Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Networks, Uncertainty, Applications and a Crusade for Optimality

In this thesis we address a collection of Network Design problems which are strongly motivated by applications from Telecommunications, Logistics and Bioinformatics. In most cases we justify the need of taking into account uncertainty in some of the problem parameters, and different Robust optimization models are used to hedge against it. Mixed integer linear programming formulations along with sophisticated algorithmic frameworks are designed, implemented and rigorously assessed for the majority of the studied problems. The obtained results yield the following observations: (i) relevant real problems can be effectively represented as (discrete) optimization problems within the framework of network design; (ii) uncertainty can be appropriately incorporated into the decision process if a suitable robust optimization model is considered; (iii) optimal, or nearly optimal, solutions can be obtained for large instances if a tailored algorithm, that exploits the structure of the problem, is designed; (iv) a systematic and rigorous experimental analysis allows to understand both, the characteristics of the obtained (robust) solutions and the behavior of the proposed algorithm.

[Ethics, empiricism and uncertainty]

Accidents can lead to difficult boundary situations. Such situations often take place in the emergency units. The medical team thus often and inevitably faces professional uncertainty in their decision-making. It is essential to communicate these uncertainties within the medical team, instead of downplaying or overriding existential hurdles in decision-making. Acknowledging uncertainties might lead to alert and prudent decisions. Thus uncertainty can have ethical value in treatment or withdrawal of treatment. It does not need to be covered in evidence-based arguments, especially as some singular situations of individual tragedies cannot be grasped in terms of evidence-based medicine.

Dopaminergic treatment in idiopathic restless legs syndrome: effects on subjective sleepiness

To assess frequency and characteristics of excessive daytime sleepiness (EDS) in restless legs syndrome (RLS) and the evolution of EDS under different RLS therapies.

Subjective stress in female elite athletes and non-athletes: Evidence from cortisol analyses

Stress response can be considered a consequence of psychological or physiological threats to the human organism. Elevated cortisol secretion represents a biological indicator of subjective stress. The extent of subjectively experienced stress depends on individual coping strategies or self-regulation skills. Because of their experience with competitive pressure, athletes might show less pronounced biological stress responses during stressful events compared to non-athletes. In the present study, the short version of the Berlin Intelligence Structure Test, a paper-pencil intelligence test, was used as an experimental stressor. Cortisol responses of 26 female Swiss elite athletes and 26 female non-athlete controls were compared. Salivary free cortisol responses were measured 15 minutes prior to, as well as immediately before and after psychometric testing. In both groups, a significant effect of time was found: High cortisol levels prior to testing decreased significantly during the testing session. Furthermore, athletes exhibited reliably lower cortisol levels than non-athlete controls. No significant interaction effects could be observed. The overall pattern of results supports the idea that elite athletes show a less pronounced cortisol-related stress response due to more efficient coping strategies.

Differential 3-year effects of first- versus second-generation antipsychotics on subjective well-being in schizophrenia using marginal structural models

This study examined the differential effects of first- (FGAs) versus second-generation antipsychotics (SGAs) on subjective well-being in patients with schizophrenia.

Human Preferences for Symmetry: Subjective Experience, Cognitive Conflict and Cortical Brain Activity

This study examines the links between human perceptions, cognitive biases and neural processing of symmetrical stimuli. While preferences for symmetry have largely been examined in the context of disorders such as obsessive-compulsive disorder and autism spectrum disorders, we examine various these phenomena in non-clinical subjects and suggest that such preferences are distributed throughout the typical population as part of our cognitive and neural architecture. In Experiment 1, 82 young adults reported on the frequency of their obsessive-compulsive spectrum behaviors. Subjects also performed an emotional Stroop or variant of an Implicit Association Task (the OC-CIT) developed to assess cognitive biases for symmetry. Data not only reveal that subjects evidence a cognitive conflict when asked to match images of positive affect with asymmetrical stimuli, and disgust with symmetry, but also that their slowed reaction times when asked to do so were predicted by reports of OC behavior, particularly checking behavior. In Experiment 2, 26 participants were administered an oddball Event-Related Potential task specifically designed to assess sensitivity to symmetry as well as the OC-CIT. These data revealed that reaction times on the OC-CIT were strongly predicted by frontal electrode sites indicating faster processing of an asymmetrical stimulus (unparallel lines) relative to a symmetrical stimulus (parallel lines). The results point to an overall cognitive bias linking disgust with asymmetry and suggest that such cognitive biases are reflected in neural responses to symmetrical/asymmetrical stimuli.

Subjective cognitive complaints one year after ceasing adjuvant endocrine treatment for early-stage breast cancer

Background:In the BIG 1-98 trial objective cognitive function improved in postmenopausal women 1 year after cessation of adjuvant endocrine therapy for breast cancer. This report evaluates changes in subjective cognitive function (SCF).Methods:One hundred postmenopausal women, randomised to receive 5 years of adjuvant tamoxifen, letrozole, or a sequence of the two, completed self-reported measures on SCF, psychological distress, fatigue, and quality of life during the fifth year of trial treatment (year 5) and 1 year after treatment completion (year 6). Changes between years 5 and 6 were evaluated using the Wilcoxon signed-rank test. Subjective cognitive function and its correlates were explored.Results:Subjective cognitive function and the other patient-reported outcomes did not change significantly after cessation of endocrine therapy with the exception of improvement for hot flushes (P=0.0005). No difference in changes was found between women taking tamoxifen or letrozole. Subjective cognitive function was the only psychosocial outcome with a substantial correlation between year 5 and 6 (Spearman's R=0.80). Correlations between SCF and the other patient-reported outcomes were generally low.Conclusion:Improved objective cognitive function but not SCF occur following cessation of adjuvant endocrine therapy in the BIG 1-98 trial. The substantial correlation of SCF scores over time may represent a stable attribute.British Journal of Cancer advance online publication, 24 April 2012; doi:10.1038/bjc.2012.156 www.bjcancer.com.