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The Low Earth Orbit (LEO) experiment Biology and Mars Experiment (BIOMEX) is an interdisciplinary and international space research project selected by ESA. The experiment will be accommodated on the space exposure facility EXPOSE-R2 on the International Space Station (ISS) and is foreseen to be launched in 2013. The prime objective of BIOMEX is to measure to what extent biomolecules, such as pigments and cellular components, are resistant to and able to maintain their stability under space and Mars-like conditions. The results of BIOMEX will be relevant for space proven biosignature definition and for building a biosignature data base (e.g. the proposed creation of an international Raman library). The library will be highly relevant for future space missions such as the search for life on Mars. The secondary scientific objective is to analyze to what extent terrestrial extremophiles are able to survive in space and to determine which interactions between biological samples and selected minerals (including terrestrial, Moon- and Mars analogs) can be observed under space and Mars-like conditions. In this context, the Moon will be an additional platform for performing similar experiments with negligible magnetic shielding and higher solar and galactic irradiation compared to LEO. Using the Moon as an additional astrobiological exposure platform to complement ongoing astrobiological LEO investigations could thus enhance the chances of detecting organic traces of life on Mars. We present a lunar lander mission with two related objectives: a lunar lander equipped with Raman and PanCam instruments which can analyze the lunar surface and survey an astrobiological exposure platform. This dual use of testing mission technology together with geo- and astrobiological analyses will significantly increase the science return, and support the human preparation objectives. It will provide knowledge about the Moon's surface itself and, in addition, monitor the stability of life-markers, such as cells, cell components and pigments, in an extraterrestrial environment with much closer radiation properties to the surface of Mars. The combination of a Raman data base of these data together with data from LEO and space simulation experiments, will lead to further progress on the analysis and interpretation of data that we will obtain from future Moon and Mars exploration missions.
The Richardson-Lucy deconvolution algorithm was applied to astronomical images in the very high-energy regime with photon energies above 100 GeV. Through a systematic study with respect to source significance, background level and source morphology we were able to derive optimal deconvolution parameters. The results presented show that deconvolution makes it possible to study structural details well below the angular resolution of the very high-energy gamma-ray experiment. (C) 2012 Elsevier B.V. All rights reserved.
The 2010 very high energy gamma-ray flare and 10 years ofmulti-wavelength oservations of M 87
(2012)
The giant radio galaxy M 87 with its proximity (16 Mpc), famous jet, and very massive black hole ((3-6) x 10(9) M-circle dot) provides a unique opportunity to investigate the origin of very high energy (VHE; E > 100 GeV) gamma-ray emission generated in relativistic outflows and the surroundings of supermassive black holes. M 87 has been established as a VHE gamma-ray emitter since 2006. The VHE gamma-ray emission displays strong variability on timescales as short as a day. In this paper, results from a joint VHE monitoring campaign on M 87 by the MAGIC and VERITAS instruments in 2010 are reported. During the campaign, a flare at VHE was detected triggering further observations at VHE (H.E.S.S.), X-rays (Chandra), and radio (43 GHz Very Long Baseline Array, VLBA). The excellent sampling of the VHE gamma-ray light curve enables one to derive a precise temporal characterization of the flare: the single, isolated flare is well described by a two-sided exponential function with significantly different flux rise and decay times of tau(rise)(d) = (1.69 +/- 0.30) days and tau(decay)(d) = (0.611 +/- 0.080) days, respectively. While the overall variability pattern of the 2010 flare appears somewhat different from that of previous VHE flares in 2005 and 2008, they share very similar timescales (similar to day), peak fluxes (Phi(>0.35 TeV) similar or equal to (1-3) x 10(-11) photons cm(-2) s(-1)), and VHE spectra. VLBA radio observations of 43 GHz of the inner jet regions indicate no enhanced flux in 2010 in contrast to observations in 2008, where an increase of the radio flux of the innermost core regions coincided with a VHE flare. On the other hand, Chandra X-ray observations taken similar to 3 days after the peak of the VHE gamma-ray emission reveal an enhanced flux from the core (flux increased by factor similar to 2; variability timescale <2 days). The long-term (2001-2010) multi-wavelength (MWL) light curve of M 87, spanning from radio to VHE and including data from Hubble Space Telescope, Liverpool Telescope, Very Large Array, and European VLBI Network, is used to further investigate the origin of the VHE gamma-ray emission. No unique, common MWL signature of the three VHE flares has been identified. In the outer kiloparsec jet region, in particular in HST-1, no enhanced MWL activity was detected in 2008 and 2010, disfavoring it as the origin of the VHE flares during these years. Shortly after two of the three flares (2008 and 2010), the X-ray core was observed to be at a higher flux level than its characteristic range (determined from more than 60 monitoring observations: 2002-2009). In 2005, the strong flux dominance of HST-1 could have suppressed the detection of such a feature. Published models for VHE gamma-ray emission from M 87 are reviewed in the light of the new data.
Background Exercise rehabilitation is highly recommended by current guidelines on prevention of cardiovascular disease, but its implementation is still poor. Many clinicians experience difficulties in prescribing exercise in the presence of different concomitant cardiovascular diseases and risk factors within the same patient. It was aimed to develop a digital training and decision support system for exercise prescription in cardiovascular disease patients in clinical practice: the European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool. Methods EXPERT working group members were requested to define (a) diagnostic criteria for specific cardiovascular diseases, cardiovascular disease risk factors, and other chronic non-cardiovascular conditions, (b) primary goals of exercise intervention, (c) disease-specific prescription of exercise training (intensity, frequency, volume, type, session and programme duration), and (d) exercise training safety advices. The impact of exercise tolerance, common cardiovascular medications and adverse events during exercise testing were further taken into account for optimized exercise prescription. Results Exercise training recommendations and safety advices were formulated for 10 cardiovascular diseases, five cardiovascular disease risk factors (type 1 and 2 diabetes, obesity, hypertension, hypercholesterolaemia), and three common chronic non-cardiovascular conditions (lung and renal failure and sarcopaenia), but also accounted for baseline exercise tolerance, common cardiovascular medications and occurrence of adverse events during exercise testing. An algorithm, supported by an interactive tool, was constructed based on these data. This training and decision support system automatically provides an exercise prescription according to the variables provided. Conclusion This digital training and decision support system may contribute in overcoming barriers in exercise implementation in common cardiovascular diseases.
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age-and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.