Bayesian Conditioning, the Reflection Principle, and Quantum Decoherence

The probabilities a Bayesian agent assigns to a set of events typically change with time, for instance when the agent updates them in the light of new data. In this paper we address the question of how an agent's probabilities at different times are constrained by Dutch-book coherence. We review and attempt to clarify the argument that, although an agent is not forced by coherence to use the usual Bayesian conditioning rule to update his probabilities, coherence does require the agent's probabilities to satisfy van Fraassen's [1984] reflection principle (which entails a related constraint pointed out by Goldstein [1983]). We then exhibit the specialized assumption needed to recover Bayesian conditioning from an analogous reflection-style consideration. Bringing the argument to the context of quantum measurement theory, we show that "quantum decoherence" can be understood in purely personalist terms---quantum decoherence (as supposed in a von Neumann chain) is not a physical process at all, but an application of the reflection principle. From this point of view, the decoherence theory of Zeh, Zurek, and others as a story of quantum measurement has the plot turned exactly backward.Comment: 14 pages, written in memory of Itamar Pitowsk

Genetic association of zinc transporter 8 (ZnT8) autoantibodies in type 1 diabetes cases

Autoantibodies to zinc transporter 8 (ZnT8A) are associated with risk of type 1 diabetes. Apart from the SLC30A8 gene itself, little is known about the genetic basis of ZnT8A. We hypothesise that other loci in addition to SLC30A8 are associated with ZnT8A. The levels of ZnT8A were measured in 2,239 British type 1 diabetic individuals diagnosed before age 17 years, with a median duration of diabetes of 4 years. Cases were tested at over 775,000 loci genome wide (including 53 type 1 diabetes associated regions) for association with positivity for ZnT8A. ZnT8A were also measured in an independent dataset of 855 family members with type 1 diabetes. Only FCRL3 on chromosome 1q23.1 and the HLA class I region were associated with positivity for ZnT8A. rs7522061T > C was the most associated single nucleotide polymorphism (SNP) in the FCRL3 region (p = 1.13 x 10(-16)). The association was confirmed in the family dataset (p a parts per thousand currency signaEuro parts per thousand 9.20 x 10(-4)). rs9258750A > G was the most associated variant in the HLA region (p = 2.06 x 10(-9) and p = 0.0014 in family cases). The presence of ZnT8A was not associated with HLA-DRB1, HLA-DQB1, HLA-A, HLA-B or HLA-C (p > 0.05). Unexpectedly, the two loci associated with the presence of ZnT8A did not alter risk of having type 1 diabetes, and the 53 type 1 diabetes risk loci did not influence positivity for ZnT8A, despite them being disease specific. ZnT8A are not primary pathogenic factors in type 1 diabetes. Nevertheless, ZnT8A testing in combination with other autoantibodies facilitates disease prediction, despite the biomarker not being under the same genetic control as the disease

Meeting the nutritional needs of older patients in the hospital setting

Malnutrition in hospitalised older patients may present in the form of micro-nutrient deficiency such as mineral and/or vitamin deficiencies or macronutrient deficiency represented by protein-energy malnutrition. The prevalence of protein – energy malnutrition among elderly hospitalised patients may vary depending on the population, the setting and the screening tool being used. However, poor nutrition among this population in hospital setting appears to be a global problem and it is often associated with increased mortality, morbidity and longer lengths of hospital stay. In conclusion, the prevalence of malnutrition is high in hospitalised older patients and a number of factors including patient related problems and the hospital environment are responsible for this development. Therefore, strategies for meeting the nutritional needs of older people in hospital should include the use of validated nutritional screening tools to identify those at risk of malnutrition and developing management interventions including the provision of oral nutritional supplements to ameliorate the undernutrition and promote health

