Temperature Dependent Depuration of Norovirus GII and Tulane Virus from Oysters (Crassostrea gigas)

Raw oysters are considered a culinary delicacy but are frequently the culprit in food-borne norovirus (NoV) infections. As commercial depuration procedures are currently unable to efficiently eliminate NoV from oysters, an optimisation of the process should be considered. This study addresses the ability of elevated water temperatures to enhance the elimination of NoV and Tulane virus (TuV) from Pacific oysters (Crassostrea gigas). Both viruses were experimentally bioaccumulated in oysters, which were thereafter depurated at 12 °C and 17 °C for 4 weeks. Infectious TuV and viral RNA were monitored weekly for 28 days by TCID₅₀ and (PMAxx-) RT-qPCR, respectively. TuV RNA was more persistent than NoV and decreased by?<?0.5 log₁₀ after 14 days, while NoV reductions were already?>?1.0 log₁₀ at this time. For RT-qPCR there was no detectable benefit of elevated water temperatures or PMAxx for either virus (p?>?0.05). TuV TCID₅₀ decreased steadily, and reductions were significantly different between the two temperatures (p?<?0.001). This was most evident on days 14 and 21 when reductions at 17 °C were 1.3–1.7 log₁₀ higher than at 12 °C. After 3 weeks, reductions?>?3.0 log₁₀ were observed at 17 °C, while at 12 °C reductions did not exceed 1.9 log₁₀. The length of depuration also had an influence on virus numbers. TuV reductions increased from?<?1.0 log₁₀ after seven days to?>?4.0 log₁₀ after 4 weeks. This implies that an extension of the depuration period to more than seven days, possibly in combination with elevated water temperatures, may be beneficial for the inactivation and removal of viral pathogens.

     

Phenological sequences reveal aggregate life history response to climatic warming

Climatic warming is associated with organisms breeding earlier in the season than is typical for their species. In some species, however, response to warming is more complex than a simple advance in the timing of all life history events preceding reproduction. Disparities in the extent to which different components of the reproductive phenology of organisms vary with climatic warming indicate that not all life history events are equally responsive to environmental variation. Here, we propose that our understanding of phenological response to climate change can be improved by considering entire sequences of events comprising the aggregate life histories of organisms preceding reproduction. We present results of a two-year warming experiment conducted on 33 individuals of three plant species inhabiting a low-arctic site. Analysis of phenological sequences of three key events for each species revealed how the aggregate life histories preceding reproduction responded to warming, and which individual events exerted the greatest influence on aggregate life history variation. For alpine chickweed (Cerastium alpinum), warming elicited a shortening of the duration of the emergence stage by 2.5 days on average, but the aggregate life history did not differ between warmed and ambient plots. For gray willow (Salix glauca), however, all phenological events monitored occurred earlier on warmed than on ambient plots, and warming reduced the aggregate life history of this species by 22 days on average. Similarly, in dwarf birch (Betula nana), warming advanced flower bud set, blooming, and fruit set and reduced the aggregate life history by 27 days on average. Our approach provides important insight into life history responses of many organisms to climate change and other forms of environmental variation. Such insight may be compromised by considering changes in individual phenological events in isolation.     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

Soil pollution in day-care centers and playgrounds in Norway: national action plan for mapping and remediation

Systematic geochemical mapping based on sampling and analysis of surface soils (0–2 cm) has been carried out in several Norwegian cities. The soils in the oldest parts of the cities are contaminated with metals (especially Pb) and polycyclic aromatic hydrocarbons (PAH). Due to the fact that children are often in contact with surface soil, it was realized that special focus had to be directed towards soils in day-care centers and playgrounds. The first mapping and remedy program was initiated in Trondheim in 1996. Here, the importance of copper–chromium–arsenic (CCA)-pressure-impregnated wood in playing equipment as a pollution source for soils was documented, and a process was started with the aim to ban this product. Soils from day-care centers in the inner city of Bergen were polluted to a degree that required remediation in 45% out of 87 centers, mainly due to high concentrations of Pb and benzo(a)pyrene. In Oslo, 38% of 700 day-care centers needed remediation due to soil pollution by Pb, BaP, Cd, Hg, Ni and PCB. Removal of CCA-impregnated wood was necessary in more than half of the day-care centers The Norwegian parliament has decided to investigate all outdoor playing areas in day-care centers, playgrounds and schoolyards in Norway, starting in 2007 with day-care centers in the ten largest cities and five most important industrial areas. The Norwegian Institute of Public Health has developed quality criteria for soils in day-care centers and playgrounds for As, Cd, Cr⁶⁺, Hg, Ni, Pb, zinc, PAH_sum16, benzo(a)pyrene, and PCB_sum7. The Geological Survey of Norway has developed guidance for mapping of soil pollution (sampling, chemical analysis and reporting) in day-care centers. Especially the sampling strategy has been developed in the period 1996–2007, and the preferred sampling strategy is to collect at least 10 samples of surface soil (0–2 cm) from (1) “original soil” on the site, (2) artificial man-made “hills”, and (3) soils used for growing vegetables. A total number of 2,000 day-care-centres are to be investigated, and necessary remediation should be completed before the end of 2010.     

