Regulation of the immune response by macrophage migration inhibitory factor: biological and structural features.

The classical T cell cytokine macrophage migration inhibitory factor (MIF) has reemerged recently as a critical mediator of the host immune and stress response. MIF has been found to be a mediator of several diseases including gram-negative septic shock and delayed-type hypersensitivity reactions. Its immunological functions include the modulation of the host macrophage and T and B cell response. In contrast to other known cytokines, MIF production is induced rather than suppressed by glucocorticoids, and MIF has been found to override the immunosuppressive effects of glucocorticoids. Recently, elucidation of the three-dimensional structure of MIF revealed that MIF has a novel, unique cytokine structure. Here the biological role of MIF is reviewed in view of its distinct immunological and structural properties.

Micro-patterning of biomolecules on polymer substrates.

UV−excimer laser photoablation was used, in combination with surface blocking techniques, to pattern proteins on the surfaces of polyimide and poly(ethylene terephthalate). This technique involves physical adsorption of avidin through laser-defined openings in low-temperature laminates or adsorbed protein blocking layers. Visualization of biomolecular patterns were monitored using avidin and fluorescein-labeled biotin as a model receptor−ligand couple. Adsorbed proteins could be shown to bind to UV-laser-treated polymer surfaces up to three times higher than on commercially available polymers. UV-laser photoablation was also used for the generation of three-dimensional structure, which leads to the possibility of biomolecule patterning within polymer-based microanalytical systems. The simplicity and easy handling of the described technique facilitate its application in microdiagnostic devices.

Abnormal balance between proliferation and apoptotic cell death in fibroblasts derived from keloid lesions.

A new culture model was developed to study the role of proliferation and apoptosis in the etiology of keloids. Fibroblasts were isolated from the superficial, central, and basal regions of six different keloid lesions by using Dulbecco's Modified Eagle Medium containing 10% fetal calf serum as a culture medium. The growth behavior of each fibroblast fraction was examined in short-term and long-term cultures, and the percentage of apoptotic cells was assessed by in situ end labeling of fragmented DNA. The fibroblasts obtained from the superficial and basal regions of keloid tissue showed population doubling times and saturation densities that were similar to those of age-matched normal fibroblasts. In contrast, the fibroblasts from the center of the keloid lesions showed significantly reduced doubling times (25.9 +/- 6.3 hours versus 43.5 +/- 6.3 hours for normal fibroblasts) and reached higher cell densities. In long-term culture, central keloid fibroblasts formed a stratified three-dimensional structure, contracted the self-produced extracellular matrix, and gave rise to nodular cell aggregates, mimicking the formation of keloid tissue. Apoptotic cells were detected in both normal and keloid-derived fibroblasts, but their numbers were twofold higher in normal cells compared with all keloid fibroblasts. To examine whether apoptosis mediates the therapeutic effect of ionizing radiation on keloids, the cells were exposed to gamma rays at a dose of 8 Gy. Under these conditions, a twofold increase in the population of apoptotic cells was detected. These results indicate that the balance between proliferation and apoptosis is impaired in keloid fibroblasts, which could be responsible for the formation of keloid tumors. The results also suggest that keloids contain at least two different fibroblast fractions that vary in growth behavior and extracellular matrix metabolism.

Dpp signaling activity requires Pentagone to scale with tissue size in the growing Drosophila wing imaginal disc.

The wing of the fruit fly, Drosophila melanogaster, with its simple, two-dimensional structure, is a model organ well suited for a systems biology approach. The wing arises from an epithelial sac referred to as the wing imaginal disc, which undergoes a phase of massive growth and concomitant patterning during larval stages. The Decapentaplegic (Dpp) morphogen plays a central role in wing formation with its ability to co-coordinately regulate patterning and growth. Here, we asked whether the Dpp signaling activity scales, i.e. expands proportionally, with the growing wing imaginal disc. Using new methods for spatial and temporal quantification of Dpp activity and its scaling properties, we found that the Dpp response scales with the size of the growing tissue. Notably, scaling is not perfect at all positions in the field and the scaling of target gene domains is ensured specifically where they define vein positions. We also found that the target gene domains are not defined at constant concentration thresholds of the downstream Dpp activity gradients P-Mad and Brinker. Most interestingly, Pentagone, an important secreted feedback regulator of the pathway, plays a central role in scaling and acts as an expander of the Dpp gradient during disc growth.

