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Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation.
Background Aversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown. Methods To elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations. Results Whereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms. Conclusion Our data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.
Gamma-ray line signatures can be expected in the very-high-energy (E-gamma > 100 GeV) domain due to self-annihilation or decay of dark matter (DM) particles in space. Such a signal would be readily distinguishable from astrophysical gamma-ray sources that in most cases produce continuous spectra that span over several orders of magnitude in energy. Using data collected with the H. E. S. S. gamma-ray instrument, upper limits on linelike emission are obtained in the energy range between similar to 500 GeV and similar to 25 TeV for the central part of the Milky Way halo and for extragalactic observations, complementing recent limits obtained with the Fermi-LAT instrument at lower energies. No statistically significant signal could be found. For monochromatic gamma-ray line emission, flux limits of (2 x 10(-7)-2 x 10(-5)) m(-2)s(-1)sr(-1) and (1 x 10(-8)- 2 x 10(-6)) m(-2)s(-1)sr(-1) are obtained for the central part of the Milky Way halo and extragalactic observations, respectively. For a DM particle mass of 1 TeV, limits on the velocity- averaged DM annihilation cross section <sigma upsilon >(chi chi ->gamma gamma) reach similar to 10(-27)cm(3)s(-1), based on the Einasto parametrization of the Galactic DM halo density profile. DOI: 10.1103/PhysRevLett.110.041301
Context. Globular clusters (GCs) are established emitters of high-energy (HE, 100 MeV < E < 100 GeV) gamma-ray radiation which could originate from the cumulative emission of the numerous millisecond pulsars (msPSRs) in the clusters’ cores or from inverse Compton (IC) scattering of relativistic leptons accelerated in the GC environment. These stellar clusters could also constitute a new class of sources in the very-high-energy (VHE, E > 100 GeV) gamma-ray regime, judging from the recent detection of a signal from the direction of Terzan 5 with the H.E.S.S. telescope array. Aims. To search for VHE gamma-ray sources associated with other GCs, and to put constraints on leptonic emission models, we systematically analyzed the observations towards 15 GCs taken with the H. E. S. S. array of imaging atmospheric Cherenkov telescopes. Methods. We searched for point-like and extended VHE gamma-ray emission from each GC in our sample and also performed a stacking analysis combining the data from all GCs to investigate the hypothesis of a population of faint emitters. Assuming IC emission as the origin of the VHE gamma-ray signal from the direction of Terzan 5, we calculated the expected gamma-ray flux from each of the 15 GCs, based on their number of millisecond pulsars, their optical brightness and the energy density of background photon fields. Results. We did not detect significant VHE gamma-ray emission from any of the 15 GCs in either of the two analyses. Given the uncertainties related to the parameter determinations, the obtained flux upper limits allow to rule out the simple IC/msPSR scaling model for NGC6388 and NGC7078. The upper limits derived from the stacking analyses are factors between 2 and 50 below the flux predicted by the simple leptonic scaling model, depending on the assumed source extent and the dominant target photon fields. Therefore, Terzan 5 still remains exceptional among all GCs, as the VHE gamma-ray emission either arises from extra-ordinarily efficient leptonic processes, or from a recent catastrophic event, or is even unrelated to the GC itself.
Background: First rank symptoms (FRS) of schizophrenia have been used for decades for diagnostic purposes. In the new version of the DSM-5, the American Psychiatric Association (APA) has abolished any further reference to FRS of schizophrenia and treats them like any other "criterion A' symptom (e.g. any kind of hallucination or delusion) with regard to their diagnostic implication. The ICD-10 is currently under revision and may follow suit. In this review, we discuss central points of criticism that are directed against the continuous use of first rank symptoms (FRS) to diagnose schizophrenia.
We used single-molecule FRET in combination with other biophysical methods and molecular simulations to investigate the effect of temperature on the dimensions of unfolded proteins. With singlemolecule FRET, this question can be addressed even under nearnative conditions, where most molecules are folded, allowing us to probe a wide range of denaturant concentrations and temperatures. We find a compaction of the unfolded state of a small cold shock protein with increasing temperature in both the presence and the absence of denaturant, with good agreement between the results from single-molecule FRET and dynamic light scattering. Although dissociation of denaturant from the polypeptide chain with increasing temperature accounts for part of the compaction, the results indicate an important role for additional temperaturedependent interactions within the unfolded chain. The observation of a collapse of a similar extent in the extremely hydrophilic, intrinsically disordered protein prothymosin suggests that the hydrophobic effect is not the sole source of the underlying interactions. Circular dichroism spectroscopy and replica exchange molecular dynamics simulations in explicit water show changes in secondary structure content with increasing temperature and suggest a contribution of intramolecular hydrogen bonding to unfolded state collapse.
Stress plays a key role in modulating addictive behavior and can cause relapse following periods of abstinence. Common effects of stress and alcohol on the dopaminergic system have been suggested, although the precise mechanisms are unclear. Here, we investigated 20 detoxified alcohol-dependent patients and 19 matched healthy controls and assessed striatal D2/D3 availability using [F-18]-fallypride positron emission tomography and stressful life events. We found a strong association between striatal D2/D3 availability and stress in patients, but not in healthy controls. Interestingly, we found increased D2/D3 receptor availability in patients with higher stress levels. This mirrors complex interactions between stress and alcohol intake in animal studies and emphasizes the importance to investigate stress exposure in neurobiological studies of addiction.
Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early-abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcohol-related stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their long-term goals. Such behaviors may include, but are not limited to, approaching alcohol.
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.