Calcium is essential for the structural integrity of the cysteine-rich, ligand-binding repeat of the low-density lipoprotein receptor

Seven cysteine-rich repeats form the ligand-binding region of the low-density lipoprotein (LDL) receptor. Each of these repeats is assumed to bind a calcium ion, which is needed for association of the receptor with its ligands, LDL and beta-VLDL. The effects of metal ions on the folding of the reduced N-terminal cysteine-rich repeat have been examined by using reverse-phase high-performance liquid chromatography to follow the formation of fully oxidized isomers with different disulfide connectivities. in the absence of calcium many of the 15 possible isomers formed on oxidation, whereas in its presence the predominant product at equilibrium had the native disulfide bond connectivities. Other metals were far less effective at directing disulfide bond formation: Mn2+ partly mimicked the action of Ca2+, but Ba2+, Sr2+, and Mg2+ had little effect. This metal-ion specificity was also observed in two-dimensional H-1 NMR spectral studies: only Ca2+ induced the native three-dimensional fold. The two paramagnetic ions, Gd3+ and Mn2+, and Cd2+ did not promote adoption of a well-defined structure, and the two paramagnetic ions did not displace calcium ions. The location of calcium ion binding sites in the repeat was also explored by NMR spectroscopy. The absence of chemical shift changes for the side chain proton resonances of Asp26, Asp36, and Glu37 from pH 3.9 to 6.8 in the presence of calcium ions and their proximal location in the NMR structures implicated these side chains as calcium ligands. Deuterium exchange NMR experiments also revealed a network of hydrogen bonds that stabilizes the putative calcium-binding loop.

Comparative analysis of dendritic cell density and total number in commonly transplanted organs: morphometric estimation in normal mice

Dendritic cells (DC) are considered to be the major cell type responsible for induction of primary immune responses. While they have been shown to play a critical role in eliciting allosensitization via the direct pathway, there is evidence that maturational and/or activational heterogeneity between DC in different donor organs may be crucial to allograft outcome. Despite such an important perceived role for DC, no accurate estimates of their number in commonly transplanted organs have been reported. Therefore, leukocytes and DC were visualized and enumerated in cryostat sections of normal mouse (C57BL/10, B10.BR, C3H) liver, heart, kidney and pancreas by immunohistochemistry (CD45 and MHC class II staining, respectively). Total immunopositive cell number and MHC class II+ cell density (C57BL/10 mice only) were estimated using established morphometric techniques - the fractionator and disector principles, respectively. Liver contained considerably more leukocytes (similar to 5-20 x 10(6)) and DC (similar to 1-3 x 10(6)) than the other organs examined (pancreas: similar to 0.6 x 10(6) and similar to 0.35 x 10(6): heart: similar to 0.8 x 10(6) and similar to 0.4 x 10(6); kidney similar to 1.2 x 10(6) and 0.65 x 10(6), respectively). In liver, DC comprised a lower proportion of all leukocytes (similar to 15-25%) than in the other parenchymal organs examined (similar to 40-60%). Comparatively, DC density in C57BL/10 mice was heart > kidney > pancreas much greater than liver (similar to 6.6 x 10(6), 5 x 10(6), 4.5 x 10(6) and 1.1 x 10(6) cells/cm(3), respectively). When compared to previously published data on allograft survival, the results indicate that the absolute number of MHC class II+ DC present in a donor organ is a poor predictor of graft outcome. Survival of solid organ allografts is more closely related to the density of the donor DC network within the graft. (C) 2000 Elsevier Science B.V. All rights reserved.

Use of liquid phase adsorption for characterizing pore network connectivity in activated carbon

A simple percolation theory-based method for determination of the pore network connectivity using liquid phase adsorption isotherm data combined with a density functional theory (DFT)-based pore size distribution is presented in this article. The liquid phase adsorption experiments have been performed using eight different esters as adsorbates and microporous-mesoporous activated carbons Filtrasorb-400, Norit ROW 0.8 and Norit ROX 0.8 as adsorbents. The density functional theory (DFT)-based pore size distributions of the carbons were obtained using DFT analysis of argon adsorption data. The mean micropore network coordination numbers, Z, of the carbons were determined based on DR characteristic plots and fitted saturation capacities using percolation theory. Based on this method, the critical molecular sizes of the model compounds used in this study were also obtained. The incorporation of percolation concepts in the prediction of multicomponent adsorption equilibria is also investigated, and found to improve the performance of the ideal adsorbed solution theory (IAST) model for the large molecules utilized in this study. (C) 2002 Elsevier Science B.V. All rights reserved.