Putting Public Service Motivation into Context. A Balance Between Universalism and Particularism

Research on Public Service Motivation (PSM) has increased enormously in the last 20 years. Besides the analysis of the antecedents of PSM and its impact on organizations and individuals, many open questions about the nature of PSM itself still remain. This article argues that the theoretical construct of PSM should be contextualized by integrating the political and administrative contexts of public servants when investigating their specific attitudes towards working in a public environment. It also challenges the efficacy of the classic four-dimensional structure of PSM when it is applied to a specific context. The findings of a confirmatory factor analysis from a dataset of 3754 employees of 279 Swiss municipalities support the appropriateness of contextualizing parts of the PSM construct. They also support the addition of an extra dimension called, according to previous research, Swiss democratic governance. With regard to our results, there is a need for further PSM research to set a definite measure of PSM, particularly in regard to the international diffusion of empirical research on PSM.Points for practitionersThis study shows that public service motivation is a relevant construct for practitioners and may be used to better assess whether public agents are motivated by values or not. Nevertheless, it stresses also that the measurement of PSM must be adapted to the institutional context as well. Public managers interested in understanding better the degree to which their employees are motivated by public values must be aware that the measurement of this PSM construct has to be contextualized. In other words, PSM is also a function of the institutional environment in which organizations operate.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

A combined computational and functional approach identifies new residues involved in pH-dependent gating of ASIC1a.

Acid-sensing ion channels (ASICs) are key receptors for extracellular protons. These neuronal nonvoltage-gated Na(+) channels are involved in learning, the expression of fear, neurodegeneration after ischemia, and pain sensation. We have applied a systematic approach to identify potential pH sensors in ASIC1a and to elucidate the mechanisms by which pH variations govern ASIC gating. We first calculated the pK(a) value of all extracellular His, Glu, and Asp residues using a Poisson-Boltzmann continuum approach, based on the ASIC three-dimensional structure, to identify candidate pH-sensing residues. The role of these residues was then assessed by site-directed mutagenesis and chemical modification, combined with functional analysis. The localization of putative pH-sensing residues suggests that pH changes control ASIC gating by protonation/deprotonation of many residues per subunit in different channel domains. Analysis of the function of residues in the palm domain close to the central vertical axis of the channel allowed for prediction of conformational changes of this region during gating. Our study provides a basis for the intrinsic ASIC pH dependence and describes an approach that can also be applied to the investigation of the mechanisms of the pH dependence of other proteins.

Aminoterminal amphipathic α-helix AH1 of hepatitis C virus nonstructural protein 4B possesses a dual role in RNA replication and virus production.

Nonstructural protein 4B (NS4B) is a key organizer of hepatitis C virus (HCV) replication complex formation. In concert with other nonstructural proteins, it induces a specific membrane rearrangement, designated as membranous web, which serves as a scaffold for the HCV replicase. The N-terminal part of NS4B comprises a predicted and a structurally resolved amphipathic α-helix, designated as AH1 and AH2, respectively. Here, we report a detailed structure-function analysis of NS4B AH1. Circular dichroism and nuclear magnetic resonance structural analyses revealed that AH1 folds into an amphipathic α-helix extending from NS4B amino acid 4 to 32, with positively charged residues flanking the helix. These residues are conserved among hepaciviruses. Mutagenesis and selection of pseudorevertants revealed an important role of these residues in RNA replication by affecting the biogenesis of double-membrane vesicles making up the membranous web. Moreover, alanine substitution of conserved acidic residues on the hydrophilic side of the helix reduced infectivity without significantly affecting RNA replication, indicating that AH1 is also involved in virus production. Selective membrane permeabilization and immunofluorescence microscopy analyses of a functional replicon harboring an epitope tag between NS4B AH1 and AH2 revealed a dual membrane topology of the N-terminal part of NS4B during HCV RNA replication. Luminal translocation was unaffected by the mutations introduced into AH1, but was abrogated by mutations introduced into AH2. In conclusion, our study reports the three-dimensional structure of AH1 from HCV NS4B, and highlights the importance of positively charged amino acid residues flanking this amphipathic α-helix in membranous web formation and RNA replication. In addition, we demonstrate that AH1 possesses a dual role in RNA replication and virus production, potentially governed by different topologies of the N-terminal part of NS4B.

