Life cycles of 17 riffle-dwelling mayfly species (Baetidae,Heptageniidae, and Ephemerellidae) in central Japan

We described the life cycles of 17 riffle-dwelling mayfly species in a central Japanese stream. Both species of Baetidae (Alainites yoshinensis Gose and Baetis thermicus Uéno) and two of nine species of Heptageniidae (Ecdyonurus scalaris Kluge and Epeorus latifolium Uéno/l-nigrus Matsumura) in this stream were multivoltine (more than one generation per year). Seven other heptageniid species (two unidentified species of Cinygmula, Epeorus aesculus Imanishi, E. curvatulus Matsumura, E. ikanonis Takahashi, E. napaeus Imanishi, and Rhithrogena japonica Uéno) and all six species of Ephemerellidae [Cincticostella elongatula (McLachlan), C. nigra (Uéno), Drunella basalis (Imanishi), D. ishiyamana Matsumura, D. sachalinensis (Matsumura), and D. trispina (Uéno)] were essentially univoltine.     

Trace elements influenced by environmental changes in Lake Biwa: (I) Seasonal variations under suboxic hypolimnion conditions during 2007 and 2009

Dissolved oxygen (DO) in the Lake Biwa hypolimnion reached its lowest level of <1 mg kg^?1 in 2007. In this paper, we report the variations in the total dissolvable (TD), dissolved (D), and labile particulate (LP) fractions of Al, Si, P, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Mo, W, and U in Lake Biwa 2007 and 2009. Al and Fe species were predominantly in the form of LP-Al and LP-Fe and were strongly correlated with one another (r = 0.99), suggesting that the weathering of aluminous minerals and the supply of clay mineral particles are the main factors that influence the distributions of Al and Fe. Although D-Al increased in the summer epilimnion, D-Fe was relatively low, probably as a result of uptake by plants. Reductive release of Fe from the bottom was not seen. Mn was also dominated by LP-Mn, but this fraction showed a different distribution to those of LP-Al and LP-Fe. The D-Mn and LP-Mn concentrations varied by factors of 700–1000 and showed marked increases in the bottom water during stratification in 2007. We believe that Mn²⁺ was released from the sediments and oxidized by DO in the bottom water. Ni, Cu, Zn, and Cr, which exist as cationic species, had LP/TD ratios of 0.1–0.7 and relatively uniform distributions. Si, P, V, As, Mo, W, and U, which form oxoacid species, had LP/TD ratios of 0–0.8. Si, P, and As were characterized by nutrient-like profiles, V, W, and U showed summer maxima in the epilimnion, and Mo had a uniform distribution. TD-Mo increased in the bottom water along with TD-Mn, while TD-V and TD-W showed significant decreases. These results are likely attributable to differences in the adsorption of these elements onto manganese oxides and iron hydroxides.     

Macroinvertebrate assemblages in an artificial habitat—a settling basin of a hydroelectric power plant: comparison with a natural riffle habitat

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.     

Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19

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.     

Longitudinal analytical approaches to genetic data

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.

     

FLEXc: protein flexibility prediction using context-based statistics,predicted structural features,and sequence information

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.