Re-entrant phase behaviour of network fluids: A patchy particle model with temperature-dependent valence

We study a model consisting of particles with dissimilar bonding sites ("patches"), which exhibits self-assembly into chains connected by Y-junctions, and investigate its phase behaviour by both simulations and theory. We show that, as the energy cost epsilon(j) of forming Y-junctions increases, the extent of the liquid-vapour coexistence region at lower temperatures and densities is reduced. The phase diagram thus acquires a characteristic "pinched" shape in which the liquid branch density decreases as the temperature is lowered. To our knowledge, this is the first model in which the predicted topological phase transition between a fluid composed of short chains and a fluid rich in Y-junctions is actually observed. Above a certain threshold for epsilon(j), condensation ceases to exist because the entropy gain of forming Y-junctions can no longer offset their energy cost. We also show that the properties of these phase diagrams can be understood in terms of a temperature-dependent effective valence of the patchy particles. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3605703]

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Properties of patchy colloidal particles close to a surface: a Monte Carlo and density functional study

We investigate the behavior of a patchy particle model close to a hard-wall via Monte Carlo simulation and density functional theory (DFT). Two DFT approaches, based on the homogeneous and inhomogeneous versions of Wertheim's first order perturbation theory for the association free energy are used. We evaluate, by simulation and theory, the equilibrium bulk phase diagram of the fluid and analyze the surface properties for two isochores, one of which is close to the liquid side of the gas-liquid coexistence curve. We find that the density profile near the wall crosses over from a typical high-temperature adsorption profile to a low-temperature desorption one, for the isochore close to coexistence. We relate this behavior to the properties of the bulk network liquid and find that the theoretical descriptions are reasonably accurate in this regime. At very low temperatures, however, an almost fully bonded network is formed, and the simulations reveal a second adsorption regime which is not captured by DFT. We trace this failure to the neglect of orientational correlations of the particles, which are found to exhibit surface induced orientational order in this regime.

Impact of europeanization in policy network governance pattrons

Comunicação apresentada na 30th Sunbelt Social Networks Conference, em Riva del Garda, Itália, a 3 de Julho de 2010.

XDense: A Dense Grid Sensor Network for Distributed Feature Extraction

XXXIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2015), III Workshop de Comunicação em Sistemas Embarcados Críticos. Vitória, Brasil.

Two stage abe fermentation with in situ pervaporation and high cell density

Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

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.

A numerical solver for a nonlinear Fokker-Planck equation representation of neuronal network dynamics

To describe the collective behavior of large ensembles of neurons in neuronal network, a kinetic theory description was developed in [13, 12], where a macroscopic representation of the network dynamics was directly derived from the microscopic dynamics of individual neurons, which are modeled by conductance-based, linear, integrate-and-fire point neurons. A diffusion approximation then led to a nonlinear Fokker-Planck equation for the probability density function of neuronal membrane potentials and synaptic conductances. In this work, we propose a deterministic numerical scheme for a Fokker-Planck model of an excitatory-only network. Our numerical solver allows us to obtain the time evolution of probability distribution functions, and thus, the evolution of all possible macroscopic quantities that are given by suitable moments of the probability density function. We show that this deterministic scheme is capable of capturing the bistability of stationary states observed in Monte Carlo simulations. Moreover, the transient behavior of the firing rates computed from the Fokker-Planck equation is analyzed in this bistable situation, where a bifurcation scenario, of asynchronous convergence towards stationary states, periodic synchronous solutions or damped oscillatory convergence towards stationary states, can be uncovered by increasing the strength of the excitatory coupling. Finally, the computation of moments of the probability distribution allows us to validate the applicability of a moment closure assumption used in [13] to further simplify the kinetic theory.

Technology, business models and network structure in the airline industry

Network airlines have been increasingly focusing their operations on hub airports through the exploitation of connecting traffic, allowing them to take advantage of economies of traffic density, which are unequivocal in the airline industry. Less attention has been devoted to airlines? decisions on point-to-point thin routes, which could be served using different aircraft technologies and different business models. This paper examines, both theoretically and empirically, the impact on airlines ?networks of the two major innovations in the airline industry in the last two decades: the regional jet technology and the low-cost business model. We show that, under certain circumstances, direct services on point-to-point thin routes can be viable and thus airlines may be interested in deviating passengers out of the hub.

Technology, business models and network structure in the airline industry

Network airlines have been increasingly focusing their operations on hub airports through the exploitation of connecting traffic, allowing them to take advantage of economies of traffic density, which are unequivocal in the airline industry. Less attention has been devoted to airlines' decisions on point-to-point thin routes, which could be served using different aircraft technologies and different business models. This paper examines, both theoretically and empirically, the impact on airlines' networks of the two major innovations in the airline industry in the last two decades: the regional jet technology and the low-cost business model. We show that, under certain circumstances, direct services on point-to-point thin routes can be viable and thus airlines may be interested in deviating passengers out of the hub. Keywords: regional jet technology; low-cost business model; point-to-point network; hub-and-spoke network JEL Classi…fication Numbers: L13; L2; L93

The Swiss Early Psychosis Project SWEPP: a national network.

Aim: The study aims to describe the activities of the Swiss Early Psychosis Project (SWEPP) which was founded in 1999 as a national network to further and disseminate knowledge on early psychosis (EP) and to enhance collaboration between healthcare groups. Methods: The present paper is a detailed account of the initiation and the development of the Swiss network. We describe all activities such as the several educational campaigns that were addressed to primary and secondary care groups since the early days. We also provide an overview of the current status of EP services throughout the country. Results: Today, most regions in Switzerland provide specialized EP inpatient and/or outpatient services with a clinical or combined clinical research approach that targets at-risk and/or first-episode populations. Some more recently initiated EP services have been launched as collaborative models between several local or regional psychiatric services. Conclusions: The increasing number of EP services and experts in Switzerland may mirror the catalyzing contribution of the Swiss Early Psychosis Project in this important field of health care. The country's small size and the increasing density of specialized services provide excellent bases for larger-scale networking activities in the future, both in clinical and research areas.