Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM) was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR) are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate(HCO3 -), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate, total nitrogen (TN), orthophosphate(PO4 3−), total phosphorus (TP), suspended solid (SS) and total organic carbon (TOC) selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008) data for training and the most recent 3 years (2009–2011) for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.
Protopalythoa is a zoanthid that, together with thousands of predominantly marine species, such as hydra, jellyfish, and sea anemones, composes the oldest eumetazoan phylum, i.e., the Cnidaria. Some of these species, such as sea wasps and sea anemones, are highly venomous organisms that can produce deadly toxins for preying, for defense or for territorial disputes. Despite the fact that hundreds of organic and polypeptide toxins have been characterized from sea anemones and jellyfish, practically nothing is known about the toxin repertoire in zoanthids. Here, based on a transcriptome analysis of the zoanthid Protopalythoa variabilis, numerous predicted polypeptides with canonical venom protein features are identified. These polypeptides comprise putative proteins from different toxin families: neurotoxic peptides, hemostatic and hemorrhagic toxins, membrane-active (pore-forming) proteins, protease inhibitors, mixed-function venom enzymes, and venom auxiliary proteins. The synthesis and functional analysis of two of these predicted toxin products, one related to the ShK/Aurelin family and the other to a recently discovered anthozoan toxin, displayed potent in vivo neurotoxicity that impaired swimming in larval zebrafish. Altogether, the complex array of venom-related transcripts that are identified in P. variabilis, some of which are first reported in Cnidaria, provides novel insight into the toxin distribution among species and might contribute to the understanding of composition and evolution of venom polypeptides in toxiferous animals.
To examine the relationship between activated-sludge bulking and levels of specific filamentous bacteria, we developed a statistics-based quantification method for estimating the biomass levels of specific filaments using 16S rRNA-targeted fluorescent in situ hybridization (FISH) probes. The results of quantitative FISH for the filament Sphaerotilus natans were similar to the results of quantitative membrane hybridization in a sample from a full-scale wastewater treatment plant. Laboratory-scale reactors were operated under different flow conditions to develop bulking and nonbulking sludge and were bioaugmented with S. natans cells to stimulate bulking. Instead of S. natans, the filament Eikelboom type 1851 became dominant in the reactors. Levels of type 1851 filaments extending out of the flocs correlated strongly with the sludge volume index, and extended filament lengths of approximately 6 x 10(8) micro m ml(-1) resulted in bulking in laboratory-scale and full-scale activated-sludge samples. Quantitative FISH showed that high levels of filaments occurred inside the flocs in nonbulking sludge, supporting the "substrate diffusion limitation" hypothesis for bulking. The approach will allow the monitoring of incremental improvements in bulking control methods and the delineation of the operational conditions that lead to bulking due to specific filaments.
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