Genetic analysis of DMSP metabolism in the marine Roseobacter clade

Genetic, biochemical, bioinformatic and molecular approaches were used to analyse microbial catabolism of dimethylsulfoniopropionate (DMSP), an abundant anti-stress compound made by marine phytoplankton. Members of the Roseobacter clade of marine α-proteobacteria may catabolise DMSP by two different routes; demethylation to form methylmercaptopropionate (MMPA), and cleavage by DMSP-lyases, yielding volatile dimethylsulfide (DMS) plus acrylate. The DMSP-lyase, DddP, was purified from Roseovarius nubinhibens ISM and characterised in vitro. Nuclear magnetic resonance spectroscopy and gas chromatography confirmed bona fide DMSP lyase activity and mutation of predicted active-site residues abolished DMS production. DddP was also detected in the fungal coral pathogen Aspergillus sydowii, likely acquired from bacteria by inter-Domain horizontal-gene-transfer. A new DMSP-lyase, DddW, was identified in another Roseobacter species, Ruegeria pomeroyi DSS-3, initially by microarray-based demonstrations that transcription of dddW was induced in cells grown with DMSP. An adjacent gene encoded the cognate transcriptional regulator. Escherichia coli cells that over-expressed DddW cleaved DMSP into DMS plus acrylate. Thus, Ruegeria pomeroyi has three DMSP-lyases, with DddP and DddQ being known already; mutational analyses showed that all three contributed to its DMSP-dependent DMS (Ddd+) phenotype. Moran’s laboratory had shown that the DMSP demethylase was encoded by R. pomeroyi dmdA. I unveiled intimate links between the demethylation and the cleavage pathway(s). A key player is acuI, which is co-transcribed with dmdA, both genes being induced by DMSP and, more markedly, the DMSP-catabolite, acrylate. Furthermore, AcuI- mutants failed to grow on acrylate as sole carbon source and were more sensitive to its toxic effects. AcuI- mutants failed to grow on DMSP so, surprisingly, Ruegeria likely uses lyase pathway(s) to grow on this compound. A potential regulatory gene, transcribed divergently from dmdA, was also identified. The microarray also, wholly unexpectedly, revealed a suite of cox genes involved in carbon monoxide oxidation that was up-regulated in response to DMS

Geological mapping using high resolution regression modelled soil geochemistry

Geological mapping, the classification of bedrock into distinct identifiable units, has traditionally been conducted at the discretion of the field geologist on the basis of human-observable properties such as those of mineralogical composition and texture. In recent years technological developments have allowed the collection and analysis of ever more advanced quantitative geoscientific datasets. We are now approaching a point where migration of traditional mapping procedures to the digital domain is a feasible reality, with such benefits as consistency, transferability and transparency. One issue that we encounter is that the most geologically informative measurements, such as those of chemical composition, tend to have their sampling density limited by their high cost. Meanwhile, remote sensed data will tend towards extremely high sampling density, but may lack stand-alone geological significance. Nonparametric regression techniques have the potential to negate this issue by modelling the most geologically informative measurements as complex interactions of multiple remote sensed covariates. In this poster we present the use of random forest regression to model soil geochemistry in south west England using remote sensed data, and demonstrate how clustering of the predicted high resolution soil geochemistry is able to differentiate geological units – a process that can be trained to match pre-existing rock classifications. We find that random forest regression based on remote sensed data is capable of predicting element concentrations in soils with superior accuracy to that of ordinary kriging of sparsely sampled point data. Crucially the random forest predictions incorporate the high resolution structure of the remote sensed covariates. This allows geological units, in this case defined purely on the basis of the geochemical composition of their soils, to be mapped with sharp boundaries limited only by the resolution of the remote sensed covariates. It seems likely that such techniques could take centre stage in the future of geological mapping: improving not only on the consistency of classified maps based on human observations, but also allowing the continuous mapping of any geologically constrained variables, such as radon potential, to the best resolution and accuracy that our covariate datasets can support

Using HIV-attributable mortality to assess the impact of antiretroviral therapy on adult mortality in rural Tanzania.

