Data-driven efficient score tests for deconvolution problems

We consider testing statistical hypotheses about densities of signals in deconvolution models. A new approach to this problem is proposed. We constructed score tests for the deconvolution with the known noise density and efficient score tests for the case of unknown density. The tests are incorporated with model selection rules to choose reasonable model dimensions automatically by the data. Consistency of the tests is proved

Short-Term Exercise Training Does Not Stimulate Skeletal Muscle ATP Synthesis in Relatives of Humans With Type 2 Diabetes

OBJECTIVE-We tested the hypothesis that short-term exercise training improves hereditary insulin resistance by stimulating ATP synthesis and investigated associations With gene polymorphisms. RESEARCH DESIGN AND METHODS-We studied 24 nono-bese first-degree relatives of type 2 diabetic patients and 12 control subjects at rest, and 48 h after three bouts of exercise. In addition to measurements of oxygen uptake and insulin sensitivity (oral glucose tolerance test), ectopic lipids and mitochondrial ATP synthesis were assessed using H-1 and P-31 magnetic resonance spectroscopy, respectively. They were genotyped for polymorphisms in genes regulating mitochondrial function, PPARGC1A (rs8192678) and NDUFB6 (rs540467). RESULTS-Relatives had slightly lower (P = 0.012) insulin sensitivity than control subjects. In control subjects, ATP synthase flux rose by 18% (P = 0.0001), being 23% higher (P = 0.002) than that in relatives after exercise training. Relatives responding to exercise training with increased ATP synthesis (+19%, P = 0.009) showed improved insulin sensitivity (P = 0.009) compared with those whose insulin sensitivity did not improve. A polymorphism in the NDUFB6 gene from respiratory chain complex I related to ATP synthesis (P = 0.02) and insulin Sensitivity response to exercise training (P = 0.05). ATP synthase flux correlated with O-2 uptake and insulin sensitivity. CONCLUSIONS-The ability of short-term exercise to stimulate ATP production distinguished individuals with improved insulin sensitivity from those whose insulin sensitivity did not improve. lit addition, the NDUFB6 gene polymorphism appeared to modulate this adaptation. This finding suggests that genes involved in mitochondrial function contribute to the response of ATP synthesis to exercise training. Diabetes 58:1333-1341, 200

Muscle Mitochondrial ATP Synthesis and Glucose Transport/Phosphorylation in Type 2 Diabetes

BACKGROUND: Muscular insulin resistance is frequently characterized by blunted increases in glucose-6-phosphate (G-6-P) reflecting impaired glucose transport/phosphorylation. These abnormalities likely relate to excessive intramyocellular lipids and mitochondrial dysfunction. We hypothesized that alterations in insulin action and mitochondrial function should be present even in nonobese patients with well-controlled type 2 diabetes mellitus (T2DM). METHODS AND FINDINGS: We measured G-6-P, ATP synthetic flux (i.e., synthesis) and lipid contents of skeletal muscle with (31)P/(1)H magnetic resonance spectroscopy in ten patients with T2DM and in two control groups: ten sex-, age-, and body mass-matched elderly people; and 11 younger healthy individuals. Although insulin sensitivity was lower in patients with T2DM, muscle lipid contents were comparable and hyperinsulinemia increased G-6-P by 50% (95% confidence interval [CI] 39%–99%) in all groups. Patients with diabetes had 27% lower fasting ATP synthetic flux compared to younger controls (p = 0.031). Insulin stimulation increased ATP synthetic flux only in controls (younger: 26%, 95% CI 13%–42%; older: 11%, 95% CI 2%–25%), but failed to increase even during hyperglycemic hyperinsulinemia in patients with T2DM. Fasting free fatty acids and waist-to-hip ratios explained 44% of basal ATP synthetic flux. Insulin sensitivity explained 30% of insulin-stimulated ATP synthetic flux. CONCLUSIONS: Patients with well-controlled T2DM feature slightly lower flux through muscle ATP synthesis, which occurs independently of glucose transport /phosphorylation and lipid deposition but is determined by lipid availability and insulin sensitivity. Furthermore, the reduction in insulin-stimulated glucose disposal despite normal glucose transport/phosphorylation suggests further abnormalities mainly in glycogen synthesis in these patients

