Measurement of the Bs0→J/ψKS0B_s^0\to J/\psi K_S^0 branching fraction

The $B_s^0\to J/\psi K_S^0$ branching fraction is measured in a data sample corresponding to 0.41$fb^{-1}$ of integrated luminosity collected with the LHCb detector at the LHC. This channel is sensitive to the penguin contributions affecting the sin2$\beta$ measurement from $B^0\to J/\psi K_S^0$ The time-integrated branching fraction is measured to be $BF(B_s^0\to J/\psi K_S^0)=(1.83\pm0.28)\times10^{-5}$. This is the most precise measurement to date

Measurement of the CP-violating phase \phi s in Bs->J/\psi\pi+\pi- decays

Measurement of the mixing-induced CP-violating phase phi_s in Bs decays is of prime importance in probing new physics. Here 7421 +/- 105 signal events from the dominantly CP-odd final state J/\psi pi+ pi- are selected in 1/fb of pp collision data collected at sqrt{s} = 7 TeV with the LHCb detector. A time-dependent fit to the data yields a value of phi_s=-0.019^{+0.173+0.004}_{-0.174-0.003} rad, consistent with the Standard Model expectation. No evidence of direct CP violation is found.Comment: 15 pages, 10 figures; minor revisions on May 23, 201

Measurement of the ratio of branching fractions BR(B0 -> K*0 gamma)/BR(Bs0 -> phi gamma) and the direct CP asymmetry in B0 -> K*0 gamma

The ratio of branching fractions of the radiative B decays B0 -> K*0 gamma and Bs0 phi gamma has been measured using an integrated luminosity of 1.0 fb-1 of pp collision data collected by the LHCb experiment at a centre-of-mass energy of sqrt(s)=7 TeV. The value obtained is BR(B0 -> K*0 gamma)/BR(Bs0 -> phi gamma) = 1.23 +/- 0.06(stat.) +/- 0.04(syst.) +/- 0.10(fs/fd), where the first uncertainty is statistical, the second is the experimental systematic uncertainty and the third is associated with the ratio of fragmentation fractions fs/fd. Using the world average value for BR(B0 -> K*0 gamma), the branching fraction BR(Bs0 -> phi gamma) is measured to be (3.5 +/- 0.4) x 10^{-5}. The direct CP asymmetry in B0 -> K*0 gamma decays has also been measured with the same data and found to be A(CP)(B0 -> K*0 gamma) = (0.8 +/- 1.7(stat.) +/- 0.9(syst.))%. Both measurements are the most precise to date and are in agreement with the previous experimental results and theoretical expectations.Comment: 21 pages, 3 figues, 4 table

First observation of the decay Bˉs0→D0K∗0\bar{B}^0_s \to D^0 K^{*0} and a measurement of the ratio of branching fractions B(Bˉs0→D0K∗0)B(Bˉ0→D0ρ0)\frac{{\cal B}(\bar{B}^0_s \to D^0 K^{*0})}{{\cal B}(\bar{B}^0 \to D^0 \rho^0)}

The first observation of the decay $\bar{B}^0_s \to D^0 K^{*0}$ using $pp$ data collected by the LHCb detector at a centre-of-mass energy of 7 TeV, corresponding to an integrated luminosity of 36 pb$^{-1}$, is reported. A signal of $34.4 \pm 6.8$ events is obtained and the absence of signal is rejected with a statistical significance of more than nine standard deviations. The $\bar{B}^0_s \to D^0 K^{*0}$ branching fraction is measured relative to that of $\bar{B}^0 \to D^0 \rho^0$: $\frac{{\cal B}(\bar{B}^0_s \to D^0 K^{*0})}{{\cal B}(\bar{B}^0 \to D^0 \rho^0)} = 1.48 \pm 0.34 \pm 0.15 \pm 0.12$, where the first uncertainty is statistical, the second systematic and the third is due to the uncertainty on the ratio of the $B^0$ and $B^0_s$ hadronisation fractions.Comment: 10 pages, 3 figures, submitted to Phys. Lett. B; ISSN 0370-269

Which clinical research questions are the most important? Development and preliminary validation of the Australia & New Zealand Musculoskeletal (ANZMUSC) Clinical Trials Network Research Question Importance Tool (ANZMUSC-RQIT).

Background and aims High quality clinical research that addresses important questions requires significant resources. In resource-constrained environments, projects will therefore need to be prioritized. The Australia and New Zealand Musculoskeletal (ANZMUSC) Clinical Trials Network aimed to develop a stakeholder-based, transparent, easily implementable tool that provides a score for the 'importance' of a research question which could be used to rank research projects in order of importance. Methods Using a mixed-methods, multi-stage approach that included a Delphi survey, consensus workshop, inter-rater reliability testing, validity testing and calibration using a discrete-choice methodology, the Research Question Importance Tool (ANZMUSC-RQIT) was developed. The tool incorporated broad stakeholder opinion, including consumers, at each stage and is designed for scoring by committee consensus. Results The ANZMUSC-RQIT tool consists of 5 dimensions (compared to 6 dimensions for an earlier version of RQIT): (1) extent of stakeholder consensus, (2) social burden of health condition, (3) patient burden of health condition, (4) anticipated effectiveness of proposed intervention, and (5) extent to which health equity is addressed by the research. Each dimension is assessed by defining ordered levels of a relevant attribute and by assigning a score to each level. The scores for the dimensions are then summed to obtain an overall ANZMUSC-RQIT score, which represents the importance of the research question. The result is a score on an interval scale with an arbitrary unit, ranging from 0 (minimal importance) to 1000. The ANZMUSC-RQIT dimensions can be reliably ordered by committee consensus (ICC 0.73-0.93) and the overall score is positively associated with citation count (standardised regression coefficient 0.33, p<0.001) and journal impact factor group (OR 6.78, 95% CI 3.17 to 14.50 for 3rd tertile compared to 1st tertile of ANZMUSC-RQIT scores) for 200 published musculoskeletal clinical trials. Conclusion We propose that the ANZMUSC-RQIT is a useful tool for prioritising the importance of a research question.William J. Taylor, Robin Willink, Denise A. O, Connor, Vinay Patel, Allison Bourne, Ian A. Harris, Samuel L. Whittle, Bethan Richards, Ornella Clavisi, Sally Green, Rana S. Hinman, Chris G. Maher, Ainslie Cahill, Annie McPherson, Charlotte Hewson, Suzie E. May, Bruce Walker, Philip C. Robinson, Davina Ghersi, Jane Fitzpatrick, Tania Winzenberg, Kieran Fallon, Paul Glasziou, Laurent Billot, Rachelle Buchbinde

A steady-state genetic algorithm with resampling for noisy inventory control

Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested technique

Predictors of 1-year compliance with adaptive servoventilation in patients with heart failure and sleep disordered breathing: preliminary data from the ADVENT-HF trial

Despite its effectiveness in suppressing sleep disordered breathing (SDB), positive airway pressure therapy (PAP) is not always well tolerated by patients and long-term adherence can be problematic. Recently, two multicentre, randomised clinical trials (RCTs) tested the effects of PAP for patients with cardiovascular disease and co-existing SDB on morbidity and mortality with negative outcomes [1, 2]. Relatively poor adherence to PAP therapy (mean 3.7 and 3.3 h·day-1, respectively) in these two trials might have contributed to their poor results. Indeed, higher PAP use per day is associated with better clinical outcomes than lower use [3]

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes