Life-Cycle Assessment of Biosolids Processing Options

Biosolids, also known as sewage sludge, are reusable organic materials separated from sewage during treatment. They can be managed in a variety of ways. Different options for biosolids handling in Sydney, Australia, are compared in this study using life-cycle assessment. Two key comparisons are made: of system scenarios (scenario 1 is local dewatering and lime amendment; scenario 2 is a centralized drying system) and of technologies (thermal drying versus lime amendment). The environmental issues addressed are energy consumption, global warming potential (GWP), and human toxicity potential (HTP).
Scenario 2 would consume 24% more energy than scenario 1. This is due to the additional electricity for pumping and particularly the petrochemical methane that supplements biogas in the drier. A centralized system using the same technologies as scenario 1 has approximately the same impacts. The GWP and HTP of the different scenarios do not differ significantly.
The assessment of technology choices shows significant differences. The ample supply of endogenous biogas at North Head sewage treatment plant for the drying option allows reductions, relative to the lime-amendment option, of 68% in energy consumption, 45% in GWP, and 23% in HTP.
Technology choices have more significant influence on the environmental profile of biosolids processing than does the choice of system configurations. Controlling variables for environmental improvement are the selection of biogas fuel, avoidance of coalsourced electrical energy, minimization of trucking distances, and raising the solids content of biosolids products.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.