Prediction of steady-state flux through variably saturated zones within a septic absorption trench

This paper describes effluent flow dynamics within a septic absorption system and the prediction of flow through the biomat and sub-biomat zone. Using soil hydraulic properties in a one dimensional model we demonstrate how soil hydraulic properties interact with biomat resistances to determine long-term acceptance rate (LTAR). The LTAR is a key parameter used in the Australian and New Zealand Standard AS1547:2000 to calculate the area of trench required to ensure trenches are not overloaded. Results show that several orders of magnitude variation in saturated hydraulic conductivity (Ks) collapse to a one order of magnitude variation in LTAR. These results are calculated from a model using basic flow theory, allowing LTAR to be estimated for any combination of biomat resistance and soil hydraulic properties. To increase the reliability of prediction of septic trench hydrology, HYDRUS 2D was used to model two dimensional flow. For more permeable soils, the exfiltration zone above sidewall biomat growth is shown to be a key pathway for excess effluent flow.

Exploiting sequence dependencies in the prediction of peroxisomal proteins

Prediction of peroxisomal matrix proteins generally depends on the presence of one of two distinct motifs at the end of the amino acid sequence. PTS1 peroxisomal proteins have a well conserved tripeptide at the C-terminal end. However, the preceding residues in the sequence arguably play a crucial role in targeting the protein to the peroxisome. Previous work in applying machine learning to the prediction of peroxisomal matrix proteins has failed W capitalize on the full extent of these dependencies. We benchmark a range of machine learning algorithms, and show that a classifier - based on the Support Vector Machine - produces more accurate results when dependencies between the conserved motif and the preceding section are exploited. We publish an updated and rigorously curated data set that results in increased prediction accuracy of most tested models.