Bayesian model search and multilevel inference for SNP association studies

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally ``validated'' in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS322 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Partisan Politics of New Social Risks in Advanced Postindustrial Democracies: Social Protection for Labor Market Outsiders

Advanced postindustrialization generates numerous challenges for the European social model. Central among these challenges is declining income, unstable employment, and inadequate training of semi- and unskilled workers. In this chapter, I assess the partisan basis of support for social policies that address the needs of these marginalized workers. I specifically consider the impacts of postindustrial cleavages among core constituencies of social democratic parties on the capacity of these parties to pursue inclusive social policies. I argue – and find support for in empirical analyses – that encompassing labor organization is the most important factor in strengthening the ability of left parties to build successful coalitions in support of outsider-friendly policies. I go beyond existing work on the topic by considering the full array of postindustrial cleavages facing left parties, by more fully elaborating why encompassing labor organization is crucial, and by considering a more complete set of measures of outsider policies than extant work. I compare my arguments and findings to important new work that stresses coalition building and partisan politics but minimizes the role of class organization

Development and application of a novel Peptide Nucleic Acid probe for the specific detection of Cronobacter (Enterobacter sakazakii) in powdered infant formula

Cronobacter spp. are causative agents of meningitis, septicemia and necrotizing enterocolitis in neonates and immunocompromised infants. Recently, contaminated powdered infant formula (PIF) has been reported as a source of these infections. In order to minimize the risk of infection, the development of a rapid, sensitive and specific method for the early detection of this bacterium in infant formula is of the utmost importance. Fluorescence in situ hybridization (FISH), a technique that allows direct visualization of whole cells, has been combined with specific peptide nucleic acid (PNA) probes, a new synthetic molecule with a better hybridization performance than DNA probes. In this work, a new FISH method for the detection of Cronobacter spp. using a novel PNA probe is reported. This PNA-FISH method was then adapted for the detection of this bacterium in PIF. The PNA-FISH procedure using the Cronobacter probe proved to be a reliable method for the detection of this pathogen in PIF samples and an alternative to existing molecular methods. It presented high specificity and sensitivity, detected less than 1 CFU per 10g of Cronobacter in infant formula and provided detection in less than 12 hours. Direct visualization of bacterial cells was possible and the method was simple and easy to use, without any special equipment apart from an epifluorescence microscope. The samples can be also analysed by flow cytometry

Evidence for a direct band gap in the topological insulator Bi2Se3 from theory and experiment

Using angle-resolved photoelectron spectroscopy and ab-initio GW calculations, we unambiguously show that the widely investigated three-dimensional topological insulator Bi2Se3 has a direct band gap at the Gamma point. Experimentally, this is shown by a three-dimensional band mapping in large fractions of the Brillouin zone. Theoretically, we demonstrate that the valence band maximum is located at the Brillouin center only if many-body effects are included in the calculation. Otherwise, it is found in a high-symmetry mirror plane away from the zone center.Comment: 8 pages, 4 figure

Acute unstable depressive syndrome (AUDS) is associated more frequently with epilepsy than major depression

<p>Abstract</p> <p>Background</p> <p>Depressive disorders are frequent in epilepsy and associated with reduced seizure control. Almost 50% of interictal depressive disorders have to be classified as atypical depressions according to DSM-4 criteria. Research has mainly focused on depressive symptoms in defined populations with epilepsy (e.g., patients admitted to tertiary epilepsy centers). We have chosen the opposite approach. We hypothesized that it is possible to define by clinical means a subgroup of psychiatric patients with higher than expected prevalence of epilepsy and seizures. We hypothesized further that these patients present with an Acute Unstable Depressive Syndrome (AUDS) that does not meet DSM-IV criteria of a Major Depressive Episode (MDE). In a previous publication we have documented that AUDS patients indeed have more often a history of epileptic seizures and abnormal EEG recordings than MDE patients (Vaaler et al. 2009). This study aimed to further classify the differences of depressive symptoms at admittance and follow-up of patients with AUDS and MDE.</p> <p>Methods</p> <p>16 AUDS patients and 16 age- and sex-matched MDE patients were assessed using the Symptomatic Organic Mental Disorder Assessment Scale (SOMAS), the Montgomery and Åsberg Depression Rating Scale (MADRS), and the Mini-Mental State Test (MMST), at day 2, day 4-6, day 14-16 and 3 months after admittance to a psychiatric emergency unit. Life events were assessed with The Social Readjustment Rating Scale (SRRS) and The Life Experience Survey (LES). We also screened for medication serum levels and illicit drug metabolites in urine.</p> <p>Results</p> <p>AUDS patients had significantly higher SOMAS scores (average score at admission 6.6 ± 0.8), reflecting increased symptom fluctuation and motor agitation, and decreased insight and concern compared to MDE patients (2.9 ± 0.7; p < 0.001). Degree of mood depression, cognition, life events, drug abuse and medication did not differ between the two groups.</p> <p>Conclusions</p> <p>AUDS patients present with rapidly fluctuating mood symptoms, motor agitation and relative lack of insight and concern. Seizures, epilepsy and EEG abnormalities are overrepresented in AUDS patients compared to MDE patients. We suggest that the study of AUDS patients may offer a new approach to better understanding epilepsy and its association with depressive disorders.</p> <p>Trial registration</p> <p>NCT00201474</p

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data