Subcellular investigation of photosynthesis-driven carbon assimilation in the symbiotic reef coral Pocillopora damicornis.

Reef-building corals form essential, mutualistic endosymbiotic associations with photosynthetic Symbiodinium dinoflagellates, providing their animal host partner with photosynthetically derived nutrients that allow the coral to thrive in oligotrophic waters. However, little is known about the dynamics of these nutritional interactions at the (sub)cellular level. Here, we visualize with submicrometer spatial resolution the carbon and nitrogen fluxes in the intact coral-dinoflagellate association from the reef coral Pocillopora damicornis by combining nanoscale secondary ion mass spectrometry (NanoSIMS) and transmission electron microscopy with pulse-chase isotopic labeling using [(13)C]bicarbonate and [(15)N]nitrate. This allows us to observe that (i) through light-driven photosynthesis, dinoflagellates rapidly assimilate inorganic bicarbonate and nitrate, temporarily storing carbon within lipid droplets and starch granules for remobilization in nighttime, along with carbon and nitrogen incorporation into other subcellular compartments for dinoflagellate growth and maintenance, (ii) carbon-containing photosynthates are translocated to all four coral tissue layers, where they accumulate after only 15 min in coral lipid droplets from the oral gastroderm and within 6 h in glycogen granules from the oral epiderm, and (iii) the translocation of nitrogen-containing photosynthates is delayed by 3 h. IMPORTANCE: Our results provide detailed in situ subcellular visualization of the fate of photosynthesis-derived carbon and nitrogen in the coral-dinoflagellate endosymbiosis. We directly demonstrate that lipid droplets and glycogen granules in the coral tissue are sinks for translocated carbon photosynthates by dinoflagellates and confirm their key role in the trophic interactions within the coral-dinoflagellate association.

Climate change effects on animal and plant phylogenetic diversity in southern Africa

Much attention has been paid to the effects of climate change on species' range reductions and extinctions. There is however surprisingly little information on how climate change driven threat may impact the tree of life and result in loss of phylogenetic diversity (PD). Some plant families and mammalian orders reveal nonrandom extinction patterns, but many other plant families do not. Do these discrepancies reflect different speciation histories and does climate induced extinction result in the same discrepancies among different groups? Answers to these questions require representative taxon sampling. Here, we combine phylogenetic analyses, species distribution modeling, and climate change projections on two of the largest plant families in the Cape Floristic Region (Proteaceae and Restionaceae), as well as the second most diverse mammalian order in Southern Africa (Chiroptera), and an herbivorous insect genus (Platypleura) in the family Cicadidae to answer this question. We model current and future species distributions to assess species threat levels over the next 70years, and then compare projected with random PD survival. Results for these animal and plant clades reveal congruence. PD losses are not significantly higher under predicted extinction than under random extinction simulations. So far the evidence suggests that focusing resources on climate threatened species alone may not result in disproportionate benefits for the preservation of evolutionary history.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

The Impact of Information Provision on Agglomeration Bonus Performance: An Experimental Study on Local Networks

The Agglomeration Bonus (AB) is a mechanism to induce adjacent landowners to spatially coordinate their land use for the delivery of ecosystem services from farmland. This paper uses laboratory experiments to explore the performance of the AB in achieving the socially optimal land management configuration in a local network environment where the information available to subjects varies. The AB poses a coordination problem between two Nash equilibria: a Pareto dominant and a risk dominant equilibrium. The experiments indicate that if subjects are informed about both their direct and indirect neighbors’ actions, they are more likely to coordinate on the Pareto dominant equilibrium relative to the case where subjects have information about their direct neighbors’ action only. However, the extra information can only delay – and not prevent – the transition to the socially inferior risk dominant Nash equilibrium. In the long run, the AB mechanism may only be partially effective in enhancing delivery of ecosystem services on farming landscapes featuring local networks.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Developmental critical period in gentic redox dysregulation: animal and human studies in schizophrenia

Converging evidence favors an abnormal susceptibility to oxidative stress in schizophrenia. Decreased levels of glutathione (GSH), the major cellular antioxidant and redox regulator, was observed in cerebrospinal-fluid and prefrontal cortex of patients. Importantly, abnormal GSH synthesis of genetic origin was observed: Two case-control studies showed an association with a GAG trinucleotide repeat (TNR) polymorphism in the GSH key synthesizing enzyme glutamate-cysteine-ligase (GCL) catalytic subunit (GCLC) gene. The most common TNR genotype 7/7 was more frequent in controls, whereas the rarest TNR genotype 8/8 was three times more frequent in patients. The disease associated genotypes (35% of patients) correlated with decreased GCLC protein, GCL activity and GSH content. Similar GSH system anomalies were observed in early psychosis patients. Such redox dysregulation combined with environmental stressors at specific developmental stages could underlie structural and functional connectivity anomalies. In pharmacological and knock-out (KO) models, GSH deficit induces anomalies analogous to those reported in patients. (a) morphology: spine density and GABA-parvalbumine immunoreactivity (PV-I) were decreased in anterior cingulate cortex. KO mice showed delayed cortical PV-I at PD10. This effect is exacerbated in mice with increased DA from PD5-10. KO mice exhibit cortical impairment in myelin and perineuronal net known to modulate PV connectivity. (b) physiology: In cultured neurons, NMDA response are depressed by D2 activation. In hippocampus, NMDA-dependent synaptic plasticity is impaired and kainate induced g-oscillations are reduced in parallel to PV-I. (c) cognition: low GSH models show increased sensitivity to stress, hyperactivity, abnormal object recognition, olfactory integration and social behavior. In a clinical study, GSH precursor N-acetyl cysteine (NAC) as add on therapy, improves the negative symptoms and decreases the side effects of antipsychotics. In an auditory oddball paradigm, NAC improves the mismatched negativity, an evoked potential related to pre-attention and to NMDA receptors function. In summary, clinical and experimental evidence converge to demonstrate that a genetically induced dysregulation of GSH synthesis combined with environmental insults in early development represent a major risk factor contributing to the development of schizophrenia Conclusion Based on these data, we proposed a model for PSIP1 promoter activity involving a complex interplay between yet undefined regulatory elements to modulate gene expression.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