BACKGROUND: The Tanzanian national HIV care and treatment programme has provided free antiretroviral therapy (ART) to HIV-positive persons since 2004. ART has been available to participants of the Kisesa open cohort study since 2005, but data to 2007 showed a slow uptake of ART and a modest impact on mortality. Additional data from the 2010 HIV serological survey provide an opportunity to update the estimated impact of ART in this setting. METHODS: The Kisesa Health and Demographic Surveillance Site (HDSS) has collected HIV serological data and demographic data, including verbal autopsy (VA) interviews since 1994. Serological data to the end of 2010 were used to make two estimates of HIV-attributable mortality, the first among HIV positives using the difference in mortality between HIV positives and HIV negatives, and the second in the population using the difference between the observed mortality rate in the whole population and the mortality rate among the HIV negatives. Four time periods (1994-1999, 2000-2004, 2005-2007, and 2008-2010) were used and HIV-attributable mortality estimates were analysed in detail for trends over time. A computer algorithm, InterVA-4, was applied to VA data to estimate the HIV-attributable mortality for the population, and this was compared to the estimates from the serological survey data. RESULTS: Among HIV-positive adults aged 45-59 years, high mortality rates were observed across all time periods in both males and females. In HIV-positive men, the HIV-attributable mortality was 91.6% (95% confidence interval (CI): 84.6%-95.3%) in 2000-2004 and 86.3% (95% CI: 71.1%-93.3%) in 2008-2010, while among women, the HIV-attributable mortality was 87.8% (95% CI: 71.1%-94.3%) in 2000-2004 and 85.8% (95% CI: 59.6%-94.4%) in 2008-2010. In the whole population, using the serological data, the HIV-attributable mortality among men aged 30-44 years decreased from 57.2% (95% CI: 46.9%-65.3%) in 2000-2004 to 36.5% (95% CI: 18.8%-50.1%) in 2008-2010, while among women the corresponding decrease was from 57.3% (95% CI: 49.7%-63.6%) to 38.7% (95% CI: 27.4%-48.2%). The HIV-attributable mortality in the population using estimates from the InterVA model was lower than that from HIV sero-status data in the period prior to ART, but slightly higher once ART became available. DISCUSSION: In the Kisesa HDSS, ART availability corresponds with a decline in adult overall mortality, although not as large as expected. Using InterVA to estimate HIV-attributable mortality showed smaller changes in HIV-related mortality following ART availability than the serological results

Gathering data on allegations of sexual abuse made against former disc jockey, Jimmy Savile

This paper will report on a collaboration between Social Work and Informatics academics and Library staff at the University. The focus of the work is to secure a data set on allegations of sexual abuse made against the former disc jockey, Jimmy Savile. The Savile case generated massive publicity; initial allegations led to what is claimed to be hundreds of others. Its reverberations have engulfed major institutions such as the BBC in controversy. Official reports label Savile a predatory paedophile and those making allegations against him victims. However, more questioning interpretations exist on the internet. The focus of our project is to secure a central source of such more questioning accounts, the blogging activity of a woman known as Anna Raccoon and the correspondence and list of contacts upon which it is based. Anna Raccoon is a former resident of Duncroft residential School from whence initial allegations against Savile emanate. Anna has terminal cancer and, as a result, her important counter-hegemonic account risks being lost. The circumstances of Anna’s illness prompted a successful bid to a call from the ESRC for ‘Urgent’ research. Its aim is to collate important data relating to the Savile case and to open it up to examination and analysis across a range of academic disciplines. This paper will outline the nature of the project, which involves accessing non-traditional data types, such as internet blogs and comments and archiving these alongside more traditional data types such as interview transcriptions and published reports. It will address questions textual analysis in particular using text mining to identify people, places and organisations within the data. Crucially, it will also discuss some of the ethical implications of engaging with data around what is a sensitive and contested subject

Universal Mortality Law, Life Expectancy and Immortality

Well protected human and laboratory animal populations with abundant resources are evolutionary unprecedented, and their survival far beyond reproductive age may be a byproduct rather than tool of evolution. Physical approach, which takes advantage of their extensively quantified mortality, establishes that its dominant fraction yields the exact law, and suggests its unusual mechanism. The law is universal for all animals, from yeast to humans, despite their drastically different biology and evolution. It predicts that the universal mortality has short memory of the life history, at any age may be reset to its value at a significantly younger age, and mean life expectancy extended (by biologically unprecedented small changes) from its current maximal value to immortality. Mortality change is rapid and stepwise. Demographic data and recent experiments verify these predictions for humans, rats, flies, nematodes and yeast. In particular, mean life expectancy increased 6-fold (to "human" 430 years), with no apparent loss in health and vitality, in nematodes with a small number of perturbed genes and tissues. Universality allows one to study unusual mortality mechanism and the ways to immortality

