Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures

Abstract. In this work we propose a novel framework for generic event monitoring in live cell culture videos, built on the assumption that un-predictable observations should correspond to biological events. We use a small set of event-free data to train a multioutput multikernel Gaussian process model that operates as an event predictor by performing autore-gression on a bank of heterogeneous features extracted from consecutive frames of a video sequence. We show that the prediction error of this model can be used as a probability measure of the presence of relevant events, that can enable users to perform further analysis or monitoring of large-scale non-annotated data. We validate our approach in two phase-contrast sequence data sets containing mitosis and apoptosis events: a new private dataset of human bone cancer (osteosarcoma) cells and a benchmark dataset of stem cells

Magnetism, superconductivity and coupling in cuprate heterostructures probed by low-energy muon-spin rotation

We present a low-energy muon-spin-rotation study of the magnetic and superconducting properties of YBa2Cu3O7/PrBa2Cu3O7 trilayer and bilayer heterostructures. By determining the magnetic-field profiles throughout these structures we show that a finite superfluid density can be induced in otherwise semiconducting PrBa2Cu3O7 layers when juxtaposed to YBa2Cu3O7 "electrodes" while the intrinsic antiferromagnetic order is unaffected.Comment: 10 pages, 9 figures; figure 9 corrected in version

Development and validation of novel clinical endpoints in intermediate age-related macular degeneration in MACUSTAR

Background Currently, no validated clinical endpoints for treatment studies exist for intermediate age-related macular degeneration (iAMD). Objective The European MACUSTAR study aims to develop and clinically validate adequate clinical endpoints for future treatment studies in iAMD and to identify early determinants of disease progression to late stage AMD. Material and methods The MACUSTAR study protocol was developed by an international consortium of researchers from academia, the pharmaceutical industry and medical device companies. The MACUSTAR project is funded by the Innovative Medicines Initiative 2 (IMI2) of the European Union. Results The MACUSTAR study consists of a cross-sectional and a longitudinal investigation. A total of 750 subjects with early, intermediate and late AMD as well as control subjects with no signs of AMD will be included with a follow-up period of 3 years. Overall, 20 European study centers are involved. Conclusion The MACUSTAR project will generate large high-quality datasets, which will allow clinical validation of novel endpoints for future interventional trials in iAMD. The aim is that these endpoints will be accepted as suitable for medication approval studies by the regulatory authorities and that understanding of the disease process will be improved

Absolute value and temperature dependence of the magnetic penetration depth in Ba(Co0.074Fe0.926)(2)As-2

The absolute value and temperature dependence of the in-plane magnetic penetration depth λ have been measured on a single crystal of Ba(Co0.074Fe0.926)2As2 using low-energy muon-spin rotation and microwave cavity perturbation. The magnetic field profiles in the Meissner state are consistent with a local London model beyond a depth of 15 nm. We determine the gap symmetry through measurements of the temperature dependence of the superfluid density which follows a two-gap s-wave model over the entire temperature range below Tc. While the intermediate to high temperature data is well fit by an energy gap model in the BCS-like (weak-coupling) limit, a second smaller gap becomes apparent at low temperatures

A Monte Carlo strategy to integrate detection and model-based face analysis

We present a novel probabilistic approach for tting a statistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined using a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable detections and therefore does not need the detectors to take an early and possible wrong hard decision before tting. Second, it is open for integration of various cues to guide the tting process. Based on the proposed approach, we implemented a completely automatic, pose and illumination invariant face recognition application. We are able to train and test the building blocks of our application on di erent databases. The system is evaluated on the Multi-PIE database and reaches state of the art performance