Prediction of drug targets in human pathogens

The identification of new and druggable targets in bacteria is a critical endeavour in pharmaceutical research of novel antibiotics to fight infectious agents. The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. A new model that combines the singular value decomposition technique with biological filters comprised of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of E. coli has been developed to predict potential drug targets in the Enterobacteriaceae family [1]. This model identified 99 potential target proteins amongst the studied bacterial family, exhibiting eight different functions that suggest that the disruption of the activities of these proteins is critical for cells. Out of these candidates, one was selected for target confirmation. To find target modulators, receptor-based pharmacophore hypotheses were built and used in the screening of a virtual library of compounds. Postscreening filters were based on physicochemical and topological similarity to known Gram-negative antibiotics and applied to the retrieved compounds. Screening hits passing all filters were docked into the proteins catalytic groove and 15 of the most promising compounds were purchased from their chemical vendors to be experimentally tested in vitro. To the best of our knowledge, this is the first attempt to rationalize the search of compounds to probe the relevance of this candidate as a new pharmacological target.

Abnormal interactions between context, memory structure, and mood in schizophrenia: an ERP investigation

This study used event-related potentials to examine interactions between mood, sentence context, and semantic memory structure in schizophrenia. Seventeen male chronic schizophrenia and 15 healthy control subjects read sentence pairs after positive, negative, or neutral mood induction. Sentences ended with expected words (EW), within-category violations (WCV), or between-category violations (BCV). Across all moods, patients showed sensitivity to context indexed by reduced N400 to EW relative to both WCV and BCV. However, they did not show sensitivity to the semantic memory structure. N400 abnormalities were particularly enhanced under a negative mood in schizophrenia. These findings suggest abnormal interactions between mood, context processing, and connections within semantic memory in schizophrenia, and a specific role of negative mood in modulating semantic processes in this disease.

Development of high specific strength components, with internal cellular structure, for several applications

Dissertação de mestrado em Engenharia Mecânica

Structural analysis of the timber structure of Ica Cathedral, Peru

Dissertação de mestrado em Structural Analysis of Monuments and Historical Constructions

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Electronic structure and optical signatures of semiconducting transition metal dichalcogenide nanosheets

CONSPECTUS: Two-dimensional (2D) crystals derived from transition metal dichalcogenides (TMDs) are intriguing materials that offer a unique platform to study fundamental physical phenomena as well as to explore development of novel devices. Semiconducting group 6 TMDs such as MoS2 and WSe2 are known for their large optical absorption coefficient and their potential for high efficiency photovoltaics and photodetectors. Monolayer sheets of these compounds are flexible, stretchable, and soft semiconductors with a direct band gap in contrast to their well-known bulk crystals that are rigid and hard indirect gap semiconductors. Recent intense research has been motivated by the distinct electrical, optical, and mechanical properties of these TMD crystals in the ultimate thickness regime. As a semiconductor with a band gap in the visible to near-IR frequencies, these 2D MX2 materials (M = Mo, W; X = S, Se) exhibit distinct excitonic absorption and emission features. In this Account, we discuss how optical spectroscopy of these materials allows investigation of their electronic properties and the relaxation dynamics of excitons. We first discuss the basic electronic structure of 2D TMDs highlighting the key features of the dispersion relation. With the help of theoretical calculations, we further discuss how photoluminescence energy of direct and indirect excitons provide a guide to understanding the evolution of the electronic structure as a function of the number of layers. We also highlight the behavior of the two competing conduction valleys and their role in the optical processes. Intercalation of group 6 TMDs by alkali metals results in the structural phase transformation with corresponding semiconductor-to-metal transition. Monolayer TMDs obtained by intercalation-assisted exfoliation retains the metastable metallic phase. Mild annealing, however, destabilizes the metastable phase and gradually restores the original semiconducting phase. Interestingly, the semiconducting 2H phase, metallic 1T phase, and a charge-density-wave-like 1T' phase can coexist within a single crystalline monolayer sheet. We further discuss the electronic properties of the restacked films of chemically exfoliated MoS2. Finally, we focus on the strong optical absorption and related exciton relaxation in monolayer and bilayer MX2. Monolayer MX2 absorbs as much as 30% of incident photons in the blue region of the visible light despite being atomically thin. This giant absorption is attributed to nesting of the conduction and valence bands, which leads to diversion of optical conductivity. We describe how the relaxation pathway of excitons depends strongly on the excitation energy. Excitation at the band nesting region is of unique significance because it leads to relaxation of electrons and holes with opposite momentum and spontaneous formation of indirect excitons.

Structure dependent resistivity and dielectric characteristics of tantalum oxynitride thin films produced by magnetron sputtering

tThe main purpose of this work is to present and to interpret the change of electrical properties of TaxNyOzthin films, produced by DC reactive magnetron sputtering. Some parameters were varied during deposi-tion: the flow of the reactive gases mixture (N2and O2, with a constant concentration ratio of 17:3); thesubstrate voltage bias (grounded, −50 V or −100 V) and the substrate (glass, (1 0 0) Si or high speed steel).The obtained films exhibit significant differences. The variation of the deposition parameters inducesvariations of the composition, microstructure and morphology. These differences cause variation of theelectrical resistivity essentially correlated with the composition and structural changes. The gradualdecrease of the Ta concentration in the films induces amorphization and causes a raise of the resistivity.The dielectric characteristics of some of the high resistance TaxNyOzfilms were obtained in the sampleswith a capacitor-like design (deposited onto high speed steel, with gold pads deposited on the dielectricTaxNyOzfilms). Some of these films exhibited dielectric constant values higher than those reported forother tantalum based dielectric films.

