the evolution and spread of MDR strains in Eswatini with a focus on bedaquiline (BDQ) and clofazimine (CFZ) resistance using whole-genome sequencing in two collections ((1) national drug resistance survey, 2009–2010; (2) MDR strains from the Nhlangano region, 2014–2017). MDR strains in collection 1 had a High cluster rate (95%, 117/123 MDR strains) with 55% grouped into the two largest clusters (gCL3, n = 28; gCL10, n = 40). All gCL10 isolates, which likely emerged around 1993 (95% Highest posterior density 1987–1998), carried the mutation RpoB I491F that is missed by commercial mDST assays. In addition, 21 (53%) gCL10 isolates shared a Rv0678 M146T mutation that correlated with elevated minimum inhibitory concentrations (MICs) to BDQ and CFZ compared to wild type isolates. gCL10 isolates with the Rv0678 M146T mutation were also detected in collection 2. The High Clustering rate suggests that transmission has been driving the MDR-TB epidemic in Eswatini for three decades. The presence of MDR strains in Eswatini that are not detected by commercial mDST assays and have elevated MICs to BDQ and CFZ potentially jeopardizes the successful implementation of new MDR-TB treatment guidelines. Measures to limit the spread of these outbreak isolates need to be implemented urgently.
Ricard V Sole - One of the best experts on this subject based on the ideXlab platform.
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small world patterns in food webs
Journal of Theoretical Biology, 2002Co-Authors: Jose M Montoya, Ricard V SoleAbstract:The analysis of some species-rich, well-defined food webs shows that they display the so-called small world behavior shared by a number of disparate complex systems. The three systems analysed (Ythan estuary web, Silwood web and the Little Rock lake web) have different levels of taxonomic resolution, but all of them involve High Clustering and short path lengths (near two degrees of separation) between species. Additionally, the distribution of connections P(k) which is skewed in all the webs analysed shows long tails indicative of power-law scaling. These features suggest that communities might be self-organized in a non-random fashion that might have important consequences in their resistance to perturbations (such as species removal). The consequences for ecological theory are outlined.
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small world patterns in food webs
Research Papers in Economics, 2000Co-Authors: Jose M Montoya, Ricard V SoleAbstract:The analysis of some species-rich, well-defined food webs shows that they display the so called Small World behavior shared by a number of disparate complex systems. The three systems analysed (Ythan estuary web, Silwood web and the Little Rock lake web) have different levels of taxonomic resolution, but all of them involve High Clustering and short path lengths between species. Additionally, the distribution of connections $P(k)$ is skewed in all the webs analysed and shows a power-law behavior $P(k) \propto k^{-\gamma}$ in two cases (with $\gamma \approx 1$). These features suggest that communities might be self-organized in such a way that High homeostasis to perturbations (with short transient times to recovery) would be at work. The consequences for ecological theory are outlined.
Lutz Jancke - One of the best experts on this subject based on the ideXlab platform.
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functional brain network efficiency predicts intelligence
Human Brain Mapping, 2012Co-Authors: Nicolas Langer, Andreas Pedroni, Lorena R R Gianotti, Jurgen Hanggi, Daria Knoch, Lutz JanckeAbstract:The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (High Clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the Clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with Higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for Highly productive cognitions. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.
Andreas Pedroni - One of the best experts on this subject based on the ideXlab platform.
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functional brain network efficiency predicts intelligence
Human Brain Mapping, 2012Co-Authors: Nicolas Langer, Andreas Pedroni, Lorena R R Gianotti, Jurgen Hanggi, Daria Knoch, Lutz JanckeAbstract:The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (High Clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the Clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with Higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for Highly productive cognitions. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.
Francesc Comellas - One of the best experts on this subject based on the ideXlab platform.
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evolving small world networks with geographical attachment preference
Journal of Physics A, 2006Co-Authors: Zhongzhi Zhang, Lili Rong, Francesc ComellasAbstract:We introduce a minimal extended evolving model for small-world networks which is controlled by a parameter. In this model, the network growth is determined by the attachment of new nodes to already existing nodes that are geographically close. We analyse several topological properties for our model both analytically and by numerical simulations. The resulting network shows some important characteristics of real-life networks such as small-world effect and High Clustering.