The International-Trade Network: Gravity Equations and Topological Properties

This paper begins to explore the determinants of the topological properties of the international - trade network (ITN). We fit bilateral-trade flows using a standard gravity equation to build a "residual" ITN where trade-link weights are depurated from geographical distance, size, border effects, trade agreements, and so on. We then compare the topological properties of the original and residual ITNs. We find that the residual ITN displays, unlike the original one, marked signatures of a complex system, and is characterized by a very different topological architecture. Whereas the original ITN is geographically clustered and organized around a few large-sized hubs, the residual ITN displays many small-sized but trade-oriented countries that, independently of their geographical position, either play the role of local hubs or attract large and rich countries in relatively complex trade-interaction patterns

Logical Development of the Cell Ontology

<p>Abstract</p> <p>Background</p> <p>The Cell Ontology (CL) is an ontology for the representation of <it>in vivo </it>cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.</p> <p>Results</p> <p>Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.</p> <p>Conclusion</p> <p>Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.</p

Compound A, a Dissociated Glucocorticoid Receptor Modulator, Inhibits T-bet (Th1) and Induces GATA-3 (Th2) Activity in Immune Cells

Background: Compound A (CpdA) is a dissociating non-steroidal glucocorticoid receptor (GR) ligand which has antiinflammatory properties exerted by down-modulating proinflammatory gene expression. By favouring GR monomer formation, CpdA does not enhance glucocorticoid (GC) response element-driven gene expression, resulting in a reduced side effect profile as compared to GCs. Considering the importance of Th1/Th2 balance in the final outcome of immune and inflammatory responses, we analyzed how selective GR modulation differentially regulates the activity of T-bet and GATA-3, master drivers of Th1 and Th2 differentiation, respectively. Results: Using Western analysis and reporter gene assays, we show in murine T cells that, similar to GCs, CpdA inhibits T-bet activity via a transrepressive mechanism. Different from GCs, CpdA induces GATA-3 activity by p38 MAPK-induction of GATA-3 phosphorylation and nuclear translocation. CpdA effects are reversed by the GR antagonist RU38486, proving the involvement of GR in these actions. ELISA assays demonstrate that modulation of T-bet and GATA-3 impacts on cytokine production shown by a decrease in IFN-c and an increase in IL-5 production, respectively. Conclusions: Taken together, through their effect favoring Th2 over Th1 responses, particular dissociated GR ligands, fo

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors

Distributed Dendritic Processing Facilitates Object Detection: A Computational Analysis on the Visual System of the Fly

Hennig P, Möller R, Egelhaaf M. Distributed Dendritic Processing Facilitates Object Detection: A Computational Analysis on the Visual System of the Fly. PLoS ONE. 2008;3(8): e3092.Background: Detecting objects is an important task when moving through a natural environment. Flies, for example, may land on salient objects or may avoid collisions with them. The neuronal ensemble of Figure Detection cells (FD-cells) in the visual system of the fly is likely to be involved in controlling these behaviours, as these cells are more sensitive to objects than to extended background structures. Until now the computations in the presynaptic neuronal network of FD-cells and, in particular, the functional significance of the experimentally established distributed dendritic processing of excitatory and inhibitory inputs is not understood. Methodology/Principal Findings: We use model simulations to analyse the neuronal computations responsible for the preference of FD-cells for small objects. We employed a new modelling approach which allowed us to account for the spatial spread of electrical signals in the dendrites while avoiding detailed compartmental modelling. The models are based on available physiological and anatomical data. Three models were tested each implementing an inhibitory neural circuit, but differing by the spatial arrangement of the inhibitory interaction. Parameter optimisation with an evolutionary algorithm revealed that only distributed dendritic processing satisfies the constraints arising from electrophysiological experiments. In contrast to a direct dendro-dendritic inhibition of the FD-cell (Direct Distributed Inhibition model), an inhibition of its presynaptic retinotopic elements (Indirect Distributed Inhibition model) requires smaller changes in input resistance in the inhibited neurons during visual stimulation. Conclusions/Significance: Distributed dendritic inhibition of retinotopic elements as implemented in our Indirect Distributed Inhibition model is the most plausible wiring scheme for the neuronal circuit of FD-cells. This microcircuit is computationally similar to lateral inhibition between the retinotopic elements. Hence, distributed inhibition might be an alternative explanation of perceptual phenomena currently explained by lateral inhibition networks

Exploring the contamination of the DES-Y1 cluster sample with SPT-SZ selected clusters

We perform a cross validation of the cluster catalogue selected by the red-sequence Matched-filter Probabilistic Percolation algorithm (redMaPPer) in Dark Energy Survey year 1 (DES-Y1) data by matching it with the Sunyaev–Zel’dovich effect (SZE) selected cluster catalogue from the South Pole Telescope SPT-SZ survey. Of the 1005 redMaPPer selected clusters with measured richness λ̂ >40 in the joint footprint, 207 are confirmed by SPT-SZ. Using the mass information from the SZE signal, we calibrate the richness–mass relation using a Bayesian cluster population model. We find a mass trend λ ∝ MB consistent with a linear relation (B ∼ 1), no significant redshift evolution and an intrinsic scatter in richness of σλ = 0.22 ± 0.06. By considering two error models, we explore the impact of projection effects on the richness–mass modelling, confirming that such effects are not detectable at the current level of systematic uncertainties. At low richness SPT-SZ confirms fewer redMaPPer clusters than expected. We interpret this richness dependent deficit in confirmed systems as due to the increased presence at low richness of low-mass objects not correctly accounted for by our richness-mass scatter model, which we call contaminants. At a richness λ̂ =40 ⁠, this population makes up >12 per cent (97.5 percentile) of the total population. Extrapolating this to a measured richness λ̂ =20 yields >22 per cent (97.5 percentile). With these contamination fractions, the predicted redMaPPer number counts in different plausible cosmologies are compatible with the measured abundance. The presence of such a population is also a plausible explanation for the different mass trends (B ∼ 0.75) obtained from mass calibration using purely optically selected clusters. The mean mass from stacked weak lensing (WL) measurements suggests that these low-mass contaminants are galaxy groups with masses ∼3–5 × 1013 M⊙ which are beyond the sensitivity of current SZE and X-ray surveys but a natural target for SPT-3G and eROSITA

