Microbial inactivation of Pseudomonas putida and Pichia pastoris using gene silencing.

Antisense deoxyoligonucleotide (ASO) gene silencing was investigated as a potential disinfection tool for industrial and drinking water treatment application. ASOs bind with their reverse complementary mRNA transcripts thereby blocking protein translation. While ASO silencing has mainly been studied in medicine, it may be useful for modulating gene expression and inactivating microorganisms in environmental applications. In this proof of concept work, gene targets were sh ble (zeocin resistance) and todE (catechol-2,3-dioxygenase) in Pichia pastoris and npt (kanamycin resistance) in Pseudomonas putida. A maximum 0.5-fold decrease in P. pastoris cell numbers was obtained following a 120 min incubation with single-stranded DNA (ssDNA) concentrations ranging from 0.2 to 200 nM as compared to the no ssDNA control. In P. putida, a maximum 5.2-fold decrease was obtained after 90 min with 400 nM ssDNA. While the silencing efficiencies varied for the 25 targets tested, these results suggest that protein activity as well as microbial growth can be altered using ASO gene silencing-based tools. If successful, this technology has the potential to eliminate some of the environmental and health issues associated with the use of strong chemical biocides. However, prior to its dissemination, more research is needed to increase silencing efficiency and develop effective delivery methods.

Some models for discretized series of events

A discretized series of events is a binary time series that indicates whether or not events of a point process in the line occur in successive intervals. Such data are common in environmental applications. We describe a class of models for them, based on an unobserved continuous-time discrete-state Markov process, which determines the rate of a doubly stochastic Poisson process, from which the binary time series is constructed by discretization. We discuss likelihood inference for these processes and their second-order properties and extend them to multiple series. An application involves modeling the times of exposures to air pollution at a number of receptors in Western Europe.

Trace Element Abundance and Renal Disease

This research investigates the relationship between elevated trace elements in soils, stream sediments and stream water and the prevalence of Chronic Kidney Disease (CKD). The study uses a collaboration of datasets provided from the UK Renal Registry Report (UKRR) on patients with renal diseases requiring treatment including Renal Replacement Therapy (RRT), the soil geochemical dataset for Northern Ireland provided by the Tellus Survey, Geological Survey of Northern Ireland (GSNI) and the bioaccessibility of Potentially Toxic Elements (PTEs) from soil samples which were obtained from the Unified Barge Method (UBM). The relationship between these factors derives from the UKRR report which highlights incidence rates of renal impaired patients showing regional variation with cases of unknown aetiology. Studies suggest a potential cause of the large variation and uncertain aetiology is associated with underlying environmental factors such as the oral bioaccessibility of trace elements in the gastrointestinal tract.
As previous research indicates that long term exposure is related to environmental factors, Northern Ireland is ideally placed for this research as people traditionally live in the same location for long periods of time. Exploratory data analysis and multivariate analyses are used to examine the soil, stream sediments and stream water geochemistry data for a range of key elements including arsenic, lead, cadmium and mercury identified from a review of previous renal disease literature. The spatial prevalence of patients with long term CKD is analysed on an area basis. Further work includes cluster analysis to detect areas of low or high incidences of CKD that are significantly correlated in space, Geographical Weighted Regression (GWR) and Poisson kriging to examine locally varying relationship between elevated concentrations of PTEs and the prevalence of CKD.

Spatial and Temporal Changes in Soil Organic Carbon Assessment using an Integrated Geostatistical and Compositional Data Analysis Approach

The Irish and UK governments, along with other countries, have made a commitment to limit the concentrations of greenhouse gases in the atmosphere by reducing emissions from the burning of fossil fuels. This can be achieved (in part) through increasing the sequestration of CO2 from the atmosphere including monitoring the amount stored in vegetation and soils. A large proportion of soil carbon is held within peat due to the relatively high carbon density of peat and organic-rich soils. This is particularly important for a country such as Ireland, where some 16% of the land surface is covered by peat. For Northern Ireland, it has been estimated that the total amount of carbon stored in vegetation is 4.4Mt compared to 386Mt stored within peat and soils. As a result it has become increasingly important to measure and monitor changes in stores of carbon in soils. The conservation and restoration of peat covered areas, although ongoing for many years, has become increasingly important. This is summed up in current EU policy outlined by the European Commission (2012) which seeks to assess the relative contributions of the different inputs and outputs of organic carbon and organic matter to and from soil. Results are presented from the EU-funded Tellus Border Soil Carbon Project (2011 to 2013) which aimed to improve current estimates of carbon in soil and peat across Northern Ireland and the bordering counties of the Republic of Ireland.
Historical reports and previous surveys provide baseline data. To monitor change in peat depth and soil organic carbon, these historical data are integrated with more recently acquired airborne geophysical (radiometric) data and ground-based geochemical data generated by two surveys, the Tellus Project (2004-2007: covering Northern Ireland) and the EU-funded Tellus Border project (2011-2013) covering the six bordering counties of the Republic of Ireland, Donegal, Sligo, Leitrim, Cavan, Monaghan and Louth. The concept being applied is that saturated organic-rich soil and peat attenuate gamma-radiation from underlying soils and rocks. This research uses the degree of spatial correlation (coregionalization) between peat depth, soil organic carbon (SOC) and the attenuation of the radiometric signal to update a limited sampling regime of ground-based measurements with remotely acquired data. To comply with the compositional nature of the SOC data (perturbations of loss on ignition [LOI] data), a compositional data analysis approach is investigated. Contemporaneous ground-based measurements allow corroboration for the updated mapped outputs. This provides a methodology that can be used to improve estimates of soil carbon with minimal impact to sensitive habitats (like peat bogs), but with maximum output of data and knowledge.