High-solids anaerobic mono-digestion of riverbank grass under thermophilic conditions

The purpose of this study was to investigate the potential of high-solids anaerobic mono-digestion of riverbank grass under thermophilic conditions, focusing on the effects of the strength and the amount of inoculum. Ensiled grass was inoculated with three different inocula; inoculum from liquid anaerobic digester (LI), inoculum from dry anaerobic digester (DI), and mixture of LI and DI (MI), at feedstock-to-inoculum ratio (FIR) of 1, 2 and 4. The ensiling process of riverbank grass reduced moisture content (p > 0.05), while the hemicellulose content was significantly increased from 30.88% to 35.15% (p < 0.05), on dry matter basis. The highest methane production was at an FIR of 2 with MI (167 L/kg VS_added), which was significantly higher (p < 0.05) than with DI, but not significant compared to LI (p > 0.05). At an FIR of 4, digesters inoculated with LI and DI failed to produce methane, whereas 135 L_CH4/kg VS_added was obtained with MI. The kinetic studies showed that at an FIR of 1 with LI and MI, the inoculum had less of effects on the hydrolysis rate constant (0.269 day^− 1 and 0.245 day^− 1) and methane production (135 versus 149 L/kg VS_added); rather, it affected the lag phase. In a thermophilic HS-AD of riverbank grass, the mixture of inoculum with low and high total solids content (TS) helps increase the TS of inoculum and digestion process. An FIR of 2 was deducted to be the limit for a better startup time and higher volumetric productivity of methane.     

Robust ridge regression for estimating the effects of correlated gene expressions on phenotypic traits

Statistical packages such as edgeR and DESeq are intended to detect genes that are relevant to phenotypic traits and diseases. A few studies have also modeled the relationships between gene expressions and traits. In the presence of multicollinearity and outliers, which are unavoidable in genetic data, the robust ridge regression estimator can be applied with the trait value as the response variable and the gene expressions as explanatory variables. In some simulation scenarios, the robust ridge estimator is resistant to outliers and less susceptible to multicollinearity than the ordinary least-squares (OLS) estimator. This study investigated the reliability of the robust ridge estimator, in a scenario where the explanatory variables have tail-dependence and negative binomial distributions, by comparing its performance to that of OLS using vine copula to model the tail-dependence among gene expressions. The robust ridge estimator and OLS were both applied to an ecological dataset. First, statistical analysis was used to compare RNA sequencing data between two treatments; then, 15 differentially expressed genes were selected. Next, the regression parameter estimates of robust ridge and OLS for the effects of the 15 contigs (explanatory variables) on trait values (response variables) were compared. Robust ridge regression was found to detect fewer positive and negative slopes than OLS regression. These results indicate that robust ridge regression can be successfully applied for RNA sequencing analysis to estimate the effect of trait-associated genes using real data, and holds great promise as a tool for modeling the association between RNA expression and phenotypic traits.