Decision-Making and Depressive Symptomatology

Difficulty making decisions is a core symptom of depressive illness, but the nature of these difficulties has not been well characterized. The two studies presented herein use the same hypothetical scenarios that call for a decision. In Study 1, participants were asked to make and explain their decisions in a free-response format, as well as to describe their prior experiences with similar situations. The results suggest that those with more depressive symptoms make decisions that are less likely to further their interests. We also identified several interesting associations between features of decision-making and the presence of depressive symptoms. In Study 2, participants were guided through their decisions with simple decision tools to investigate whether the association between depressive symptoms and poor decisions is better accounted for by failure to use of good decision-making strategies, or by other factors, such as differences in priorities or goals. With this minimal intervention the quality of decisions no longer declined significantly as a function of depressive symptom severity. Moreover, few associations between depressive symptom severity and decision-related goals and priorities were evident, suggesting that the previously-exposed difficulties of depressed individuals with decision-making were largely the result of their failure to use effective decision-making techniques

Making sense of perceptions of risk of diseases and vaccinations: a qualitative study combining models of health beliefs, decision-making and risk perception

<p>Abstract</p> <p>Background</p> <p>Maintaining high levels of childhood vaccinations is important for public health. Success requires better understanding of parents' perceptions of diseases and consequent decisions about vaccinations, however few studies have considered this from the theoretical perspectives of risk perception and decision-making under uncertainty. The aim of this study was to examine the utility of subjective risk perception and decision-making theories to provide a better understanding of the differences between immunisers' and non-immunisers' health beliefs and behaviours.</p> <p>Methods</p> <p>In a qualitative study we conducted semi-structured in-depth interviews with 45 Australian parents exploring their experiences and perceptions of disease severity and susceptibility. Using scenarios about 'a new strain of flu' we explored how risk information was interpreted.</p> <p>Results</p> <p>We found that concepts of dread, unfamiliarity, and uncontrollability from the subjective perception of risk and ambiguity, optimistic control and omission bias from explanatory theories of decision-making under uncertainty were useful in understanding why immunisers, incomplete immunisers and non-immunisers interpreted severity and susceptibility to diseases and vaccine risk differently. Immunisers dreaded unfamiliar diseases whilst non-immunisers dreaded unknown, long term side effects of vaccines. Participants believed that the risks of diseases and complications from diseases are not equally spread throughout the community, therefore, when listening to reports of epidemics, it is not the number of people who are affected but the familiarity or unfamiliarity of the disease and the characteristics of those who have had the disease that prompts them to take preventive action. Almost all believed they themselves would not be at serious risk of the 'new strain of flu' but were less willing to take risks with their children's health.</p> <p>Conclusion</p> <p>This study has found that health messages about the risks of disease which are communicated as though there is equality of risk in the population may be unproductive as these messages are perceived as unbelievable or irrelevant. The findings from this study have implications beyond the issue of childhood vaccinations as we grapple with communicating risks of new epidemics, and indeed may usefully contribute to the current debate especially in the UK of how these theories of risk and decision-making can be used to 'nudge' other health behaviours.</p

Numerical Comparison of Cusum and Shiryaev-Roberts Procedures for Detecting Changes in Distributions

The CUSUM procedure is known to be optimal for detecting a change in distribution under a minimax scenario, whereas the Shiryaev-Roberts procedure is optimal for detecting a change that occurs at a distant time horizon. As a simpler alternative to the conventional Monte Carlo approach, we propose a numerical method for the systematic comparison of the two detection schemes in both settings, i.e., minimax and for detecting changes that occur in the distant future. Our goal is accomplished by deriving a set of exact integral equations for the performance metrics, which are then solved numerically. We present detailed numerical results for the problem of detecting a change in the mean of a Gaussian sequence, which show that the difference between the two procedures is significant only when detecting small changes.Comment: 21 pages, 8 figures, to appear in Communications in Statistics - Theory and Method

When Flexibility Is Stable: Implicit Long-Term Shaping of Olfactory Preferences

Preferences are traditionally assumed to be stable. However, empirical evidence such as preference modulation following choices calls this assumption into question. The evolution of such postchoice preference over long time spans, even when choices have been explicitly forgotten, has so far not been studied. In two experiments, we investigated this question by using a variant of the free choice paradigm: In a first session, participants evaluated the pleasantness of a number of odors. We then formed pairs of similarly rated odors, and asked participants to choose their favorite, for each pair. Participants were then presented with all odors again, and asked for another pleasantness rating. In a second session 1 week later, a third pleasantness rating was obtained, and participants were again asked to choose between the same options. Results suggested postchoice preference modulation immediately and 1 week after choice for both chosen and rejected options, even when choices were not explicitly remembered. A third experiment, using another paradigm, confirmed that choice can have a modulatory impact on preferences, and that this modulation can be long-lasting. Taken together, these findings suggest that although preferences appear to be flexible because they are modulated by choices, this modulation also appears to be stable over time and even without explicit recollection of the choice. These results bring a new argument to the idea that postchoice preference modulation could rely on implicit mechanisms, and are consistent with the recent proposal that cognitive dissonance reduction could to some extent be implicit

Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale