When the ends outweigh the means: mood and level of identification in depression.

Journal ArticleResearch Support, Non-U.S. Gov'tCopyright © 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa businessResearch in healthy controls has found that mood influences cognitive processing via level of action identification: happy moods are associated with global and abstract processing; sad moods are associated with local and concrete processing. However, this pattern seems inconsistent with the high level of abstract processing observed in depressed patients, leading Watkins (2008, 2010) to hypothesise that the association between mood and level of goal/action identification is impaired in depression. We tested this hypothesis by measuring level of identification on the Behavioural Identification Form after happy and sad mood inductions in never-depressed controls and currently depressed patients. Participants used increasingly concrete action identifications as they became sadder and less happy, but this effect was moderated by depression status. Consistent with Watkins' (2008) hypothesis, increases in sad mood and decreases in happiness were associated with shifts towards the use of more concrete action identifications in never-depressed individuals, but not in depressed patients. These findings suggest that the putatively adaptive association between mood and level of identification is impaired in major depression

Processing mode causally influences emotional reactivity: distinct effects of abstract versus concrete construal on emotional response.

addresses: Mood Disorders Centre, School of Psychology, University of Exeter, UK. [email protected]: PMCID: PMC2672048types: Journal Article; Research Support, Non-U.S. Gov'tThis is a postprint of an article published in Emotion © 2008 copyright American Psychological Association. 'This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.' Emotion is available online at: http://www.apa.org/pubs/journals/emo/index.aspxThree studies are reported showing that emotional responses to stress can be modified by systematic prior practice in adopting particular processing modes. Participants were induced to think about positive and negative scenarios in a mode either characteristic of or inconsistent with the abstract-evaluative mind-set observed in depressive rumination, via explicit instructions (Experiments 1 and 2) and via implicit induction of interpretative biases (Experiment 3), before being exposed to a failure experience. In all three studies, participants trained into the mode antithetical to depressive rumination demonstrated less emotional reactivity following failure than participants trained into the mode consistent with depressive rumination. These findings provide evidence consistent with the hypothesis that processing mode modifies emotional reactivity and support the processing-mode theory of rumination

The Impatient May Use Limited Optimism to Minimize Regret

Discounted-sum games provide a formal model for the study of reinforcement learning, where the agent is enticed to get rewards early since later rewards are discounted. When the agent interacts with the environment, she may regret her actions, realizing that a previous choice was suboptimal given the behavior of the environment. The main contribution of this paper is a PSPACE algorithm for computing the minimum possible regret of a given game. To this end, several results of independent interest are shown. (1) We identify a class of regret-minimizing and admissible strategies that first assume that the environment is collaborating, then assume it is adversarial---the precise timing of the switch is key here. (2) Disregarding the computational cost of numerical analysis, we provide an NP algorithm that checks that the regret entailed by a given time-switching strategy exceeds a given value. (3) We show that determining whether a strategy minimizes regret is decidable in PSPACE

Probabilistic inference for determining options in reinforcement learning

Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to alleviate these concerns. Instead of learning a single monolithic policy, the agent learns a set of simpler sub-policies as well as the initiation and termination probabilities for each of those sub-policies. While existing option learning algorithms frequently require manual specification of components such as the sub-policies, we present an algorithm which infers all relevant components of the option framework from data. Furthermore, the proposed approach is based on parametric option representations and works well in combination with current policy search methods, which are particularly well suited for continuous real-world tasks. We present results on SMDPs with discrete as well as continuous state-action spaces. The results show that the presented algorithm can combine simple sub-policies to solve complex tasks and can improve learning performance on simpler tasks

Morphological characterization of sweet and sour cherry cultivars in a germplasm bank at Portugal

Nine sweet cherry and eight sour cherry varieties located in a germplasm bank at Fundauo, Portugal, were studied from the viewpoint of characterization. Most of them were autochthonous cultivars that have a high risk of extinction since at the present they are markedly minor varieties. Morphological characteristics were evaluated in different organs: crown and trunk of the trees, leaves, flowers and fruits, over a three consecutive years period. Statistical analyses were carried out in order to detect similarities between cultivars as well as the existence of synonymies. Qualitative characteristics of the fruits were scored in order to carry out the multivariate analysis. A dendrogram of the evaluated characters shows the marked differentiation between sour and sweet cherries and suggests the existing synonymies. Conservation of the autochthonous cultivars in the future is highly recommended

Behavioural activation therapy for depression after stroke (BEADS): a study protocol for a feasibility randomised controlled pilot trial of a psychological intervention for post-stroke depression

Background There is currently insufficient evidence for the clinical and cost-effectiveness of psychological therapies for treating post-stroke depression. Methods/Design BEADS is a parallel group feasibility multicentre randomised controlled trial with nested qualitative research and economic evaluation. The aim is to evaluate the feasibility of undertaking a full trial comparing behavioural activation (BA) to usual stroke care for 4 months for patients with post-stroke depression. We aim to recruit 72 patients with post-stroke depression over 12 months at three centres, with patients identified from the National Health Service (NHS) community and acute services and from the voluntary sector. They will be randomly allocated to receive behavioural activation in addition to usual care or usual care alone. Outcomes will be measured at 6 months after randomisation for both participants and their carers, to determine their effectiveness. The primary clinical outcome measure for the full trial will be the Patient Health Questionnaire-9 (PHQ-9). Rates of consent, recruitment and follow-up by centre and randomised group will be reported. The acceptability of the intervention to patients, their carers and therapists will also be assessed using qualitative interviews. The economic evaluation will be undertaken from the National Health Service and personal social service perspective, with a supplementary analysis from the societal perspective. A value of information analysis will be completed to identify the areas in which future research will be most valuable. Discussion The feasibility outcomes from this trial will provide the data needed to inform the design of a definitive multicentre randomised controlled trial evaluating the clinical and cost-effectiveness of behavioural activation for treating post-stroke depression

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Construction and in vivo assembly of a catalytically proficient and hyperthermostable de novo enzyme

Although catalytic mechanisms in natural enzymes are well understood, achieving the diverse palette of reaction chemistries in re-engineered native proteins has proved challenging. Wholesale modification of natural enzymes is potentially compromised by their intrinsic complexity, which often obscures the underlying principles governing biocatalytic efficiency. The maquette approach can circumvent this complexity by combining a robust de novo designed chassis with a design process that avoids atomistic mimicry of natural proteins. Here, we apply this method to the construction of a highly efficient, promiscuous, and thermostable artificial enzyme that catalyzes a diverse array of substrate oxidations coupled to the reduction of H2O2. The maquette exhibits kinetics that match and even surpass those of certain natural peroxidases, retains its activity at elevated temperature and in the presence of organic solvents, and provides a simple platform for interrogating catalytic intermediates common to natural heme-containing enzymes

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal