Chemical constituents associated with sewage settling velocity profiles

A methodology has been developed to measure the chemical constituents associated with the settling velocity fractions that comprise a wastewater settling velocity profile (SVP). 31 wastewater samples were collected from fifteen different catchments in England and Wales. For each catchment, settling velocity and associated chemical constituent profiles were determined. The results are mainly for Suspended Solids (SS), Chemical Oxygen Demand (COD), Phosphorus (P) and Total Kjeadahl Nitrogen (TKN), however these are supplemented by the results from 5 events for a suite of heavy metals. COD, P, Hg, Mn and Pb were found to be predominantly associated with the solid phase and TKN, Al, Cu and Fe with the liquor phase of the wastewater samples. The results in the thesis are expressed as mass of pollutant (g) per mass total SS (kg). COD and P were found to be mainly associated with the sinkers and had a particular affinity for solids with settling velocities in the range 0.9-9.03mm/sec. TKN was mainly associated with the soluble phase, however of the solids that did settle, a peak was found to be associated within the settling velocity range 0.9-9.03mm/sec. The relationships identified for COD and P were generally found to be unaffected by flow conditions and catchment characteristics. However, TKN was found to be affected by catchment type. Data on the distribution of heavy metals was limited, and no specific relationships with solids were identified. 16 mean pollutant profiles are presented in the thesis. Presentation of the data in this form will enable the results to be of use in the design of sedimentation devices to predict removal efficiencies for solids and associated pollutants. The findings of the research may also be applied to modelling tools to provide further characteristics on the solids that are modelled than is currently used. This would enhance the overall performance of tools used in integrated catchment modelling.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).