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991.
The macroinvertebrate assemblages in an artificial habitat, the settling basin of a hydroelectric power plant, were investigated and compared with those in a natural habitat, riffles in a nearby river. This study showed that macroinvertebrate density was much higher in the settling basin than in the riffles. Macroinvertebrate assemblage composition differed between the settling basin and the riffles. The difference was probably due to the widespread bryophyte beds in the settling basin. Cincticostella, Brachycentrus, Ephmerella, and chironomid midges, which are usually abundant in bryophyte beds, were present at much higher densities in the settling basin. Cheumatopsyche and Taeniopterygidae were also present at higher densities in the settling basin than in the natural riffles. In contrast, Epeorus was present at lower density in the settling basin than in the natural riffles. This study suggests that the settling basin increases β-diversity in riverine ecosystems. 相似文献
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Babesia divergens builds a complex population structure composed of specific ratios of infected cells to ensure a prompt response to changing environmental conditions 下载免费PDF全文
Jeny R. Cursino‐Santos Manpreet Singh Petra Pham Marilis Rodriguez Cheryl A. Lobo 《Cellular microbiology》2016,18(6):859-874
Babesia parasites cause a malaria‐like febrile illness by infection of red blood cells (RBCs). Despite the growing importance of this tick‐borne infection, its basic biology has been neglected. Using novel synchronization tools, the sequence of intra‐erythrocytic events was followed from invasion through development and differentiation to egress. The dynamics of the parasite population were studied in culture, revealing for the first time, the complete array of morphological forms in a precursor–product relationship. Important chronological constants including Babesia's highly unusual variable intra‐erythrocytic life cycle, the life span of each population of infected cells and the time required for the genesis of the different parasite stages were elucidated. Importantly, the maintenance of specific ratios of the infected RBC populations was shown to be responsible for the parasites' choice of developmental pathways, enabling swift responses to changing environmental conditions like availability of RBCs and nutrition. These results could impact the control of parasite proliferation and therefore disease. 相似文献
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Justo Lorenzo Bermejo 《BMC genetics》2016,17(Z2):S2
This paper summarizes the contributions from the Population-Based Association group at the Genetic Analysis Workshop 19. It provides an overview of the new statistical approaches tried out by group members in order to take best advantage of population-based sequence data.Although contributions were highly heterogeneous regarding the applied quality control criteria and the number of investigated variants, several technical issues were identified, leading to practical recommendations. Preliminary analyses revealed that Hurdle-negative binomial regression is a promising approach to investigate the distribution of allele counts instead of called genotypes from sequence data. Convergence problems, however, limited the use of this approach, creating a technical challenge shared by environment-stratified models used to investigate rare variant-environment interactions, as well as by rare variant haplotype analyses using well-established public software. Estimates of relatedness and population structure strongly depended on the allele frequency of selected variants for inference. Another practical recommendation was that dissenting probability values from standard and small-sample tests of a particular hypothesis may reflect a lack of validity of large-sample approximations. Novel statistical approaches that integrate evolutionary information showed some advantage to detect weak genetic signals, and Bayesian adjustment for confounding was able to efficiently estimate causal genetic effects. Haplotype association methods may constitute a valuable complement of collapsing approaches for sequence data. This paper reports on the experience of members of the Population-Based Association group with several novel, promising approaches to preprocessing and analyzing sequence data, and to following up identified association signals. 相似文献
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Background
Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure–related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.Results
Two analytical methods were applied to the GAW19: GWA using real phenotypic data, and one approach to WGS using 200 simulated replicates. The first GWA approach applied a GEE-based model to identify gene-based associations with 4 derived hypertension phenotypes. This GEE model identified 1 significant locus, GRM7, which passed multiple test corrections for 2 hypertension-derived traits. The second GWA approach employed the LME to estimate genetic associations with systolic blood pressure (SBP) change trajectories identified using LCGM. This LCGM method identified 5 SBP trajectories and association analyses identified a genome-wide significant locus, near ATOX1 (p?=?1.0E?8). Finally, a third VC-based model using WGS and simulated SBP phenotypes that constrained the β coefficient for a genetic variant across each time point was calculated and compared to an unconstrained approach. This constrained VC approach demonstrated increased power for WGS variants of moderate effect, but when larger genetic effects were present, averaging across time points was as effective.Conclusion
In this paper, we summarize 3 GAW19 contributions applying novel statistical methods and testing previously proposed techniques under alternative conditions for longitudinal genetic association. We conclude that these approaches when appropriately applied have the potential to: (a) increase statistical power; (b) decrease trait heterogeneity and standard error; (c) decrease computational burden in WGS; and (d) have the potential to identify genetic variants influencing subphenotypes important for understanding disease progression.999.
Ashraf Yaseen Mais Nijim Brandon Williams Lei Qian Min Li Jianxin Wang Yaohang Li 《BMC bioinformatics》2016,17(8):281
Background
The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.Results
In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.Conclusions
Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.1000.