International School for Advanced Studies

About: International School for Advanced Studies is a education organization based out in Trieste, Friuli-Venezia Giulia, Italy. It is known for research contribution in the topics: Galaxy & Dark matter. The organization has 3751 authors who have published 13433 publications receiving 588454 citations. The organization is also known as: SISSA & Scuola Internazionale Superiore di Studi Avanzati.

The distribution of dark matter in galaxies

Abstract: The distribution of the non-luminous matter in galaxies of different luminosity and Hubble type is much more than a proof of the existence of dark particles governing the structures of the Universe. Here, we will review the complex but well-ordered scenario of the properties of the dark halos also in relation with those of the baryonic components they host. Moreover, we will present a number of tight and unexpected correlations between selected properties of the dark and the luminous matter. Such entanglement evolves across the varying properties of the luminous component and it seems to unequivocally lead to a dark particle able to interact with the Standard Model particles over cosmological times. This review will also focus on whether we need a paradigm shift, from pure collisionless dark particles emerging from "first principles", to particles that we can discover only by looking to how they have designed the structure of the galaxies. \keywords{Dark matter \and Galaxies \and Cosmology \and Elementary particles}

Brain-derived neurotrophic factor (BDNF) induces dendritic targeting of BDNF and tyrosine kinase B mRNAs in hippocampal neurons through a phosphatidylinositol-3 kinase-dependent pathway.

Abstract: This study aims to understand the mechanisms of dendritic targeting of brain-derived neurotrophic factor (BDNF) and tyrosine kinase B (TrkB) mRNAs. We show that brief depolarizations are sufficient to induce accumulation of BDNF and TrkB mRNAs in dendrites of hippocampal neurons. Endogenous BDNF, secreted during the KCl stimulation, contributes significantly to the dendritic accumulation of BDNF–TrkB mRNAs. In the absence of depolarization, 1 min pulses of exogenous BDNF are sufficient to induce dendritic accumulation of BDNF–TrkB mRNAs. After binding to TrkB, BDNF exerts this action by activating a PI-3 kinase-dependent pathway. The accumulation of dendritic mRNA by BDNF is not mediated by BDNF-induced neurotransmitter release. Because most hippocampal neurons coexpress BDNF and TrkB receptors, these results show that the subcellular distribution of BDNF–TrkB mRNAs is under the control of an autocrine–paracrine BDNF–TrkB-dependent loop.

Galactic antiproton spectrum at high energies: Background expectation versus exotic contributions

Abstract: A new generation of upcoming space-based experiments will soon start to probe the spectrum of cosmic-ray antiparticles with an unprecedented accuracy and, in particular, will open up a window to energies much higher than those accessible so far. It is thus timely to carefully investigate the expected antiparticle fluxes at high energies. Here, we perform such an analysis for the case of antiprotons. We consider both standard sources as the collision of other cosmic rays with interstellar matter, as well as exotic contributions from dark matter annihilations in the galactic halo. Up to energies well above 100 GeV, we find that the background flux in antiprotons is almost uniquely determined by the existing low-energy data on various cosmic-ray species; for even higher energies, however, the uncertainties in the parameters of the underlying propagation model eventually become significant. We also show that if the dark matter is composed of particles with masses at the TeV scale, which is naturally expected in extra-dimensional models as well as in certain parameter regions of supersymmetric models, the annihilation flux can become comparable to---or even dominate---the antiproton background at the high energies considered here.

Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks

Abstract: Motivation: Inferring a gene regulatory network exclusively from microarray expression profiles is a difficult but important task. The aim of this work is to compare the predictive power of some of the most popular algorithms in different conditions (like data taken at equilibrium or time courses) and on both synthetic and real microarray data. We are in particular interested in comparing similarity measures both of linear type (like correlations and partial correlations) and of non-linear type (mutual information and conditional mutual information), and in investigating the underdetermined case (less samples than genes). Results: In our simulations we see that all network inference algorithms obtain better performances from data produced with ‘structural’ perturbations, like gene knockouts at steady state, than with any dynamical perturbation. The predictive power of all algorithms is confirmed on a reverse engineering problem from Escherichia coli gene profiling data: the edges of the ‘physical’ network of transcription factor–binding sites are significantly overrepresented among the highest weighting edges of the graph that we infer directly from the data without any structure supervision. Comparing synthetic and in vivo data on the same network graph allows us to give an indication of how much more complex a real transcriptional regulation program is with respect to an artificial model. Availability: Software is freely available at the URL http://people.sissa.it/~altafini/papers/SoBiAl07/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics

Abstract: The use of free-energy landscapes rationalizes a wide range of aspects of protein behavior by providing a clear illustration of the different states accessible to these molecules, as well as of their populations and pathways of interconversion. The determination of the free-energy landscapes of proteins by computational methods is, however, very challenging as it requires an extensive sampling of their conformational spaces. We describe here a technique to achieve this goal with relatively limited computational resources by incorporating nuclear magnetic resonance (NMR) chemical shifts as collective variables in metadynamics simulations. As in this approach the chemical shifts are not used as structural restraints, the resulting free-energy landscapes correspond to the force fields used in the simulations. We illustrate this approach in the case of the third Ig-binding domain of protein G from streptococcal bacteria (GB3). Our calculations reveal the existence of a folding intermediate of GB3 with nonnative structural elements. Furthermore, the availability of the free-energy landscape enables the folding mechanism of GB3 to be elucidated by analyzing the conformational ensembles corresponding to the native, intermediate, and unfolded states, as well as the transition states between them. Taken together, these results show that, by incorporating experimental data as collective variables in metadynamics simulations, it is possible to enhance the sampling efficiency by two or more orders of magnitude with respect to standard molecular dynamics simulations, and thus to estimate free-energy differences among the different states of a protein with a kBT accuracy by generating trajectories of just a few microseconds.