Heat kernel smoothing via Laplace-Beltrami eigenfunctions and its application to subcortical structure modeling

We present a new subcortical structure shape modeling framework using heat kernel smoothing constructed with the Laplace-Beltrami eigenfunctions. The cotan discretization is used to numerically obtain the eigenfunctions of the Laplace-Beltrami operator along the surface of subcortical structures of the brain. The eigenfunctions are then used to construct the heat kernel and used in smoothing out measurements noise along the surface. The proposed framework is applied in investigating the influence of age (38-79 years) and gender on amygdala and hippocampus shape. We detected a significant age effect on hippocampus in accordance with the previous studies. In addition, we also detected a significant gender effect on amygdala. Since we did not find any such differences in the traditional volumetric methods, our results demonstrate the benefit of the current framework over traditional volumetric methods.

Large-scale protein structure modeling of the Saccharomyces cerevisiae genome

The function of a protein generally is determined by its three-dimensional (3D) structure. Thus, it would be useful to know the 3D structure of the thousands of protein sequences that are emerging from the many genome projects. To this end, fold assignment, comparative protein structure modeling, and model evaluation were automated completely. As an illustration, the method was applied to the proteins in the Saccharomyces cerevisiae (baker’s yeast) genome. It resulted in all-atom 3D models for substantial segments of 1,071 (17%) of the yeast proteins, only 40 of which have had their 3D structure determined experimentally. Of the 1,071 modeled yeast proteins, 236 were related clearly to a protein of known structure for the first time; 41 of these previously have not been characterized at all.

Structure of luminescent mono and di-urethanesil nanocomposites doped with Eu3+ ions

A structure modeling of two families of sol-gel derived Eu3+-doped organic/inorganic hybrids based on the results of small-angle X-ray scattering experiments is reported. The materials are composed of poly(oxyethylene) chains grafted at one or both ends to siloxane groups and are called mono- and di-urethanesils, respectively. A theoretical function corresponding to a two-level hierarchical structure model fits well the experimental Scattering curves. The first level corresponds to small siloxane clusters embedded in a polymeric matrix. The secondary level is associated to the existence of siloxane cluster rich domains surrounded by a cluster-depleted polymeric matrix. Results show that increasing europium doping favors the growth of the secondary domains. (C) 2002 Elsevier B.V. B.V. All rights reserved.

X-ray structure of the human hyperplastic discs protein: An ortholog of the C-terminal domain of poly(A)-binding protein

The poly(A)-binding protein (PABP) recognizes the 3′ mRNA poly(A) tail and plays an essential role in eukaryotic translation initiation and mRNA stabilization/degradation. PABP is a modular protein, with four N-terminal RNA-binding domains and an extensive C terminus. The C-terminal region of PABP is essential for normal growth in yeast and has been implicated in mediating PABP homo-oligomerization and protein–protein interactions. A small, proteolytically stable, highly conserved domain has been identified within this C-terminal segment. Remarkably, this domain is also present in the hyperplastic discs protein (HYD) family of ubiquitin ligases. To better understand the function of this conserved region, an x-ray structure of the PABP-like segment of the human HYD protein has been determined at 1.04-Å resolution. The conserved domain adopts a novel fold resembling a right-handed supercoil of four α-helices. Sequence profile searches and comparative protein structure modeling identified a small ORF from the Arabidopsis thaliana genome that encodes a structurally similar but distantly related PABP/HYD domain. Phylogenetic analysis of the experimentally determined (HYD) and homology modeled (PABP) protein surfaces revealed a conserved feature that may be responsible for binding to a PABP interacting protein, Paip1, and other shared interaction partners.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Projecto de fundações e estrutura de um edifício

Trabalho de Projecto para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Estruturas

Optimization of structures modeled with a Meshfree approach

Radial basis functions are being used in different scientific areas in order to reproduce the geometrical modeling of an object/structure, as well as to predict its behavior. Due to its characteristics, these functions are well suited for meshfree modeling of physical quantities, which for instances can be associated to the data sets of 3D laser scanning point clouds. In the present work the geometry of a structure is modeled by using multiquadric radial basis functions, and its configuration is further optimized in order to obtain better performances concerning to its static and dynamic behavior. For this purpose the authors consider the particle swarm optimization technique. A set of case studies is presented to illustrate the adequacy of the meshfree model used, as well as its link to particle swarm optimization technique. © 2014 IEEE.

Characterisation of the putative effector interaction site of the regulatory HbpR protein from Pseudomonas azelaica by site-directed mutagenesis.

Bacterial transcription activators of the XylR/DmpR subfamily exert their expression control via σ(54)-dependent RNA polymerase upon stimulation by a chemical effector, typically an aromatic compound. Where the chemical effector interacts with the transcription regulator protein to achieve activation is still largely unknown. Here we focus on the HbpR protein from Pseudomonas azelaica, which is a member of the XylR/DmpR subfamily and responds to biaromatic effectors such as 2-hydroxybiphenyl. We use protein structure modeling to predict folding of the effector recognition domain of HbpR and molecular docking to identify the region where 2-hydroxybiphenyl may interact with HbpR. A large number of site-directed HbpR mutants of residues in- and outside the predicted interaction area was created and their potential to induce reporter gene expression in Escherichia coli from the cognate P(C) promoter upon activation with 2-hydroxybiphenyl was studied. Mutant proteins were purified to study their conformation. Critical residues for effector stimulation indeed grouped near the predicted area, some of which are conserved among XylR/DmpR subfamily members in spite of displaying different effector specificities. This suggests that they are important for the process of effector activation, but not necessarily for effector specificity recognition.

