A combination is presented of the inclusive deep inelastic cross sections measured by the H1 and ZEUS Collaborations in neutral and charged current unpolarised e ± p scattering at HERA during the period 1994-2000. The data span six orders of magnitude in negative four-momentum-transfer squared, Q 2 , and in Bjorken x. The combination method used takes the correlations of systematic uncertainties into account, resulting in an improved accuracy. The combined data are the sole input in a NLO QCD analysis which determines a new set of parton distributions, HERAPDF1.0, with small experimental uncertainties. This set includes an estimate of the model and parametrisation uncertainties of the fit result.

Subject motion during magnetic resonance imaging (MRI) has been problematic since its introduction as a clinical imaging modality. While sensitivity to particle motion or blood flow can be used to provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications. It is one of the most frequent sources of artefacts. Over 30 years of research have produced numerous methods to mitigate or correct for motion artefacts, but no single method can be applied in all imaging situations. Instead, a ‘toolbox’ of methods exists, where each tool is suitable for some tasks, but not for others. This article reviews the origins of motion artefacts and presents current mitigation and correction methods. In some imaging situations, the currently available motion correction tools are highly effective; in other cases, appropriate tools still need to be developed. It seems likely that this multifaceted approach will be what eventually solves the motion sensitivity problem in MRI, rather than a single solution that is effective in all situations. This review places a strong emphasis on explaining the physics behind the occurrence of such artefacts, with the aim of aiding artefact detection and mitigation in particular clinical situations.

Motion correction in magnetic resonance imaging by real‐time adjustment of the imaging pulse sequence was first proposed more than 20 years ago. Recent advances have resulted from combining real‐time correction with new navigator and external tracking mechanisms capable of quantifying rigid‐body motion in all 6 degrees of freedom. The technique is now often referred to as “prospective motion correction.” This article describes the fundamentals of prospective motion correction and reviews the latest developments in its application to brain imaging and spectroscopy. Although emphasis is placed on the brain as the organ of interest, the same principles apply whenever the imaged object can be approximated as a rigid body. Prospective motion correction can be used with most MR sequences, so it has potential to make a large impact in clinical routine. To maximize the benefits obtained from the technique, there are, however, several challenges still to be met. These include practical implementation issues, such as obtaining tracking data with minimal delay, and more fundamental problems, such as the magnetic field distortions caused by a moving object. This review discusses these challenges and summarizes the state of the art. We hope that this work will motivate further developments in prospective motion correction and help the technique to reach its full potential. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

Inappropriate transfers are a potentially large problem. Some inappropriate transfers may be associated with poor quality of care in SNFs. This study demonstrates that structured implicit review meets criteria for reliable assessment of inappropriate transfer rates. Structured implicit review may be a valuable tool for identifying inappropriate transfers from SNFs to EDs and hospitals.

Measurements of open charm production cross sections in deep-inelastic ep scattering at HERA from the H1 and ZEUS Collaborations are combined. Reduced cross sections σ cc red for charm production are obtained in the kinematic range of photon virtuality 2.5 ≤ Q 2 ≤ 2000 GeV 2 and Bjorken scaling variable 3 · 10 −5 ≤ x ≤ 5 · 10 −2 . The combination method accounts for the correlations of the systematic uncertainties among the different data sets. The combined charm data together with the combined inclusive a e-mail: levy@alzt. deep-inelastic scattering cross sections from HERA are used as input for a detailed NLO QCD analysis to study the influence of different heavy flavour schemes on the parton distribution functions. The optimal values of the charm mass as a parameter in these different schemes are obtained. The implications on the NLO predictions for W ± and Z production cross sections at the LHC are investigated. Using the fixed flavour number scheme, the running mass of the charm quark is determined.

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Inclusive charm and beauty cross sections are measured in e − p and e + p neutral current collisions at HERA in the kinematic region of photon virtuality 5 ≤ Q 2 ≤ 2000 GeV 2 and Bjorken scaling variable 0.0002 ≤ x ≤ 0.05. The data were collected with the H1 detector in the years 2006 and 2007 corresponding to an integrated luminosity of 189 pb −1 . The numbers of charm and beauty events are determined using variables reconstructed by the H1 vertex detector including the impact parameter of tracks to the primary vertex and the position of the secondary vertex. The measurements are combined with previous data and compared to QCD predictions.

A measurement is presented of inelastic photoand electroproduction of J /ψ mesons in ep scattering at HERA. The data were recorded with the H1 detector in the period from 2004 to 2007. Single and double differential cross sections are determined and the helicity distributions of the J /ψ mesons are analysed. The results are compared to theoretical predictions in the colour singlet model and in the framework of non-relativistic QCD. Calculations in the colour singlet model using a k T factorisation ansatz are able to give a good description of the data, while colour singlet model calculations to next-to-leading order in collinear factorisation underestimate the data.