La dignité de l'animal. Quel statut pour les animaux à l'heure des technosciences?

A bien des égards, comme le philosophe Emmanuel Kant l'avait déjà signalé, la manière dont nous traitons les animaux nous renvoie en miroir notre propre attitude envers nos compagnons humains. La maladie de la vache folle n'est-elle pas le résultat d'une rationalité industrielle déraisonnable et, par là même, le révélateur des dérives de la rationalité instrumentale et des menaces qui pèsent sur l'éthique ? Les perspectives ouvertes par le génie génétique et par les xénotransplantations n'annoncent-elles pas une fuite en avant dans les illusions d'une technoscience sans conscience et sans précaution ? Comment trouver un équilibre entre les intérêts des patients, les finalités de la médecine, le respect de l'animal et les réalités de l'économie ou de la politique ? Pour promouvoir une éthique à la hauteur de ces questions, faut-il abandonner tout anthropocentrisme, adopter une philosophie centrée sur la vie et le vivant (biocentrisme) prenant uniquement en compte le critère de la souffrance humaine et animale (pathocentrisme) ?Fruit d'un colloque interdisciplinaire tenu à Lausanne en mai 1999, cet ouvrage donne la parole à des chercheurs d'horizon très variés : des spécialistes de la zoologie, de l'étude du comportement animal, de la douleur animale et humaine et de l'anthropologie culturelle, des juristes, des médecins, des philosophes et des théologiens.

Introduction. Dignité humaine et dignité de l'animal: une dialectique asymétrique et nécessaire

(Résumé de l'ouvrage) A bien des égards, comme le philosophe Emmanuel Kant l'avait déjà signalé, la manière dont nous traitons les animaux nous renvoie en miroir notre propre attitude envers nos compagnons humains. La maladie de la vache folle n'est-elle pas le résultat d'une rationalité industrielle déraisonnable et, par là même, le révélateur des dérives de la rationalité instrumentale et des menaces qui pèsent sur l'éthique ? Les perspectives ouvertes par le génie génétique et par les xénotransplantations n'annoncent-elles pas une fuite en avant dans les illusions d'une technoscience sans conscience et sans précaution ? Comment trouver un équilibre entre les intérêts des patients, les finalités de la médecine, le respect de l'animal et les réalités de l'économie ou de la politique ? Pour promouvoir une éthique à la hauteur de ces questions, faut-il abandonner tout anthropocentrisme, adopter une philosophie centrée sur la vie et le vivant (biocentrisme) prenant uniquement en compte le critère de la souffrance humaine et animale (pathocentrisme) ?Fruit d'un colloque interdisciplinaire tenu à Lausanne en mai 1999, cet ouvrage donne la parole à des chercheurs d'horizon très variés : des spécialistes de la zoologie, de l'étude du comportement animal, de la douleur animale et humaine et de l'anthropologie culturelle, des juristes, des médecins, des philosophes et des théologiens.

Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks

Sampling issues represent a topic of ongoing interest to the forensic science community essentially because of their crucial role in laboratory planning and working protocols. For this purpose, forensic literature described thorough (Bayesian) probabilistic sampling approaches. These are now widely implemented in practice. They allow, for instance, to obtain probability statements that parameters of interest (e.g., the proportion of a seizure of items that present particular features, such as an illegal substance) satisfy particular criteria (e.g., a threshold or an otherwise limiting value). Currently, there are many approaches that allow one to derive probability statements relating to a population proportion, but questions on how a forensic decision maker - typically a client of a forensic examination or a scientist acting on behalf of a client - ought actually to decide about a proportion or a sample size, remained largely unexplored to date. The research presented here intends to address methodology from decision theory that may help to cope usefully with the wide range of sampling issues typically encountered in forensic science applications. The procedures explored in this paper enable scientists to address a variety of concepts such as the (net) value of sample information, the (expected) value of sample information or the (expected) decision loss. All of these aspects directly relate to questions that are regularly encountered in casework. Besides probability theory and Bayesian inference, the proposed approach requires some additional elements from decision theory that may increase the efforts needed for practical implementation. In view of this challenge, the present paper will emphasise the merits of graphical modelling concepts, such as decision trees and Bayesian decision networks. These can support forensic scientists in applying the methodology in practice. How this may be achieved is illustrated with several examples. The graphical devices invoked here also serve the purpose of supporting the discussion of the similarities, differences and complementary aspects of existing Bayesian probabilistic sampling criteria and the decision-theoretic approach proposed throughout this paper.