Browsing by Subject "Bayesian"

Sort by: Order: Results:

Now showing items 1-6 of 6
  • Raubenheimer, Marie-Claire (Helsingin yliopisto, 2020)
    Oil spillages represent a serious environmental hazard for flora and fauna of marine and coastal ecosystems. Though marine oil spills have decreased since the 1970s, the increasing production of petroleum goods remains a potential source of pollution due to its use and transportation. When aquatic organisms, including fish, are exposed to toxic oil compounds, this can cause sublethal morphological changes and increase mortality. In this context, herring have been frequently studied, and results suggest that particularly herrings eggs and larvae are highly susceptible to oil toxicity. In this thesis, a Bayesian meta-analysis was conducted to investigate the effects of crude and fuel oil on the mortality of herring eggs from the genus Clupea. Observations from laboratory studies, collected during a literature review, served as input for the statistical analysis. To this end, Bayesian inference modeling was applied to generate posterior probability distributions for additional mortality caused by exposure to oil mixtures. Also, oil concentration, oil type, exposure time, and temperature were analyzed to study possible correlations with mortality impacts. The results of this study suggest that acute mortality of exposed herring eggs is similar to mortality observed for individuals exposed to only small concentrations or none at all. Of all evaluated oil types, medium grade crude oil caused the most significant change in instantaneous mortality with increasing oil concentration. Generally, distinct oil types had a greater influence on mortality outcomes than temperatures at the given concentrations. For the lowest temperatures, some correlations for increased mortality were found. Overall, the unexplained variability between the reviewed studies has a relatively small influence on mortality outcomes. In conclusion, the mortality of exposed herrings eggs is most likely delayed due to sublethal effects, rather than immediate, at the modeled concentrations. Altogether, uncertainty amongst the posterior probability distributions is high, indicating a wide possibility range for the monitored parameters' actual values. The reasons for elevated uncertainty likely stem from diverse experimental setups, biological differences between tested species, relatively small sample sizes, and model-related issues. Thus, future research could consider additional variables, information from observational studies and other fish species to reduce uncertainty in mortality outcomes.
  • Vikkula, Sami (Helsingin yliopisto, 2021)
    Oil spills in aquatic environments are devastating disasters with both biological and economic impacts. Fish populations are among the many subjects of these impacts. In literature, there are numerous assessments of oil spill impacts on fish populations. From all applied research methods, the focus of this thesis is on Bayesian methods. In prior research, several Bayesian models have been developed for assessing oil spill impacts on fish populations. These models, however, have focused on the assessment of impacts from past spills. They have not been used for predicting impacts of possible future oil spills. Furthermore, the models have not utilized data from laboratory studies. Some examples can be found of models assessing economic impacts of oil spills on fish populations however, none of them assess the economic impacts that follow from decreases in biomass. The aim of this thesis is to develop a Bayesian bioeconomic prediction model, which would be able to predict oil spill impacts on Baltic Sea main basin herring population, and the consequential economic impacts on fishermen. The idea is to predict the impacts of several hypothetical oil spill scenarios. As a result of this thesis, a bioeconomic prediction model was developed, which can predict both biological and economic impacts of oil spills on Baltic Sea main basin herring through additional oil induced mortality of herring eggs. The model can be applied to other fish populations in other regions as well. The model utilizes laboratory studies for assessing population level impacts. The model can be used for both assessing risks of the impacts of possible future oil spills, and for decision analysis after a spill has already occurred. Furthermore, the model can be used for assessing unknown aspects of past oil spills. The economic predictions can be used, for example, to estimate the compensations that could possibly be paid to fishermen. In the future, the prediction model should be developed further, especially regarding its stock-recruitment relationship assumptions. In addition, the model’s assumptions regarding the calculation of oil induced additional mortality and the economic impacts, should be expanded.
  • Hewetson, Michael; Venner, Monica; Volquardsen, Jan; Sykes, Ben William; Hallowell, Gayle Davina; Vervuert, Ingrid; Fosgate, Geoffrey Theodore; Tulamo, Riitta-Mari (2018)
    Background: Equine gastric ulcer syndrome is an important cause of morbidity in weanling foals. Many foals are asymptomatic, and the development of an inexpensive screening test to ensure an early diagnosis is desirable. The objective of this study was to determine the diagnostic accuracy of blood sucrose for diagnosis of EGUS in weanling foals. Results: 45 foals were studied 7 days before and 14 days after weaning. The diagnostic accuracy of blood sucrose for diagnosis of gastric lesions (GL); glandular lesions (GDL); squamous lesions (SQL) and clinically significant gastric lesions (CSL) at 45 and 90 min after administration of 1 g/kg of sucrose via nasogastric intubation was assessed using ROC curves and calculating the AUC. For each lesion type, sucrose concentration in blood was compared to gastroscopy; and sensitivities (Se) and specificities (Sp) were calculated across a range of sucrose concentrations. Cut- off values were selected manually to optimize Se. Because of concerns over the validity of the gold standard, additional Se, Sp, and lesion prevalence data were subsequently estimated and compared using Bayesian latent class analysis. Using the frequentist approach, the prevalence of GL; GDL; SQL and CSL before weaning was 21; 9; 7 and 8% respectively; and increased to 98; 59; 97 and 82% respectively after weaning. At the selected cut- off, Se ranged from 84 to 95% and Sp ranged from 47 to 71%, depending upon the lesion type and time of sampling. In comparison, estimates of Se and Sp were consistently higher when using a Bayesian approach, with Se ranging from 81 to 97%; and Sp ranging from 77 to 97%, depending upon the lesion type and time of sampling. Conclusions: Blood sucrose is a sensitive test for detecting EGUS in weanling foals. Due to its poor specificity, it is not expected that the sucrose blood test will replace gastroscopy, however it may represent a clinically useful screening test to identify foals that may benefit from gastroscopy. Bayesian latent class analysis represents an alternative method to evaluate the diagnostic accuracy of the blood sucrose test in an attempt to avoid bias associated with the assumption that gastroscopy is a perfect test.
