Advanced multivariate analysis to assess remediation of hydrocarbons in soils

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

Still searching for graves: an analytical strategy for interpreting geophysical data used in the search for “unmarked” graves

Searching for and mapping the physical extent of unmarked graves using geophysical techniques has proven difficult in many cases. The success of individual geophysical techniques for detecting graves depends on a site-by-site basis. Significantly, detection of graves often results from measured contrasts that are linked to the background soils rather than the type of archaeological feature associated with the grave. It is evident that investigation of buried remains should be considered within a 3D space as the variation in burial environment can be extremely varied through the grave. Within this paper, we demonstrate the need for a multi-method survey strategy to investigate unmarked graves, as applied at a “planned” but unmarked pauper’s cemetery. The outcome from this case study provides new insights into the strategy that is required at such sites. Perhaps the most significant conclusion is that unmarked graves are best understood in terms of characterization rather than identification. In this paper, we argue for a methodological approach that, while following the current trends to use multiple techniques, is fundamentally dependent on a structured approach to the analysis of the data. The ramifications of this case study illustrate the necessity of an integrated strategy to provide a more holistic understanding of unmarked graves that may help aid in management of these unseen but important aspects of our heritage. It is concluded that the search for graves is still a current debate and one that will be solved by methodological rather than technique-based arguments.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.