Assessing Landscape Condition Relative to Water Resources in the Western United States: A Strategic Approach

The Environmental Monitoring and Assessment Program (EMAP) is proposing an ambitious agenda to assess the status of streams and estuaries in a 12-State area of the western United States by the end of 2003. Additionally, EMAP is proposing to access landscape conditions as they relate to stream and estuary conditions across the west. The goal of this landscape project is to develop a landscape model that can be used to identify the relative risks of streams and estuaries to potential declines due to watershed-scale, landscape conditions across the west. To do so, requires an understanding of quantitative relationships between landscape composition and pattern metrics and parameters of stream and estuary conditions. This paper describes a strategic approach for evaluating the degree to which landscape composition and pattern influence stream and estuary condition, and the development and implementation of a spatially-distributed, landscape analysis approach.     

Uncertainties in Predictions of Surface Water Acidity using the MAGIC Model

In this study, we have used the MAGIC model together with data from the Birkenes catchment in Norway, at which 27 years of data (1974–2000) are available. We calibrated the MAGIC model to the five year observed average chemistry around 1990, and then used the data from the five year period around 1980 to refine the calibration. From 1990, forecasts were run for the different sets of inputs and parameters, and the sets of inputs and parameters were further refined using observations for the period 1996–2000. Through an automatic calibration routine, the model was calibrated a large number of times with different sets of input data to account for the uncertainties in the observed data using a Monte Carlo set-up. The results show that the uncertainty in the model predictions decreases as more observed data from different points in time are used in the model calibration. The results also show that when usingthe time series data in calibration, the distribution of the forecastchanged. The distribution of the predicted Acid Neutralisation Capacity (ANC) in the future is lower for the more refined model calibration. The 10 and 90 percentiles of predicted ANC in 2010 are –3 to 21 μeq L^-1 when only a five-year average is used for calibration, but are –7 to 9 μeq L^-1 when data from the three different time periods are used.     

Market aspects in product life cycle inventory methodology

In planning a product life cycle inventory analysis, the correct choice of methodology often depends on market information. This paper discusses a number of points in the life cycle inventory methodology (choice of product alternatives, geographical system boundaries, technological levels and co-product allocation rules) where the market aspect becomes obvious and where a disregard for these aspects may lead to serious flaws in the results.     

Growth and mortality in young larval herring (Clupea harengus); effects of repetitive changes in food availability

Following yolk resorption, laboratory-reared larval Baltic herring (Clupea harengus L.) were exposed to two sequences of food restriction for 5 d and re-alimentation for 10 d. Comparisons regarding larval growth (standard length and content of water-soluble protein), mortality and content of the sum of trypsin and trypsinogen were made with larvae at a continuous high ration. Larvae exposed to varying prey abundance grew less in length than the control, and during the second high-ration period (Day 22 to 32) growth in length ceased. From the first low-ration period onwards, the content of water-soluble protein in these larvae was lower than that of the control larvae, and the survival rate of the low-high ration group was 59% compared to 77% in the larvae at a continuous high ration. In contrast, the effects of varying food availability were minor on larval content of trypsin and trypsinogen. Results are compared with previous findings in larval Clyde herring, and the effects of larval stock and timing and duration of food restriction on larval growth performance are discussed.     

Sustainable management of shellfish resources in Bandon Bay, Gulf of Thailand

Bandon Bay (Surat Thani Province) is one of the most productive coastal areas in southern Thailand. The Tapi River and 18 channels are the main sources of freshwater, nutrients, organic matter and sediment to the bay and the loading of freshwater and nutrients provide essential support for the production of phytoplankton in the estuarine ecosystem. Bandon Bay is important as natural spawning, nursery and feeding grounds for shellfish such as oysters, blood cockles, green mussels, short-necked clams, mud crabs and shrimps, and the estuary also serves as an excellent area for mariculturing of shellfish. In fact, oysters and blood cockles cultured in Bandon Bay are now being exported worldwide. However, Bandon Bay is also a textbook example of overexploitation of coastal resources in the tropics including all the derived changes in the estuarial ecosystem with severe socio-economic consequences. Hence, there is an urgent need for setting up an integrated management plant for a sustainable use of shellfish resources in Bandon Bay. The present study attempts to integrate water quality simulation results, socio-economic data and information on existing shellfish resource use in the process of proposing a set of sustainable management strategies for shellfish resources in Bandon Bay. These strategies involve: (1) using water quality modeling to monitor ecological and environmental changes in shellfish culture beds and their natural habitats in the process of setting up a master plan for management of waste water discharge into Bandon Bay; (2) zoning of shellfish mariculture in the coastal area in order to solve conflicts between resource users; (3) setting up a clear system for taxation of mariculture where the revenue may be used for (4) setting up and managing mangrove strips as filters of pollution and sediment around Bandon Bay; and finally (5) it is suggested to form a committee with members representing all relevant stakeholders plus the local government in order to work on resolving the existing and potential future conflicts over resource usage in Bandon Bay. This methodology may be seen as an important contribution towards a Bandon Bay sustainable management approach, based on the principles of integrated coastal zone management because it is science-based and takes into consideration the needs and perceptions of people involved in coastal resource extraction.     

Using multi-objective classification to model communities of soil microarthropods

In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.