Composition and structure variation for magnetron sputtered tantalum oxynitride thin films, as function of deposition parameters

Tantalum oxynitride thin films were produced by magnetron sputtering. The films were deposited usinga pure Ta target and a working atmosphere with a constant N2/O2ratio. The choice of this constant ratiolimits the study concerning the influence of each reactive gas, but allows a deeper understanding of theaspects related to the affinity of Ta to the non-metallic elements and it is economically advantageous.This work begins by analysing the data obtained directly from the film deposition stage, followed bythe analysis of the morphology, composition and structure. For a better understanding regarding theinfluence of the deposition parameters, the analyses are presented by using the following criterion: thefilms were divided into two sets, one of them produced with grounded substrate holder and the otherwith a polarization of −50 V. Each one of these sets was produced with different partial pressure of thereactive gases P(N2+ O2). All the films exhibited a O/N ratio higher than the N/O ratio in the depositionchamber atmosphere. In the case of the films produced with grounded substrate holder, a strong increaseof the O content is observed, associated to the strong decrease of the N content, when P(N2+ O2) is higherthan 0.13 Pa. The higher Ta affinity for O strongly influences the structural evolution of the films. Grazingincidence X-ray diffraction showed that the lower partial pressure films were crystalline, while X-rayreflectivity studies found out that the density of the films depended on the deposition conditions: thehigher the gas pressure, the lower the density. Firstly, a dominant -Ta structure is observed, for lowP(N2+ O2); secondly a fcc-Ta(N,O) structure, for intermediate P(N2+ O2); thirdly, the films are amorphousfor the highest partial pressures. The comparison of the characteristics of both sets of produced TaNxOyfilms are explained, with detail, in the text.

Singularities of the dynamical structure factors of the spin-1/2 XXX chain at finite magnetic field

We study the longitudinal and transverse spin dynamical structure factors of the spin-1/2 XXX chain at finite magnetic field h, focusing in particular on the singularities at excitation energies in the vicinity of the lower thresholds. While the static properties of the model can be studied within a Fermi-liquid like description in terms of pseudoparticles, our derivation of the dynamical properties relies on the introduction of a form of the ‘pseudofermion dynamical theory’ (PDT) of the 1D Hubbard model suitably modified for the spin-only XXX chain and other models with two pseudoparticle Fermi points. Specifically, we derive the exact momentum and spin-density dependences of the exponents ζτ(k) controlling the singularities for both the longitudinal  and transverse (τ = t) dynamical structure factors for the whole momentum range  , in the thermodynamic limit. This requires the numerical solution of the integral equations that define the phase shifts in these exponents expressions. We discuss the relation to neutron scattering and suggest new experiments on spin-chain compounds using a carefully oriented crystal to test our predictions.

Food Structure Design: Innovation in food structure-properties relationships

This Special Issue gathers selected contributions from the 1st Congress on Food Structure Design, covering most of the topics described above.

Nonlinear behavior of gelatin networks reveals a hierarchical structure

We investigate the strain hardening behavior of various gelatin networks-namely physical gelatin gel, chemically cross-linked gelatin gel, and a hybrid gel made of a combination of the former two-under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillations shear protocols. Further, the internal structures of physical gelatin gels and chemically cross-linked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically cross-linked network whereas, in the physical gelatin gels, a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as the correlation length (ξ), the cross-sectional polymer chain radius (Rc) and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physical and chemically cross-linked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized nonlinear elastic theory that has been used to fit the stress-strain curves. The chemical cross-linking that generates coils and aggregates hinders the free stretching of the triple helix bundles in the physical gels.

Evaluation of the performance of steel fibre reinforced self-compacting concrete in elevated slab systems; from the material to the structure

Degree of Doctor of Philosophy of Structural/Civil Engineering

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Poly(ester-urethane) scaffolds: effect of structure on properties and osteogenic activity of stem cells

The present study aimed to investigate the effect of structure (design and porosity) on the matrix stiffness and osteogenic activity of stem cells cultured on poly(ester-urethane) (PEU) scaffolds. Different three-dimensional (3D) forms of scaffold were prepared from lysine-based PEU using traditional salt-leaching and advanced bioplotting techniques. The resulting scaffolds were characterized by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), mercury porosimetry and mechanical testing. The scaffolds had various pore sizes with different designs, and all were thermally stable up to 300â °C. In vitrotests, carried out using rat bone marrow stem cells (BMSCs) for bone tissue engineering, demonstrated better viability and higher cell proliferation on bioplotted scaffolds compared to salt-leached ones, most probably due to their larger and interconnected pores and stiffer nature, as shown by higher compressive moduli, which were measured by compression testing. Similarly, SEM, von Kossa staining and EDX analyses indicated higher amounts of calcium deposition on bioplotted scaffolds during cell culture. It was concluded that the design with larger interconnected porosity and stiffness has an effect on the osteogenic activity of the stem cells.

Exploring the factor structure of neurocognitive measures in older individuals

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the "best fit" model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.