Glial hyaluronate-binding protein expression in aggregating brain cell cultures.

We analyzed the expression of glial hyaluronate-binding protein (GHAP), an integral component of the extracellular matrix, in aggregating brain cell cultures of fetal rat telencephalon using immunofluorescence. GHAP immunoreactivity appeared after 1 week in culture, simultaneous with the first deposits of myelin basic protein, and showed a development-dependent increase. Comparison of glia-enriched and neuron-enriched cultures showed that only glial cells express GHAP. Three peptide growth factors, epidermal growth factor, fibroblast growth factor and platelet-derived growth factor, which are known to stimulate the differentiation of glial cells, modulated the deposit of GHAP immunoreactivity. The 3-dimensional structure of aggregate cultures promoted GHAP deposition, suggesting that cell-cell interactions are required for extracellular matrix formation. Furthermore GHAP production seemed to depend on the developmental stage of the glial cells.

Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.

Artificial evolution on network structures : how time and space influence dynamics

Abstract The main objective of this work is to show how the choice of the temporal dimension and of the spatial structure of the population influences an artificial evolutionary process. In the field of Artificial Evolution we can observe a common trend in synchronously evolv¬ing panmictic populations, i.e., populations in which any individual can be recombined with any other individual. Already in the '90s, the works of Spiessens and Manderick, Sarma and De Jong, and Gorges-Schleuter have pointed out that, if a population is struc¬tured according to a mono- or bi-dimensional regular lattice, the evolutionary process shows a different dynamic with respect to the panmictic case. In particular, Sarma and De Jong have studied the selection pressure (i.e., the diffusion of a best individual when the only selection operator is active) induced by a regular bi-dimensional structure of the population, proposing a logistic modeling of the selection pressure curves. This model supposes that the diffusion of a best individual in a population follows an exponential law. We show that such a model is inadequate to describe the process, since the growth speed must be quadratic or sub-quadratic in the case of a bi-dimensional regular lattice. New linear and sub-quadratic models are proposed for modeling the selection pressure curves in, respectively, mono- and bi-dimensional regu¬lar structures. These models are extended to describe the process when asynchronous evolutions are employed. Different dynamics of the populations imply different search strategies of the resulting algorithm, when the evolutionary process is used to solve optimisation problems. A benchmark of both discrete and continuous test problems is used to study the search characteristics of the different topologies and updates of the populations. In the last decade, the pioneering studies of Watts and Strogatz have shown that most real networks, both in the biological and sociological worlds as well as in man-made structures, have mathematical properties that set them apart from regular and random structures. In particular, they introduced the concepts of small-world graphs, and they showed that this new family of structures has interesting computing capabilities. Populations structured according to these new topologies are proposed, and their evolutionary dynamics are studied and modeled. We also propose asynchronous evolutions for these structures, and the resulting evolutionary behaviors are investigated. Many man-made networks have grown, and are still growing incrementally, and explanations have been proposed for their actual shape, such as Albert and Barabasi's preferential attachment growth rule. However, many actual networks seem to have undergone some kind of Darwinian variation and selection. Thus, how these networks might have come to be selected is an interesting yet unanswered question. In the last part of this work, we show how a simple evolutionary algorithm can enable the emrgence o these kinds of structures for two prototypical problems of the automata networks world, the majority classification and the synchronisation problems. Synopsis L'objectif principal de ce travail est de montrer l'influence du choix de la dimension temporelle et de la structure spatiale d'une population sur un processus évolutionnaire artificiel. Dans le domaine de l'Evolution Artificielle on peut observer une tendence à évoluer d'une façon synchrone des populations panmictiques, où chaque individu peut être récombiné avec tout autre individu dans la population. Déjà dans les année '90, Spiessens et Manderick, Sarma et De Jong, et Gorges-Schleuter ont observé que, si une population possède une structure régulière mono- ou bi-dimensionnelle, le processus évolutionnaire montre une dynamique différente de celle d'une population panmictique. En particulier, Sarma et De Jong ont étudié la pression de sélection (c-à-d la diffusion d'un individu optimal quand seul l'opérateur de sélection est actif) induite par une structure régulière bi-dimensionnelle de la population, proposant une modélisation logistique des courbes de pression de sélection. Ce modèle suppose que la diffusion d'un individu optimal suit une loi exponentielle. On montre que ce modèle est inadéquat pour décrire ce phénomène, étant donné que la vitesse de croissance doit obéir à une loi quadratique ou sous-quadratique dans le cas d'une structure régulière bi-dimensionnelle. De nouveaux modèles linéaires et sous-quadratique sont proposés pour des structures mono- et bi-dimensionnelles. Ces modèles sont étendus pour décrire des processus évolutionnaires asynchrones. Différentes dynamiques de la population impliquent strategies différentes de recherche de l'algorithme résultant lorsque le processus évolutionnaire est utilisé pour résoudre des problèmes d'optimisation. Un ensemble de problèmes discrets et continus est utilisé pour étudier les charactéristiques de recherche des différentes topologies et mises à jour des populations. Ces dernières années, les études de Watts et Strogatz ont montré que beaucoup de réseaux, aussi bien dans les mondes biologiques et sociologiques que dans les structures produites par l'homme, ont des propriétés mathématiques qui les séparent à la fois des structures régulières et des structures aléatoires. En particulier, ils ont introduit la notion de graphe sm,all-world et ont montré que cette nouvelle famille de structures possède des intéressantes propriétés dynamiques. Des populations ayant ces nouvelles topologies sont proposés, et leurs dynamiques évolutionnaires sont étudiées et modélisées. Pour des populations ayant ces structures, des méthodes d'évolution asynchrone sont proposées, et la dynamique résultante est étudiée. Beaucoup de réseaux produits par l'homme se sont formés d'une façon incrémentale, et des explications pour leur forme actuelle ont été proposées, comme le preferential attachment de Albert et Barabàsi. Toutefois, beaucoup de réseaux existants doivent être le produit d'un processus de variation et sélection darwiniennes. Ainsi, la façon dont ces structures ont pu être sélectionnées est une question intéressante restée sans réponse. Dans la dernière partie de ce travail, on montre comment un simple processus évolutif artificiel permet à ce type de topologies d'émerger dans le cas de deux problèmes prototypiques des réseaux d'automates, les tâches de densité et de synchronisation.

