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.

Small Proline Rich Protein-2 Expression and Regulation in the Caco-2 model of Intestinal Epithelial Differentiation along the Crypt-Villus Axis

Small proline-rich protein-2 (SPRR2) functions as a determinant of flexibility and permeability in the mature cornified envelope of the skin. SPRR2 is strongly upregulated by the commensal flora and may mediate signaling to differentiated epithelia of the small intestine and colon. Yet, SPRR2 function in the GI tract is largely unexplored. Using the Caco-2 model of intestinal epithelial differentiation along the crypt-villus axis, we hypothesized that SPRR2 would be preferentially expressed in post-confluent differentiated Caco-2 cells and examined SPRR2 regulation by the protein kinase A pathway (PKA) and short chain fatty acids (SCFAs). Differentiation-dependent SPRR2 expression was examined in cytoskeletal-, membrane-, and nuclear-enriched fractions by immunoblotting and confocal immunofluorescence. We studied the effect of SCFAs, known inducers of differentiation, on SPRR2 expression in pre-confluent undifferentiated Caco-2 cells and explored potential mechanisms involved in this induction using MAP kinase inhibitors. SPRR2 expression was also compared between HIEC crypt cells and 16 to 20 week primary fetal villus cells as well as in different segments in mouse small intestine and colon. We determined if SPRR2 is increased by gram negative bacteria such as S. typhimurium. SPRR2 expression increased in a differentiation-dependent manner in Caco-2 cells and was present in human fetal epithelial villus cells but absent in HIEC crypt cells. Differentiation-induced SPRR2 was down-regulated by 8-Br-cAMP as well as by forskolin/IBMX co-treatment. SPRR2 was predominantly cytoplasmic and did not accumulate in Triton X-100-insoluble cytoskeletal fractions. SPRR2 was present in the membrane- and nuclear-enriched fractions and demonstrated co-localization with F-actin at the apical actin ring. No induction was seen with the specific HDAC inhibitor trichostatin A, while SCFAs and the HDAC inhibitor SBHA all induced SPRR2. SCFA responses were inhibited by MAP kinase inhibitors SB203580 and U0126, thus suggesting that the SCFA effect may be mediated by orphan G-protein receptors GPR41 and GPR43. S. typhimurium induced SPRR2 in undifferentiated cells. We conclude that SPRR2 protein expression is associated with differentiated epithelia and is regulated by PKA signaling and by by-products of the bowel flora. This is the first report to establish an in vitro model to study the physiology and regulation of SPRR2.

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%.

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.