Expansion of myeloid-derived suppressor cells contributes to metabolic osteoarthritis through subchondral bone remodeling

Background: Osteoarthritis (OA) subsequent to acute joint injury accounts for a significant proportion of all arthropathies. Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of myeloid progenitor cells classically known for potent immune-suppressive activity; however, MDSCs can also differentiate into osteoclasts. In addition, this population is known to be expanded during metabolic disease. The objective of this study was to determine the role of MDSCs in the context of OA pathophysiology. Methods: In this study, we examined the differentiation and functional capacity of MDSCs to become osteoclasts in vitro and in vivo using mouse models of OA and in MDSC quantitation in humans with OA pathology relative to obesity status. Results: We observed that MDSCs are expanded in mice and humans during obesity. MDSCs were expanded in peripheral blood of OA subjects relative to body mass index and in mice fed a high-fat diet (HFD) compared to mice fed a low-fat diet (LFD). In mice, monocytic MDSC (M-MDSC) was expanded in diet-induced obesity (DIO) with a further expansion after destabilization of the medial meniscus (DMM) surgery to induce post-traumatic OA (PTOA) (compared to sham-operated controls). M-MDSCs from DIO mice had a greater capacity to form osteoclasts in culture with increased subchondral bone osteoclast number. In humans, we observed an expansion of M-MDSCs in peripheral blood and synovial fluid of obese subjects compared to lean subjects with OA. Conclusion: These data suggest that MDSCs are reprogrammed in metabolic disease, with the potential to contribute towards OA progression and severity

C-Reactive Protein and the Disease Analog Model May Identify Predisposed Pre-Obese African-American Women

While the obesity rate in the Unites States has been reported to have hit a plateau, the overall percentage of obese Americans remains alarmingly high (27% self-reported, 33% population estimate). While the subgroup with the highest 2010 obesity rate is Black, non-Hispanic women (41.9%), there remains a disparity in the research with regards to this population group. The implication of an elevated obese population puts a strain on health care, overall quality of life, and is associated with a number of other co-morbidities. Given this background, pilot work to evaluate a disease analog model for obesity would be useful with the potential for identifying seemingly normal-weight individuals who are most susceptible to developing obesity

A machine learning approach to geochemical mapping

Geochemical maps provide invaluable evidence to guide decisions on issues of mineral exploration, agriculture, and environmental health. However, the high cost of chemical analysis means that the ground sampling density will always be limited. Traditionally, geochemical maps have been produced through the interpolation of measured element concentrations between sample sites using models based on the spatial autocorrelation of data (e.g. semivariogram models for ordinary kriging). In their simplest form such models fail to consider potentially useful auxiliary information about the region and the accuracy of the maps may suffer as a result. In contrast, this study uses quantile regression forests (an elaboration of random forest) to investigate the potential of high resolution auxiliary information alone to support the generation of accurate and interpretable geochemical maps. This paper presents a summary of the performance of quantile regression forests in predicting element concentrations, loss on ignition and pH in the soils of south west England using high resolution remote sensing and geophysical survey data. Through stratified 10-fold cross validation we find the accuracy of quantile regression forests in predicting soil geochemistry in south west England to be a general improvement over that offered by ordinary kriging. Concentrations of immobile elements whose distributions are most tightly controlled by bedrock lithology are predicted with the greatest accuracy (e.g. Al with a cross-validated R2 of 0.79), while concentrations of more mobile elements prove harder to predict. In addition to providing a high level of prediction accuracy, models built on high resolution auxiliary variables allow for informative, process based, interpretations to be made. In conclusion, this study has highlighted the ability to map and understand the surface environment with greater accuracy and detail than previously possible by combining information from multiple datasets. As the quality and coverage of remote sensing and geophysical surveys continue to improve, machine learning methods will provide a means to interpret the otherwise-uninterpretable