Reduction of respiratory motion artifacts for free-breathing whole-heart coronary MRA by weighted iterative reconstruction.

PURPOSE: To combine weighted iterative reconstruction with self-navigated free-breathing coronary magnetic resonance angiography for retrospective reduction of respiratory motion artifacts. METHODS: One-dimensional self-navigation was improved for robust respiratory motion detection and the consistency of the acquired data was estimated on the detected motion. Based on the data consistency, the data fidelity term of iterative reconstruction was weighted to reduce the effects of respiratory motion. In vivo experiments were performed in 14 healthy volunteers and the resulting image quality of the proposed method was compared to a navigator-gated reference in terms of acquisition time, vessel length, and sharpness. RESULT: Although the sampling pattern of the proposed method contained 60% more samples with respect to the reference, the scan efficiency was improved from 39.5 ± 10.1% to 55.1 ± 9.1%. The improved self-navigation showed a high correlation to the standard navigator signal and the described weighting efficiently reduced respiratory motion artifacts. Overall, the average image quality of the proposed method was comparable to the navigator-gated reference. CONCLUSION: Self-navigated coronary magnetic resonance angiography was successfully combined with weighted iterative reconstruction to reduce the total acquisition time and efficiently suppress respiratory motion artifacts. The simplicity of the experimental setup and the promising image quality are encouraging toward future clinical evaluation. Magn Reson Med 73:1885-1895, 2015. © 2014 Wiley Periodicals, Inc.