Number of siblings and the risk of solid tumours: a nation-wide study

We analysed the effects of number of siblings on the risk of solid tumours using the Swedish Family-Cancer Database, including population-based information on over 11 million individuals and more than 178 000 cancer patients diagnosed between 1958 and 2004. Incidence rate ratios (RRs), estimated by Poisson regression models, were adjusted for age, sex, birth cohort, area of residence and socioeconomic status. Having eight or more siblings vs none increased the risk of stomach cancer (RR=1.83, 95% confidence interval (CI), 1.44–2.34). Anal cancer diagnosed before age 40 showed the strongest association with the total siblings (RR=3.27, 95% CI, 2.04–5.26 for five or more siblings vs none). Endometrial (RR=0.76, 95% CI, 0.70–0.82), testicular (RR=0.71, 95% CI, 0.62–0.82), skin cancer (RR=0.82, 95% CI, 0.69–0.97) and melanoma (RR=0.72, 95% CI, 0.65–0.79) showed strong decreased risks for five or more siblings vs none. Prostate cancer risk for those with five or more older siblings vs none was 1.38 (95% CI, 1.23–1.55). Having five or more younger siblings was most strongly associated with stomach cancer (RR=1.59, 95% CI, 1.29–1.95) and melanoma (RR=0.68, 95% CI, 0.59–0.79). We conclude that sibship characteristics are strong correlates of cancer risk at several sites; plausible interpretations include socioeconomic status

The fallacy of placing confidence in confidence intervals

Interval estimates – estimates of parameters that include an allowance for sampling uncertainty – have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; CIs are thought to be a guide to which parameter values are plausible or reasonable; and the confidence coefficient of the interval (e.g., 95 %) is thought to index the plausibility that the true parameter is included in the interval. We show in a number of examples that CIs do not necessarily have any of these properties, and can lead to unjustified or arbitrary inferences. For this reason, we caution against relying upon confidence interval theory to justify interval estimates, and suggest that other theories of interval estimation should be used instead

MAIT cells launch a rapid, robust and distinct hyperinflammatory response to bacterial superantigens and quickly acquire an anergic phenotype that impedes their cognate antimicrobial function: Defining a novel mechanism of superantigen-induced immunopathology and immunosuppression

Superantigens (SAgs) are potent exotoxins secreted by Staphylococcus aureus and Streptococcus pyogenes. They target a large fraction of T cell pools to set in motion a "cytokine storm" with severe and sometimes life-threatening consequences typically encountered in toxic shock syndrome (TSS). Given the rapidity with which TSS develops, designing timely and truly targeted therapies for this syndrome requires identification of key mediators of the cytokine storm's initial wave. Equally important, early host responses to SAgs can be accompanied or followed by a state of immunosuppression, which in turn jeopardizes the host's ability to combat and clear infections. Unlike in mouse models, the mechanisms underlying SAg-associated immunosuppression in humans are ill-defined. In this work, we have identified a population of innate-like T cells, called mucosa-associated invariant T (MAIT) cells, as the most powerful source of pro-inflammatory cytokines after exposure to SAgs. We have utilized primary human peripheral blood and hepatic mononuclear cells, mouse MAIT hybridoma lines, HLA-DR4-transgenic mice, MAIThighHLA-DR4+ bone marrow chimeras, and humanized NOD-scid IL-2Rγnull mice to demonstrate for the first time that: i) mouse and human MAIT cells are hyperresponsive to SAgs, typified by staphylococcal enterotoxin B (SEB); ii) the human MAIT cell response to SEB is rapid and far greater in magnitude than that launched by unfractionated conventional T, invariant natural killer T (iNKT) or γδ T cells, and is characterized by production of interferon (IFN)-γ, tumor necrosis factor (TNF)-α and interleukin (IL)-2, but not IL-17A; iii) high-affinity MHC class II interaction with SAgs, but not MHC-related protein 1 (MR1) participation, is required for MAIT cell activation; iv) MAIT cell responses to SEB can occur in a T cell receptor (TCR) Vβ-specific manner but are largely contributed by IL-12 and IL-18; v) as MAIT cells are primed by SAgs, they also begin to develop a molecular signature consistent with exhaustion and failure to participate in antimicrobial defense. Accordingly, they upregulate lymphocyte-activation gene 3 (LAG-3), T cell immunoglobulin and mucin-3 (TIM-3), and/or programmed cell death-1 (PD-1), and acquire an anergic phenotype that interferes with their cognate function against Klebsiella pneumoniae and Escherichia coli; vi) MAIT cell hyperactivation and anergy co-utilize a signaling pathway that is governed by p38 and MEK1/2. Collectively, our findings demonstrate a pathogenic, rather than protective, role for MAIT cells during infection. Furthermore, we propose a novel mechanism of SAg-associated immunosuppression in humans. MAIT cells may therefore provide an attractive therapeutic target for the management of both early and late phases of severe SAg-mediated illnesses

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.</p