Background
Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts.
Methods
Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (μStat) are not confounded by unrealistic assumptions (e.g., linearity, independence).
Results
By incorporating knowledge about relationships between SNPs, μGWAS (GWAS based on μStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes.
Conclusion
With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for ad verse events and novel directions for drug development.
The process of anaerobic digestion is highly influenced by the environmental and operational factors like organic acids concentration and the reactor volume occupied by the feed material. The optimum level of organic acids is commonly assumed to be in the range between 2,500 and 3,500 mg/l for the anaerobic digestion process. It was observed that the production of total organic acids during hydrolysis of grass using cattle dung slurry (CDS) as the inoculum reached up to 4,850 mg/l in 6 days, while on the other hand it reached 5,700 mg/l within 4 days when rumen content was used as inoculum. The organic acids production continued to the 30th day in the case of rumen content, while in the case of CDS it stopped within 10 days because of pH drop. As compared to CDS the anaerobic digestion of grass with rumen content showed better degradation and biogas production with nearly 80% of methane and up to 80 and 95% reduction in chemical oxygen demand and organic acids respectively.
Membrane separation proved to be an excellent means to maintain high residence time of microorganisms in an anaerobic hydrolysis reactor, and relatively low concentration of hydrolysis products. The microbial biocommunity typical for the rumen environment could be maintained, and the reactor efficiency of the reactor improved. Less than 4 days were reqired to reach almost complete hydrolysis of the grass fed into the reactor. To avoid blocking of the membrane unit, a backwash system is necessary. The membranes needed to be backwashed every 20 min with 4 bar gas-pressure for 10 s. After this treatment the initial permeability was regained. The plant was operated with a flux of 12 ml h(-1) cm(-2) on average. The transmembrane pressure was in the range of 0.8-0.9 bar. 90% of the dissolved fatty acids permeated through the membrane.
The microbial ecology of the rumen is very complex. Different species of bacteria, protozoa, and fungi are involved in digestion of plant material in ruminants. In spite of complicated interrelationships among the various groups of microorganisms in the rumen ecosystem, Bacteria and Archaea are believed to play a major role because of their numerical predominance and metabolic diversity. In this work we are presenting the results for microbial population dynamics of rumen microbes during two-stage anaerobic digestion of grass. The reactors were inoculated with fresh rumen content. Fluorescent in situ hybridization, confocal laser scanning microscopy and epifluorescence microscopy were employed for microbial investigation. It was observed that Bacteria dominated in the hydrolytic reactor (1st stage) whereas Archaea were predominant in the methanogenic reactor (2nd stage). The stability of the methanogenic reactor was result of the dominance of Methanosaeta species (mainly the filamentous type).
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