A two-component flavin-dependent monooxygenase involved in actinorhodin biosynthesis in Streptomyces coelicolor.

International audienceThe two-component flavin-dependent monooxygenases belong to an emerging class of enzymes involved in oxidation reactions in a number of metabolic and biosynthetic pathways in microorganisms. One component is a NAD(P)H:flavin oxidoreductase, which provides a reduced flavin to the second component, the proper monooxygenase. There, the reduced flavin activates molecular oxygen for substrate oxidation. Here, we study the flavin reductase ActVB and ActVA-ORF5 gene product, both reported to be involved in the last step of biosynthesis of the natural antibiotic actinorhodin in Streptomyces coelicolor. For the first time we show that ActVA-ORF5 is a FMN-dependent monooxygenase that together with the help of the flavin reductase ActVB catalyzes the oxidation reaction. The mechanism of the transfer of reduced FMN between ActVB and ActVA-ORF5 has been investigated. Dissociation constant values for oxidized and reduced flavin (FMNox and FMNred) with regard to ActVB and ActVA-ORF5 have been determined. The data clearly demonstrate a thermodynamic transfer of FMNred from ActVB to ActVA-ORF5 without involving a particular interaction between the two protein components. In full agreement with these data, we propose a reaction mechanism in which FMNox binds to ActVB, where it is reduced, and the resulting FMNred moves to ActVA-ORF5, where it reacts with O2 to generate a flavinperoxide intermediate. A direct spectroscopic evidence for the formation of such species within ActVA-ORF5 is reported

Power-up: a reanalysis of 'power failure' in neuroscience using mixture modelling

Evidence for endemically low statistical power has recently cast neuroscience findings into doubt. If low statistical power plagues neuroscience, this reduces confidence in reported effects. However, if statistical power is not uniformly low, such blanket mistrust might not be warranted. Here, we provide a different perspective on this issue, analysing data from an influential paper reporting a median power of 21% across 49 meta-analyses (Button et al., 2013). We demonstrate, using Gaussian mixture modelling, that the sample of 730 studies included in that analysis comprises several subcomponents; therefore the use of a single summary statistic is insufficient to characterise the nature of the distribution. We find that statistical power is extremely low for studies included in meta-analyses that reported a null result; and that it varies substantially across subfields of neuroscience, with particularly low power in candidate gene association studies. Thus, while power in neuroscience remains a critical issue, the notion that studies are systematically underpowered is not the full story: low power is far from a universal problem. SIGNIFICANCE STATEMENT: Recently, researchers across the biomedical and psychological sciences have become concerned with the reliability of results. One marker for reliability is statistical power: the probability of finding a statistically significant result, given that the effect exists. Previous evidence suggests that statistical power is low across the field of neuroscience. Our results present a more comprehensive picture of statistical power in neuroscience: on average, studies are indeed underpowered-some very seriously so-but many studies show acceptable or even exemplary statistical power. We show that this heterogeneity in statistical power is common across most subfields in neuroscience (psychology, neuroimaging, etc.). This new, more nuanced picture of statistical power in neuroscience could affect not only scientific understanding, but potentially policy and funding decisions for neuroscience research

Association between habenula dysfunction and motivational symptoms in unmedicated major depressive disorder

The lateral habenula plays a central role in reward and punishment processing and has been suggested to drive the cardinal symptom of anhedonia in depression. This hypothesis is largely based on observations of habenula hypermetabolism in animal models of depression, but the activity of habenula and its relationship with clinical symptoms in patients with depression remains unclear. High-resolution functional magnetic resonance imaging (fMRI) and computational modelling were used to investigate the activity of the habenula during a probabilistic reinforcement learning task with rewarding and punishing outcomes in 21 unmedicated patients with major depression and 17 healthy participants. High-resolution anatomical scans were also acquired to assess group differences in habenula volume. Healthy individuals displayed the expected activation in the left habenula during receipt of punishment and this pattern was confirmed in the computational analysis of prediction error processing. In depressed patients, there was a trend towards attenuated left habenula activation to punishment, while greater left habenula activation was associated with more severe depressive symptoms and anhedonia. We also identified greater habenula volume in patients with depression, which was associated with anhedonic symptoms. Habenula dysfunction may contribute to abnormal response to punishment in patients with depression, and symptoms such as anhedonia

Le SIG du projet Ifora et les bases de données associées : outils de base pour les études phylogéographiques

Les SIG permettent de gérer des informations géoréférencées, d'intégrer différentes dimensions de l'espace, de croiser et combiner des données multiples (géomorphologie, climat, végétation, faune...), de construire des hypothèses et de les visualiser spatialement (proximité génétique et spatiale de diverses espèces ; recherche de paramètres environnementaux déterminant l'endémisme d'une espèce...), d'assigner des variables écologiques à postériori aux individus collectés et d'en tirer des analyses statistiques, et de présenter graphiquement les informations pertinentes pour une meilleure communication. Le SIG du projet IFORA a été conçu comme un outil intégrateur des données des diverses équipes, permettant (i) de comparer les patrons de répartition des espèces sur lesquelles nous travaillons, (ii) de poser des hypothèses sur les déterminants environnementaux de ces répartitions et (iii) de les confronter aux hypothèses relatives à l'existence de refuges forestiers. Le SIG du projet incorpore actuellement divers types de données : MNT et courbes de niveaux, réseaux hydrographiques, données pluviométriques, frontières des états, réseaux routiers, villes, cartes de végétation. L'un des travaux les plus importants réalisé à ce jour concerne la digitalisation de la carte de végétation de R. Letouzey sur l'ensemble du Cameroun. Le SIG permet de travailler à plusieurs échelles et de réaliser des cartes adaptées aux besoins des diverses équipes. Il permet, par exemple, de positionner les points de collecte d'échantillons (botaniques, zoologiques) sur la carte de végétation de Letouzey afin de développer des analyses écologiques. Toutefois, à ce jour, toutes les potentialités du SIG n'ont pas encore été valorisées, tant d'un point de vue scientifique que de communication. (Texte intégral

Intermolecular electron transfer in two-iron superoxide reductase: a putative role for the desulforedoxin center as an electron donor to the iron active site.

International audienceSuperoxide reductase (SOR) is a superoxide detoxification system present in some microorganisms. Its active site consists of an unusual mononuclear iron center with an FeN4S1 coordination which catalyzes the one-electron reduction of superoxide to form hydrogen peroxide. Different classes of SORs have been described depending on the presence of an additional rubredoxin-like, desulforedoxin iron center, whose function has remained unknown until now. In this work, we investigated the mechanism of the reduction of the SOR iron active site using the NADPH:flavodoxin oxidoreductase from Escherichia coli, which was previously shown to efficiently transfer electrons to the Desulfoarculus baarsii SOR. When present, the additional rubredoxin-like iron center could function as an electronic relay between cellular reductases and the iron active site for superoxide reduction. This electron transfer was mainly intermolecular, between the rubredoxin-like iron center of one SOR and the iron active site of another SOR. These data provide the first experimental evidence for a possible role of the rubredoxin-like iron center in the superoxide detoxifying activity of SOR

The impact of induced anxiety on affective response inhibition

Studying the effects of experimentally induced anxiety in healthy volunteers may increase our understanding of the mechanisms underpinning anxiety disorders. Experimentally induced stress (via threat of unpredictable shock) improves accuracy at withholding a response on the sustained attention to response task (SART), and in separate studies improves accuracy to classify fearful faces, creating an affective bias. Integrating these findings, participants at two public science engagement events (n = 46, n = 55) were recruited to explore the effects of experimentally induced stress on an affective version of the SART. We hypothesized that we would see an improved accuracy at withholding a response to affectively congruent stimuli (i.e. increased accuracy at withholding a response to fearful 'no-go' distractors) under threat of shock. Induced anxiety slowed reaction time, and at the second event quicker responses were made to fearful stimuli. However, we did not observe improved inhibition overall during induced anxiety, and there was no evidence to suggest an interaction between induced anxiety and stimulus valence on response accuracy. Indeed Bayesian analysis provided decisive evidence against this hypothesis. We suggest that the presence of emotional stimuli might make the safe condition more anxiogenic, reducing the differential between conditions and knocking out any threat-potentiated improvement

Toward an Ethics of AI Belief

Philosophical research in AI has hitherto largely focused on the ethics of AI. In this paper we, an ethicist of belief and a machine learning scientist, suggest that we need to pursue a novel area of philosophical research in AI - the epistemology of AI, and in particular an ethics of belief for AI, i.e., an ethics of AI belief. Here we take the ethics of belief, a field that has been defined in various ways, to refer to a sub-field within epistemology. This subfield is concerned with the study of possible moral, practical, and other non-alethic dimensions of belief. And in this paper, we will primarily be concerned with the normative question within the ethics of belief of what agents - both human and artificial - ought to believe, rather than with descriptive questions concerning whether certain beliefs meet various evaluative standards such as being true, being justified or warranted, constituting knowledge, and so on. We suggest four topics in extant work in the ethics of (human) belief that can be applied to an ethics of AI belief: doxastic wronging by AI; morally owed beliefs; pragmatic and moral encroachment on AI beliefs; and moral responsibility for AI beliefs. We also indicate an important nascent area of philosophical research in epistemic injustice and AI that has not yet been recognized as research in the ethics of AI belief, but which is so in virtue of concerning moral and practical dimensions of belief

The life and labours of the late Rev. John Valton written by himself ; and now edited with many additions and letters

https://place.asburyseminary.edu/ecommonsatsdigitalresources/1428/thumbnail.jp

Recommendations for Bayesian hierarchical model specifications for case-control studies in mental health

Hierarchical model fitting has become commonplace for case-control studies of cognition and behaviour in mental health. However, these techniques require us to formalise assumptions about the data-generating process at the group level, which may not be known. Specifically, researchers typically must choose whether to assume all subjects are drawn from a common population, or to model them as deriving from separate populations. These assumptions have profound implications for computational psychiatry, as they affect the resulting inference (latent parameter recovery) and may conflate or mask true group-level differences. To test these assumptions we ran systematic simulations on synthetic multi-group behavioural data from a commonly used multi-armed bandit task (reinforcement learning task). We then examined recovery of group differences in latent parameter space under the two commonly used generative modelling assumptions: (1) modelling groups under a common shared group-level prior (assuming all participants are generated from a common distribution, and are likely to share common characteristics); (2) modelling separate groups based on symptomatology or diagnostic labels, resulting in separate group-level priors. We evaluated the robustness of these approaches to variations in data quality and prior specifications on a variety of metrics. We found that fitting groups separately (assumptions 2), provided the most accurate and robust inference across all conditions. Our results suggest that when dealing with data from multiple clinical groups, researchers should analyse patient and control groups separately as it provides the most accurate and robust recovery of the parameters of interest.Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstrac