GODIVA2: interactive visualization of environmental data on the Web

GODIVA2 is a dynamic website that provides visual access to several terabytes of physically distributed, four-dimensional environmental data. It allows users to explore large datasets interactively without the need to install new software or download and understand complex data. Through the use of open international standards, GODIVA2 maintains a high level of interoperability with third-party systems, allowing diverse datasets to be mutually compared. Scientists can use the system to search for features in large datasets and to diagnose the output from numerical simulations and data processing algorithms. Data providers around Europe have adopted GODIVA2 as an INSPIRE-compliant dynamic quick-view system for providing visual access to their data.

Climate predictability in the second year

In this paper, the predictability of climate arising from ocean heat content (OHC) anomalies is investigated in the HadCM3 coupled atmosphere–ocean model. An ensemble of simulations of the twentieth century are used to provide initial conditions for a case study. The case study consists of two ensembles started from initial conditions with large differences in regional OHC in the North Atlantic, the Southern Ocean and parts of the West Pacific. Surface temperatures and precipitation are on average not predictable beyond seasonal time scales, but for certain initial conditions there may be longer predictability. It is shown that, for the case study examined here, some aspects of tropical precipitation, European surface temperatures and North Atlantic sea-level pressure are potentially predictable 2 years ahead. Predictability also exists in the other case studies, but the climate variables and regions, which are potentially predictable, differ. This work was done as part of the Grid for Coupled Ensemble Prediction (GCEP) eScience project.

Decadal climate prediction (project GCEP)

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

Modeling the user knowledge by belief networks

This paper describes the user modeling component of EPIAIM, a consultation system for data analysis in epidemiology. The component is aimed at representing knowledge of concepts in the domain, so that their explanations can be adapted to user needs. The first part of the paper describes two studies aimed at analysing user requirements. The first one is a questionnaire study which examines the respondents' familiarity with concepts. The second one is an analysis of concept descriptions in textbooks and from expert epidemiologists, which examines how discourse strategies are tailored to the level of experience of the expected audience. The second part of the paper describes how the results of these studies have been used to design the user modeling component of EPIAIM. This module works in a two-step approach. In the first step, a few trigger questions allow the activation of a stereotype that includes a "body" and an "inference component". The body is the representation of the body of knowledge that a class of users is expected to know, along with the probability that the knowledge is known. In the inference component, the learning process of concepts is represented as a belief network. Hence, in the second step the belief network is used to refine the initial default information in the stereotype's body. This is done by asking a few questions on those concepts where it is uncertain whether or not they are known to the user, and propagating this new evidence to revise the whole situation. The system has been implemented on a workstation under UNIX. An example of functioning is presented, and advantages and limitations of the approach are discussed.

Studies on particulate matter (PM10) and its precursors over urban environment of Reading, UK

The extent to which airborne particles penetrate into the human respiratory system is determined mainly by their size, with possible health effects. The research over the scientific evidence of the role of airborne particles in adverse health effects has been intensified in recent years. In the present study, seasonal variations of PM10 and its relation with anthropogenic activities have been studied by using the data from UK National Air Quality Archive over Reading, UK. The diurnal variation of PM10 shows a morning peak during 7:00-10:00 LT and an evening peak during 19:00-22:00 LT. 3 The variation between 12:00 and 17:00 LT remains more or less steady for PM10 with the minimum value of similar to 16 mu g m(-3). PM10 and black smoke (BS) concentrations during weekdays were found to be high compared to weekends. A reduction in the concentration of PM10 has been found during the Christmas holidays compared to normal days during December. Seasonal variations of PM10 showed high values during spring compared to other seasons. A linear relationship has been found between PM10 and NO, during March, July, November and December suggesting that most of the PM10 is due to local traffic exhaust emissions. PM10 and SO2 concentrations showed positive correlation with the correlation coefficient of R-2 = 0.65 over the study area. Seasonal variations of SO2 and NOx showed high concentrations during winter and low concentrations during spring. Fraction of BS in PM10 has been found to be 50% during 2004 over the study area. (C) 2005 Elsevier Ltd. All rights reserved.

Isolation of lactoperoxidase using different cation exchange resins by batch and column procedures

Lactoperoxidase (LP) was isolated from whey protein by cation-exchange using Carboxymethyl resin (CM-25C) and Sulphopropyl Toyopearl resin (SP-650C). Both batch and column procedures were employed and the adsorption capacities and extraction efficiencies were compared. The resin bed volume to whey volume ratios were 0.96:1.0 for CM-25C and ≤ 0.64:1.0 for SP-650 indicating higher adsorption capacity of SP-650 compared to CM-25C. The effluent LP activity depended on both the enzyme activity in the whey and the amount of whey loaded on the column within the saturation limits of the resin. The percentage recovery was high below the saturation point and fell off rapidly with over-saturation. While effective recovery was achieved with column extraction procedures, the recovery was poor in batch procedures. The whey-resin contact time had little impact on the enzyme adsorption. SDS PAGE and HPLC analyses were also carried out, the purity was examined and the proteins characterised in terms of molecular weights. Reversed phase HPLC provided clear distinction of the LP and lactoferrin (LF) peaks. The enzyme purity was higher in column effluents compared to batch effluents, judged on the basis of the clarity of the gel bands and the resolved peaks in HPLC chromatograms.

Apparent digestibility and nitrogen utilization of diets based on maize silage harvested at three stages of maturity and fed to steers

Advancing maize crop maturity is associated with changes in ear-to-stover ratio which may have consequences for the digestibility of the ensiled crop. The apparent digestibility and nitrogen retention of three diets (Early, Mid and Late) containing maize silages made from maize of advancing harvest date [dry matter (DM) contents of the maize silages were 273, 314 and 367 g kg(-1) for the silages in the Early, Mid and Late diets respectively], together with a protein supplement offered in sufficient quantities to make the diets isonitrogenous, were measured in six Holstein-Friesian steers in an incomplete Latin square design with four periods. Dry-matter intake of maize silage tended to be least for the Early diet and greatest for the Medium diet (P=0(.)182). Apparent digestibility of DM and organic matter did not differ between diets. Apparent digestibility of energy was lowest in the Late diet (P = 0(.)057) and the metabolizable energy concentrations of the three silages were calculated as 11(.)0, 11(.)1 and 10(.)6 MJ kg(-1) DM for the Early, Medium and Late diets respectively (P = 0(.)068). No differences were detected between diets in starch digestibility but the number of undamaged grains present in the faeces of animals fed the Late diet was significantly higher than with the Early and Mid diets (P = 0(.)006). The apparent digestibility of neutral-detergent fibre of the diets reduced significantly as silage DM content increased (P = 0(.)012) with a similar trend for the apparent digestibility of acid-detergent fibre (P = 0(.)078). Apparent digestibility of nitrogen (N) was similar for the Early and Mid diets, both being greater than the Late diet (P = 0(.)035). Nitrogen retention did not differ between diets. It was concluded that delaying harvest until the DM content is above 300 g kg(-1) can negatively affect the nutritive value of maize silage in the UK.

Relationship between incidence and severity of cashew gummosis in semiarid north-eastern Brazil

The incidence-severity relationship for cashew gummosis, caused by Lasiodiplodia theobromae, was studied to determine the feasibility of using disease incidence to estimate indirectly disease severity in order to establish the potential damage caused by this disease in semiarid north-eastern Brazil. Epidemics were monitored in two cashew orchards, from 1995 to 1998 in an experimental field composed of 28 dwarf clones, and from 2000 to 2002 in a commercial orchard of a single clone. The two sites were located 10 km from each other. Logarithmic transformation achieved the best linear adjustment of incidence and severity data as determined by coefficients of determination for place, age and pooled data. A very high correlation between incidence and severity was found in both fields, with different disease pressures, different cashew genotypes, different ages and at several epidemic stages. Thus, the easily assessed gummosis incidence could be used to estimate gummosis severity levels.