A search for axion-like particles was performed at the 400 GeV proton beam-dump experiment at CERN. Exploring an empty decay region of 35 m length and 9 m2 cross section, we searched for decays of neutral and penetrating scalar particles into a pair of photons, electrons or muons. No evidence for the existence for such particles was found in this experiment. Limits are quoted as a function of the mass and of the model independent decay constant of axions

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A search for heavy neutrinos was conducted in the neutrino beam produced by the 400 GeV proton beam-dump and in the 400 GeV wide-band neutrino beam at CERN. A heavy neutrino associated with the τ lepton was searched for in the beam-dump experiment. No assumption on the nature of heavy neutrinos was made in the wide-band beam experiment. A search was made for neutrinos decaying into two electrons and a light neutrino. Since no events were observed, an upper limit on the neutrino mixing angles as a function of the neutrino mass is derived

The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter-and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper we demonstrate that change in volumetric measurements of lesion load alone is not a good method for performing this separation, even for highly performing segmentation methods. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on a second external dataset confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracy of 83 % in separating stable and progressive timepoints.Both lesion volume and lesion count have previously been shown to be, together with clinical covariates, strong predictors of disease course across a population. However, in this paper we demonstrate that for individual patients, changes in these measures are not an adequate means of establishing no evidence of disease activity. Meanwhile, directly detecting tissue which changes, with high confidence, from non-lesion to lesion is a feasible methodology for identifying radiologically active patients.

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