Insights on Glucocorticoid Receptor Activity Modulation through the Binding of Rigid Steroids

Background: The glucocorticoid receptor (GR) is a transcription factor that regulates gene expression in a ligand-dependent fashion. This modular protein is one of the major pharmacological targets due to its involvement in both cause and treatment of many human diseases. Intense efforts have been made to get information about the molecular basis of GR activity. Methodology/Principal Findings: Here, the behavior of four GR-ligand complexes with different glucocorticoid and antiglucocorticoid properties were evaluated. The ability of GR-ligand complexes to oligomerize in vivo was analyzed by performing the novel Number and Brightness assay. Results showed that most of GR molecules form homodimers inside the nucleus upon ligand binding. Additionally, in vitro GR-DNA binding analyses suggest that ligand structure modulates GRDNA interaction dynamics rather than the receptor's ability to bind DNA. On the other hand, by coimmunoprecipitation studies we evaluated the in vivo interaction between the transcriptional intermediary factor 2 (TIF2) coactivator and different GR-ligand complexes. No correlation was found between GR intranuclear distribution, cofactor recruitment and the homodimerization process. Finally, Molecular determinants that support the observed experimental GR LBD-ligand/TIF2 interaction were found by Molecular Dynamics simulation. Conclusions/Significance: The data presented here sustain the idea that in vivo GR homodimerization inside the nucleus can be achieved in a DNA-independent fashion, without ruling out a dependent pathway as well. Moreover, since at least one GR-ligand complex is able to induce homodimer formation while preventing TIF2 coactivator interaction, results suggest that these two events might be independent from each other. Finally, 21-hydroxy-6,19-epoxyprogesterone arises as a selective glucocorticoid with potential pharmacological interest. Taking into account that GR homodimerization and cofactor recruitment are considered essential steps in the receptor activation pathway, results presented here contribute to understand how specific ligands influence GR behavior. © 2010 Presman et al.Fil:Presman, D.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Alvarez, L.D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Levi, V. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Martí, M.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Veleiro, A.S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Burton, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Pecci, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

BDNF polymorphism predicts the rate of decline in skilled task performance and hippocampal volume in healthy individuals

Numerous studies have indicated a link between the presence of polymorphism in brain-derived neurotrophic factor (BDNF) and cognitive and affective disorders. However, only a few have studied these effects longitudinally along with structural changes in the brain. This study was carried out to investigate whether valine-to-methionine substitution at position 66 (val66met) of pro-BDNF could be linked to alterations in the rate of decline in skilled task performance and structural changes in hippocampal volume. Participants consisted of 144 healthy Caucasian pilots (aged 40–69 years) who completed a minimum of 3 consecutive annual visits. Standardized flight simulator score (SFSS) was measured as a reliable and quantifiable indicator for skilled task performance. In addition, a subset of these individuals was assessed for hippocampal volume alterations using magnetic resonance imaging. We found that val66met substitution in BDNF correlated longitudinally with the rate of decline in SFSS. Structurally, age-dependent hippocampal volume changes were also significantly altered by this substitution. Our study suggests that val66met polymorphism in BDNF can be linked to the rate of decline in skilled task performance. Furthermore, this polymorphism could be used as a predictor of the effects of age on the structure of the hippocampus in healthy individuals. Such results have implications for understanding possible disabilities in older adults performing skilled tasks who are at a higher risk for cognitive and affective disorders

Modulation of dendritic spine development and plasticity by BDNF and vesicular trafficking: fundamental roles in neurodevelopmental disorders associated with mental retardation and autism

The process of axonal and dendritic development establishes the synaptic circuitry of the central nervous system (CNS) and is the result of interactions between intrinsic molecular factors and the external environment. One growth factor that has a compelling function in neuronal development is the neurotrophin brain-derived neurotrophic factor (BDNF). BDNF participates in axonal and dendritic differentiation during embryonic stages of neuronal development, as well as in the formation and maturation of dendritic spines during postnatal development. Recent studies have also implicated vesicular trafficking of BDNF via secretory vesicles, and both secretory and endosomal trafficking of vesicles containing synaptic proteins, such as neurotransmitter and neurotrophin receptors, in the regulation of axonal and dendritic differentiation, and in dendritic spine morphogenesis. Several genes that are either mutated or deregulated in neurodevelopmental disorders associated with mental retardation have now been identified, and several mouse models of these disorders have been generated and characterized. Interestingly, abnormalities in dendritic and synaptic structure are consistently observed in human neurodevelopmental disorders associated with mental retardation, and in mouse models of these disorders as well. Abnormalities in dendritic and synaptic differentiation are thought to underlie altered synaptic function and network connectivity, thus contributing to the clinical outcome. Here, we review the roles of BDNF and vesicular trafficking in axonal and dendritic differentiation in the context of dendritic and axonal morphological impairments commonly observed in neurodevelopmental disorders associated with mental retardation

Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays