The ocean sampling day consortium.

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world's oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

Sediment contaminant surveillance in Milford Haven Waterway

Sediment contaminants were monitored in Milford Haven Waterway (MHW) since 1978 (hydrocarbons) and 1982 (metals), with the aim of providing surveillance of environmental quality in one of the UK’s busiest oil and gas ports. This aim is particularly important during and after large-scale investment in liquefied natural gas (LNG) facilities. However, methods inevitably have changed over the years, compounding the difficulties of coordinating sampling and analytical programmes. After a review by the MHW Environmental Surveillance Group (MHWESG), sediment hydrocarbon chemistry was investigated in detail in 2010. Natural Resources Wales (NRW) contributed their MHW data for 2007 and 2012, collected to assess the condition of the Special Area of Conservation (SAC) designated under the European Union Habitats Directive. Datasets during 2007-2012 have thus been more comparable. The results showed conclusively that a MHW-wide peak in concentrations of sediment polycyclic aromatic hydrocarbons (PAHs), metals and other contaminants occurred in late 2007. This was corroborated by independent annual monitoring at one centrally-located station with peaks in early 2008 and 2011. The spatial and temporal patterns of recovery from the 2007 peak, shown by MHW-wide surveys in 2010 and 2012, indicate several probable causes of contaminant trends, as follows: atmospheric deposition, catchment runoff, sediment resuspension from dredging, and construction of two LNG terminals and a power station. Adverse biological effects predictable in 2007 using international sediment quality guidelines, were independently tested by data from monitoring schemes of more than a decade duration in MHW (starfish, limpets), and in the wider SAC (grey seals). Although not proving cause and effect, many of these potential biological receptors showed a simultaneous negative response to the elevated 2007 contamination following intense dredging activity in 2006. Wetland bird counts were typically at a peak in the winter of 2005-2006 previous to peak dredging. In the following winter 2006-2007, shelduck in Pembroke River showed their lowest winter count, and spring 2007 was the largest ever drop in numbers of broods across MHW between successive breeding seasons. Wigeon counts in Pembroke River were again low in late 2012 after further dredging nearby. These results are strongly supported by PAH data reported previously from invertebrate bioaccumulation studies in MHW 2007-2010, themselves closely reflecting sediment

Wireless networked control systems with QoS-based sampling

Closing feedback loops using an IEEE 802.11b ad hoc wireless communication network incurs many challenges sensitivity to varying channel conditions and lower physical transmission rates tend to limit the bandwidth of the communication channel. Given that the bandwidth usage and control performance are linked, a method of adapting the sampling interval based on an 'a priori', static sampling policy has been proposed and, more significantly, assuring stability in the mean square sense using discrete-time Markov jump linear system theory. Practical issues including current limitations of the 802.11 b protocol, the sampling policy and stability are highlighted. Simulation results on a cart-mounted inverted pendulum show that closed-loop stability can be improved using sample rate adaptation and that the control design criteria can be met in the presence of channel errors and severe channel contention.

Deep sampling and testing in soft stratified clay

A 37-m thick layer of stratified clay encountered during a site investigation at Swann's Bridge, near the sea-coast at Limavady, Northern Ireland, is one of the deepest and thickest layers of this type of material recorded in Ireland. A study of the relevant literature and stratigraphic evidence obtained from the site investigation showed that despite being close to the current shoreline, the clay was deposited in a fresh-water glacial lake formed approximately 13 000 BP. The 37-m layer of clay can be divided into two separate zones. The lower zone was deposited as a series of laminated layers of sand, silt, and clay, whereas the upper zone was deposited as a largely homogeneous mixture. A comprehensive series of tests was carried out on carefully selected samples from the full thickness of the deposit. The results obtained from these tests were complex and confusing, particularly the results of tests done on samples from the lower zone. The results of one-dimensional compression tests, unconsolidated undrained triaxial tests, and consolidated undrained triaxial compression tests showed that despite careful sampling, all of the specimens from the lower zone exhibited behaviour similar to that of reconstituted clays. It was immediately clear that the results needed explanation. This paper studies possible causes of the results from tests carried out on the lower Limavady clay. It suggests a possible mechanism based on anisotropic elasticity, yielding, and destructuring that provides an understanding of the observed behaviour.Key words: clay, laminations, disturbance, yielding, destructuring, reconstituted.

Development of sampling methods for Raman analysis of solid dosage forms of therapeutic and illicit drugs

The results of a study aimed at determining the most important experimental parameters for automated, quantitative analysis of solid dosage form pharmaceuticals (seized and model 'ecstasy' tablets) are reported. Data obtained with a macro-Raman spectrometer were complemented by micro-Raman measurements, which gave information on particle size and provided excellent data for developing statistical models of the sampling errors associated with collecting data as a series of grid points on the tablets' surface. Spectra recorded at single points on the surface of seized MDMA-caffeine-lactose tablets with a Raman microscope (lambda(ex) = 785 nm, 3 mum diameter spot) were typically dominated by one or other of the three components, consistent with Raman mapping data which showed the drug and caffeine microcrystals were ca 40 mum in diameter. Spectra collected with a microscope from eight points on a 200 mum grid were combined and in the resultant spectra the average value of the Raman band intensity ratio used to quantify the MDMA: caffeine ratio, mu(r), was 1.19 with an unacceptably high standard deviation, sigma(r), of 1.20. In contrast, with a conventional macro-Raman system (150 mum spot diameter), combined eight grid point data gave mu(r) = 1.47 with sigma(r) = 0.16. A simple statistical model which could be used to predict sigma(r) under the various conditions used was developed. The model showed that the decrease in sigma(r) on moving to a 150 mum spot was too large to be due entirely to the increased spot diameter but was consistent with the increased sampling volume that arose from a combination of the larger spot size and depth of focus in the macroscopic system. With the macro-Raman system, combining 64 grid points (0.5 mm spacing and 1-2 s accumulation per point) to give a single averaged spectrum for a tablet was found to be a practical balance between minimizing sampling errors and keeping overhead times at an acceptable level. The effectiveness of this sampling strategy was also tested by quantitative analysis of a set of model ecstasy tablets prepared from MDEA-sorbitol (0-30% by mass MDEA). A simple univariate calibration model of averaged 64 point data had R-2 = 0.998 and an r.m.s. standard error of prediction of 1.1% whereas data obtained by sampling just four points on the same tablet showed deviations from the calibration of up to 5%.