  • Hewetson, Michael; Venner, Monica; Volquardsen, Jan; Sykes, Ben W; Hallowell, Gayle D; Vervuert, Ingrid; Fosgate, Geoffrey T; Tulamo, Riitta-Mari (BioMed Central, 2018)
    Abstract Background Equine gastric ulcer syndrome is an important cause of morbidity in weanling foals. Many foals are asymptomatic, and the development of an inexpensive screening test to ensure an early diagnosis is desirable. The objective of this study was to determine the diagnostic accuracy of blood sucrose for diagnosis of EGUS in weanling foals. Results 45 foals were studied 7 days before and 14 days after weaning. The diagnostic accuracy of blood sucrose for diagnosis of gastric lesions (GL); glandular lesions (GDL); squamous lesions (SQL) and clinically significant gastric lesions (CSL) at 45 and 90 min after administration of 1 g/kg of sucrose via nasogastric intubation was assessed using ROC curves and calculating the AUC. For each lesion type, sucrose concentration in blood was compared to gastroscopy; and sensitivities (Se) and specificities (Sp) were calculated across a range of sucrose concentrations. Cut-off values were selected manually to optimize Se. Because of concerns over the validity of the gold standard, additional Se, Sp, and lesion prevalence data were subsequently estimated and compared using Bayesian latent class analysis. Using the frequentist approach, the prevalence of GL; GDL; SQL and CSL before weaning was 21; 9; 7 and 8% respectively; and increased to 98; 59; 97 and 82% respectively after weaning. At the selected cut-off, Se ranged from 84 to 95% and Sp ranged from 47 to 71%, depending upon the lesion type and time of sampling. In comparison, estimates of Se and Sp were consistently higher when using a Bayesian approach, with Se ranging from 81 to 97%; and Sp ranging from 77 to 97%, depending upon the lesion type and time of sampling. Conclusions Blood sucrose is a sensitive test for detecting EGUS in weanling foals. Due to its poor specificity, it is not expected that the sucrose blood test will replace gastroscopy, however it may represent a clinically useful screening test to identify foals that may benefit from gastroscopy. Bayesian latent class analysis represents an alternative method to evaluate the diagnostic accuracy of the blood sucrose test in an attempt to avoid bias associated with the assumption that gastroscopy is a perfect test.
  • Hopker, James; Griffin, Jim; Brookhouse, James; Peters, John; Schumacher, Yorck Olaf; Iljukov, Sergei (2020)
    The efficient use of testing resources is crucial in the fight against doping in sports. The athlete biological passport relies on the need to identify the right athletes to test, and the right time to test them. Here we present an approach to longitudinal tracking of athlete performance to provide an additional, more intelligence-led approach to improve targeted antidoping testing. The performance results of athletes (male shot putters, male 100 m sprinters, and female 800 m runners) were obtained from a performance results database. Standardized performances, which adjust for average career performance, were calculated to determine the volatility in performance over an athlete's career. We then used a Bayesian spline model to statistically analyse changes within an athlete's standardized performance over the course of a career both for athletes who were presumed "clean" (not doped), and those previously convicted of doping offences. We used the model to investigate changes in the slope of each athlete's career performance trajectory and whether these changes can be linked to doping status. The model was able to identify differences in the standardized performance of clean and doped athletes, with the sign of the change able to provide some discrimination. Consistent patterns of standardized performance profile are seen across shot put, 100 m and 800 m for both the clean and doped athletes we investigated. This study demonstrates the potential for modeling athlete performance data to distinguish between the career trajectories of clean and doped athletes, and to enable the risk stratification of athletes on their risk of doping.
  • Zhou, Yanli; Acerbi, Luigi; Ma, Wei Ji (2020)
    Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account-in a very specific way-in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing "probabilistic computation" in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception. Author summary Our percept of the world is governed not only by the sensory information we have access to, but also by the way we interpret this information. When presented with a visual scene, our visual system undergoes a process of grouping visual elements together to form coherent entities so that we can interpret the scene more readily and meaningfully. For example, when looking at a pile of autumn leaves, one can still perceive and identify a whole leaf even when it is partially covered by another leaf. While Gestalt psychologists have long described perceptual organization with a set of qualitative laws, recent studies offered a statistically-optimal-Bayesian, in statistical jargon-interpretation of this process, whereby the observer chooses the scene configuration with the highest probability given the available sensory inputs. However, these studies drew their conclusions without considering a key actor in this kind of statistically-optimal computations, that is the role of sensory uncertainty. One can easily imagine that our decision on whether two contours belong to the same leaf or different leaves is likely going to change when we move from viewing the pile of leaves at a great distance (high sensory uncertainty), to viewing very closely (low sensory uncertainty). Our study examines whether and how people incorporate uncertainty into contour integration, an elementary form of perceptual organization, by varying sensory uncertainty from trial to trial in a simple contour integration task. We found that people indeed take into account sensory uncertainty, however in a way that subtly deviates from optimal behavior.