Developing of Service Platform Thinking in Modern Electricity Distribution Business

Palvelukehitystoiminta sitoo huomattavan määrän resursseja ja on pitkäkestoista toimintaa. Innovatiivisuuteen tähtäämällä ja systemaattisella tuotekehitystyöllä yritys parantaa jatkuvuutta omassa liiketoiminnassaan. Alusta-ajattelu tuo uuden ulottuvuuden tuotteiden ja palveluiden kehitykseen. Alustan kehittäminen tukemaan tuote- ja palvelukehitystoimintaa ja yksinkertaistamaan tuote/palvelurakenteita antaa yrityksissä lisäpotentiaalia esimerkiksi lyhentyneiden kehitysaikojen, paremman kompleksisuuden hallinnan ja kustannustehokkuuden nousun myötä. Toimintojen tehostuminen yritystasolla saa aikaan mahdollisuuksien lisääntymisen nykyisillä liiketoimintasektoreilla. Palvelualustan kehityksellä päästään palvelurakenteen mallintamisen kautta parempaan liiketoiminnan hallitsemiseen ja systemaattisempaan tuotekehityksen läpivientiin. Palvelualustan yhtenä tärkeimpänä hyötynä on, että palvelun rakenteellisuus saadaan kuvattua alustaan. Lisäksi on tärkeää määritellä vastuutukset alustan kehityksessä, sekä pystyä mallintamaan informaation kulku (rajapinnat) prosesseissa.

AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis

Affiliation: Département de biochimie, Faculté de médecine, Université de Montréal

Modelagem molecular da interação entre a proteína de fusão do vírus sincicial respiratório humano e inibidores da ação viral. -

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise in silico de um novo “cluster” de PKS II Metagenômico

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

SAG2A protein from Toxoplasma gondii interacts with both innate and adaptive immune compartments of infected hosts

Abstract Background Toxoplasma gondii is an intracellular parasite that causes relevant clinical disease in humans and animals. Several studies have been performed in order to understand the interactions between proteins of the parasite and host cells. SAG2A is a 22 kDa protein that is mainly found in the surface of tachyzoites. In the present work, our aim was to correlate the predicted three-dimensional structure of this protein with the immune system of infected hosts. Methods To accomplish our goals, we performed in silico analysis of the amino acid sequence of SAG2A, correlating the predictions with in vitro stimulation of antigen presenting cells and serological assays. Results Structure modeling predicts that SAG2A protein possesses an unfolded C-terminal end, which varies its conformation within distinct strain types of T. gondii. This structure within the protein shelters a known B-cell immunodominant epitope, which presents low identity with its closest phyllogenetically related protein, an orthologue predicted in Neospora caninum. In agreement with the in silico observations, sera of known T. gondii infected mice and goats recognized recombinant SAG2A, whereas no serological cross-reactivity was observed with samples from N. caninum animals. Additionally, the C-terminal end of the protein was able to down-modulate pro-inflammatory responses of activated macrophages and dendritic cells. Conclusions Altogether, we demonstrate herein that recombinant SAG2A protein from T. gondii is immunologically relevant in the host-parasite interface and may be targeted in therapeutic and diagnostic procedures designed against the infection.

Post-translational tyrosine nitration of eosinophil granule toxins mediated by eosinophil peroxidase

Nitration of tyrosine residues has been observed during various acute and chronic inflammatory diseases. However, the mechanism of tyrosine nitration and the nature of the proteins that become tyrosine nitrated during inflammation remain unclear. Here we show that eosinophils but not other cell types including neutrophils contain nitrotyrosine-positive proteins in specific granules. Furthermore, we demonstrate that the human eosinophil toxins, eosinophil peroxidase (EPO), major basic protein, eosinophil-derived neurotoxin (EDN) and eosinophil cationic protein (ECP), and the respective murine toxins, are post-translationally modified by nitration at tyrosine residues during cell maturation. High resolution affinity-mass spectrometry identified specific single nitration sites at Tyr349 in EPO and Tyr33 in both ECP and EDN. ECP and EDN crystal structures revealed and EPO structure modeling suggested that the nitrated tyrosine residues in the toxins are surface exposed. Studies in EPO(-/-), gp91phox(-/-), and NOS(-/-) mice revealed that tyrosine nitration of these toxins is mediated by EPO in the presence of hydrogen peroxide and minute amounts of NOx. Tyrosine nitration of eosinophil granule toxins occurs during maturation of eosinophils, independent of inflammation. These results provide evidence that post-translational tyrosine nitration is unique to eosinophils.