Cooperative Research Centre

About: Cooperative Research Centre is a based out in . It is known for research contribution in the topics: Population & Sea ice. The organization has 7633 authors who have published 8607 publications receiving 429721 citations.

Impact of DEM accuracy and resolution on topographic indices

Abstract: Topography is an important land-surface characteristic that affects most aspects of the water balance in a catchment, including the generation of surface and sub-surface runoff; the flow paths followed by water as it moves down and through hillslopes and the rate of water movement. All of the spatially explicit fully distributed hydraulic and hydrological models use topography (represented by the DEM of the area modelled) to derive bathymetry. DEM is also used to derive some other key information critical in fully distributed hydraulic and hydrological models. With high-resolution DEMs such as LiDAR (Light Detection and Ranging) becoming more readily available and also with the advancements in computing facilities which can handle these large data sets, there is a need to quantify the impact of using different resolution DEMs (e.g. 1 m against 10 m or 25 m) on hydrologically important variables and the loss of accuracy and reliability of the results as we move from high resolution to coarser resolution. The results from statistical analysis carried out to compare field survey elevations with the LiDAR DEM-derived elevations, show that there are small differences between the two data sets but LiDAR DEM is a reasonably good representation of the actual ground surface compared to other commonly used DEMs derived from contour maps. The results from the analysis clearly show that the accuracy and resolution of the input DEM have serious implications on the values of the hydrologically important spatial indices derived from the DEM. The result also indicates that the loss of details by re-sampling the higher resolution DEM to coarser resolution are much less compared to the details captured in the commonly available coarse resolution DEM derived from contour maps. Topographic indices based on contour derived DEMs should be used with caution and where available, the higher resolution DEM should be used instead of the coarse resolution one.

Algicidal Effects of a Novel Marine Pseudoalteromonas Isolate (Class Proteobacteria, Gamma Subdivision) on Harmful Algal Bloom Species of the Genera Chattonella, Gymnodinium, and Heterosigma

Abstract: During a bacterial survey of the Huon Estuary in southern Tasmania, Australia, we isolated a yellow-pigmented Pseudoalteromonas strain (class Proteobacteria, gamma subdivision), designated strain Y, that had potent algicidal effects on harmful algal bloom species. This organism was identified by 16S rRNA sequencing as a strain with close affinities to Pseudoalteromonas peptidysin. This bacterium caused rapid cell lysis and death (within 3 h) of gymnodinoids (including Gymnodinium catenatum) and raphidophytes (Chattonella marina and Heterosigma akashiwo). It caused ecdysis of armored dinoflagellates (e.g., Alexandrium catenella, Alexandrium minutum, and Prorocentrum mexicanum), but the algal cultures then recovered over the subsequent 24 h. Strain Y had no effect on a cryptomonad (Chroomonas sp.), a diatom (Skeletonema sp.), a cyanobacterium (Oscillatoria sp.), and two aplastidic protozoans. The algicidal principle of strain Y was excreted into the seawater medium and lost its efficacy after heating. Another common bacterial species, Pseudoalteromonas carrageenovora, was isolated at the same time and did not have these algicidal effects. The minimum concentrations of strain Y required to kill G. catenatum were higher than the mean concentrations found in nature under nonbloom conditions. However, the new bacterium showed a chemotactic, swarming behavior that resulted in localized high concentrations around target organisms. These observations imply that certain bacteria could play an important role in regulating the onset and development of harmful algal blooms.

Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits

Abstract: Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.

Complexity in "simple" electrolyte solutions: Ion pairing in MgSO4(aq)

Abstract: A detailed investigation of aqueous solutions of MgSO4 has been made by dielectric relaxation spectroscopy over a wide range of frequencies (0.2 ≤ ν/GHz ≤ 89) and concentrations (0.017 ≤ c/M ≤ 2.24). Detailed analysis of the spectra shows conclusively, as has long been inferred from ultrasonic absorption studies, the simultaneous presence of double solvent separated (2SIP), solvent-shared (SIP), and contact (CIP) ion pairs. The constants derived for the stepwise formation of each ion pair type and for the overall association are in excellent agreement with literature estimates based on other kinds of measurements. In addition, evidence has been obtained for the existence of a triple ion, Mg2SO42+(aq), or possibly a more aggregated species, at high electrolyte concentrations (c > 1 M). Support for the presence of CIPs, SIPs, and the triple ion is provided by Raman spectroscopy. The implications of the present findings for quantitative models of the thermodynamic behavior of higher-valent electrolytes are b...