29 resultados para Neuropsychological Assessment


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Despite of improving levels of hygiene, the incidence of registered food borne disease has been at the same level for many years: there were 40 to 90 epidemics in which 1000-9000 persons contracted food poisoning through food or drinking water in Finland. Until the year 2004 salmonella and campylobacter were the most common bacterial causes of food borne diseases, but in years 2005-2006 Bacillus cereus was the most common. Similar developement has been published i.e. in Germany already in the 1990´s. One reason for this can be Bacillus cereus and its emetic toxin, cereulide. Bacillus cereus is a common environmental bacterium that contaminates raw materials of food. Otherwise than salmonella and campylobacter, Bacillus cereus is a heat resistant bacterium, capable of surviving most cooking procedures due to the production of highly thermo resistant spores. The food involved has usually been heat treated and surviving spores are the source of the food poisoning. The heat treatment induces germination of the spore and the vegetative cells then produce toxins. This doctoral thesis research focuses on developing methods for assessing and eliminating risks to food safety by cereulide producing Bacillus cereus. The biochemistry and physiology of cereulide production was investigated and the results were targeted to offer tools for minimizing toxin risk in food during the production. I developed methods for the extraction and quantitative analysis of cereulide directly from food. A prerequisite for that is knowledge of the chemical and physical properties of the toxin. Because cereulide is practically insoluble in water, I used organic solvents; methanol, ethanol and pentane for the extraction. For extraction of bakery products I used high temperature (100C) and pressure (103.4 bars). Alternaties for effective extraction is to flood the plain food with ethanol, followed by stationary equilibration at room temperature. I used this protocol for extracting cereulide from potato puree and penne. Using this extraction method it is also possible also extract cereulide from liquid food, like milk. These extraction methods are important improvement steps for studying of Bacillus cereus emetic food poisonings. Prior my work, cereulide extraction was done using water. As the result, the yield was poor and variable. To investigate suspected food poisonings, it is important to show actual toxicity of the incriminated food. Many toxins, but not cereulide, inactivate during food processing like heating. The next step is to identify toxin by chemical methods. I developed with my colleague Maria Andesson a rapid assay for the detection of cereulide toxicity, within 5 to 15 minutes. By applying this test it is possible to rapidly detect which food was causing the food poisoning. The chemical identification of cereulide was achieved using mass spectrometry. I used cereulide specific molecular ions, m/z (+/-0.3) 1153.8 (M+H+), 1171.0 (M+NH4+), 1176.0 (M+Na+) and 1191.7 (M+K+) for reliable identification. I investigated foods to find out their amenability to accumulate cereulide. Cereulide was formed high amounts (0.3 to 5.5 microg/g wet wt) when of cereulide producing B. cereus strains were present in beans, rice, rice-pastry and meat-pastry, if stored at non refrigerated temperatures (21-23C). Rice and meat pastries are frequently consumed under conditions where no cooled storage is available e.g. picnics and outdoor events. Bacillus cereus is a ubiquitous spore former and is therefore difficult to eliminate from foods. It is therefore important to know which conditions will affect the formation of cereulide in foods. My research showed that the cereulide content was strongly (10 to 1000 fold differences in toxin content) affected by the growth environment of the bacterium. Storage of foods under nitrogen atmosphere (> 99.5 %) prevented the production of cereulide. But when also carbon dioxide was present, minimizing the oxygen contant (< 1%) did not protect the food from formation of cereulide in preliminary experiments. Also food supplements affected cereulide production at least in the laboratory. Adding free amino acids, leucine and valine, stimulated cereulide production 10 to 20 fold. In peptide bonded form these amino acids are natural constituents in all proteins. Interestingly, adding peptide bonded leucine and valine had no significant effect on cereulide production. Free amino acids leucine and valine are approved food supplements and widely used as flawour modifiers in food technology. My research showed that these food supplements may increase food poisoning risk even though they are not toxic themselves.

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In recent years, concern has arisen over the effects of increasing carbon dioxide (CO2) in the earth's atmosphere due to the burning of fossil fuels. One way to mitigate increase in atmospheric CO2 concentration and climate change is carbon sequestration to forest vegeta-tion through photosynthesis. Comparable regional scale estimates for the carbon balance of forests are therefore needed for scientific and political purposes. The aim of the present dissertation was to improve methods for quantifying and verifying inventory-based carbon pool estimates of the boreal forests in the mineral soils. Ongoing forest inventories provide a data based on statistically sounded sampling for estimating the level of carbon stocks and stock changes, but improved modelling tools and comparison of methods are still needed. In this dissertation, the entire inventory-based large-scale forest carbon stock assessment method was presented together with some separate methods for enhancing and comparing it. The enhancement methods presented here include ways to quantify the biomass of understorey vegetation as well as to estimate the litter production of needles and branches. In addition, the optical remote sensing method illustrated in this dis-sertation can be used to compare with independent data. The forest inventory-based large-scale carbon stock assessment method demonstrated here provided reliable carbon estimates when compared with independent data. Future ac-tivity to improve the accuracy of this method could consist of reducing the uncertainties regarding belowground biomass and litter production as well as the soil compartment. The methods developed will serve the needs for UNFCCC reporting and the reporting under the Kyoto Protocol. This method is principally intended for analysts or planners interested in quantifying carbon over extensive forest areas.

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In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.

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The future use of genetically modified (GM) plants in food, feed and biomass production requires a careful consideration of possible risks related to the unintended spread of trangenes into new habitats. This may occur via introgression of the transgene to conventional genotypes, due to cross-pollination, and via the invasion of GM plants to new habitats. Assessment of possible environmental impacts of GM plants requires estimation of the level of gene flow from a GM population. Furthermore, management measures for reducing gene flow from GM populations are needed in order to prevent possible unwanted effects of transgenes on ecosystems. This work develops modeling tools for estimating gene flow from GM plant populations in boreal environments and for investigating the mechanisms of the gene flow process. To describe spatial dimensions of the gene flow, dispersal models are developed for the local and regional scale spread of pollen grains and seeds, with special emphasis on wind dispersal. This study provides tools for describing cross-pollination between GM and conventional populations and for estimating the levels of transgenic contamination of the conventional crops. For perennial populations, a modeling framework describing the dynamics of plants and genotypes is developed, in order to estimate the gene flow process over a sequence of years. The dispersal of airborne pollen and seeds cannot be easily controlled, and small amounts of these particles are likely to disperse over long distances. Wind dispersal processes are highly stochastic due to variation in atmospheric conditions, so that there may be considerable variation between individual dispersal patterns. This, in turn, is reflected to the large amount of variation in annual levels of cross-pollination between GM and conventional populations. Even though land-use practices have effects on the average levels of cross-pollination between GM and conventional fields, the level of transgenic contamination of a conventional crop remains highly stochastic. The demographic effects of a transgene have impacts on the establishment of trangenic plants amongst conventional genotypes of the same species. If the transgene gives a plant a considerable fitness advantage in comparison to conventional genotypes, the spread of transgenes to conventional population can be strongly increased. In such cases, dominance of the transgene considerably increases gene flow from GM to conventional populations, due to the enhanced fitness of heterozygous hybrids. The fitness of GM plants in conventional populations can be reduced by linking the selectively favoured primary transgene to a disfavoured mitigation transgene. Recombination between these transgenes is a major risk related to this technique, especially because it tends to take place amongst the conventional genotypes and thus promotes the establishment of invasive transgenic plants in conventional populations.

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In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.

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Physicochemical characterization of freshwater samples from Finland, Sweden, the Netherlands, and Spain revealed that water hardness and pH decreased and the quantity and quality of humic substances changed considerably in this geographical series from south to north. Since the ambient water chemistry may affect the availability of chemicals, the total aqueous concentration of a chemical may be insufficient to predict the bioconcentration, subsequent biological response, and thus risk. In addition, organisms could be affected directly by water quality characteristics. In this context the main objective of this thesis was to investigate the bioavailability of selected ecotoxicologically relevant chemicals (cadmium, benzo(a)pyrene, and pyrene) in various European surface waters and to show the importance of certain water chemistry characteristics in interpreting the bioavailability and toxicity results. The bioavailability of cadmium to Daphnia magna was examined in very soft humic lake water. Humic substances as natural ligands decreased the free and bioavailable proportion of cadmium in soft lake water. As a consequence the uptake rate and the acute toxicity decreased compared with the humic-free reference. When the hardness of humic lake water was artificially elevated, the acute toxicity of cadmium decreased, although the proportion of free cadmium increased. The decreased bioavailability of cadmium in hard water was a result of effective competition for uptake by the hardness cations, especially calcium ions. The protective role of humic substances and water hardness against cadmium toxicity was also observed in Lumbriculus variegatus, although D. magna was more sensitive to cadmium. The bioavailability of two polycyclic aromatic hydrocarbons (PAHs), pyrene and benzo(a)pyrene, was studied in European surface waters of varying water chemistry. Humic substances acted as complexing ligands with both PAHs, but the bioavailability of the more lipophilic benzo(a)pyrene to D. magna was affected more by humic substances than that of pyrene. In addition, not only the quantity of humic substances, but also their quality affected the bioavailability of benzo(a)pyrene. Nevertheless, the humic substances played a protective role in the photo-enhanced toxicity of pyrene under UV-B radiation. Water hardness had no effect on pyrene toxicity. Results indicate that the typical physicochemical characteristics of boreal freshwaters should be considered carefully in local and regional risk assessment of chemicals concerning the Fennoscandian region.