Demographic and geographic vascular risk factor differences in European young adults with ischemic stroke: the 15 cities young stroke study.

BACKGROUND AND PURPOSE: We compared among young patients with ischemic stroke the distribution of vascular risk factors among sex, age groups, and 3 distinct geographic regions in Europe. METHODS: We included patients with first-ever ischemic stroke aged 15 to 49 years from existing hospital- or population-based prospective or consecutive young stroke registries involving 15 cities in 12 countries. Geographic regions were defined as northern (Finland, Norway), central (Austria, Belgium, France, Germany, Hungary, The Netherlands, Switzerland), and southern (Greece, Italy, Turkey) Europe. Hierarchical regression models were used for comparisons. RESULTS: In the study cohort (n=3944), the 3 most frequent risk factors were current smoking (48.7%), dyslipidemia (45.8%), and hypertension (35.9%). Compared with central (n=1868; median age, 43 years) and northern (n=1330; median age, 44 years) European patients, southern Europeans (n=746; median age, 41 years) were younger. No sex difference emerged between the regions, male:female ratio being 0.7 in those aged <34 years and reaching 1.7 in those aged 45 to 49 years. After accounting for confounders, no risk-factor differences emerged at the region level. Compared with females, males were older and they more frequently had dyslipidemia or coronary heart disease, or were smokers, irrespective of region. In both sexes, prevalence of family history of stroke, dyslipidemia, smoking, hypertension, diabetes mellitus, coronary heart disease, peripheral arterial disease, and atrial fibrillation positively correlated with age across all regions. CONCLUSIONS: Primary preventive strategies for ischemic stroke in young adults-having high rate of modifiable risk factors-should be targeted according to sex and age at continental level.

From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways

Background: Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks. Results: First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented. Conclusion: In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.

Neuroprotective gene therapy for Huntington's disease, using polymer-encapsulated cells engineered to secrete human ciliary neurotrophic factor: results of a phase I study.

Huntington's disease (HD) is a monogenic neurodegenerative disease that affects the efferent neurons of the striatum. The protracted evolution of the pathology over 15 to 20 years, after clinical onset in adulthood, underscores the potential of therapeutic tools that would aim at protecting striatal neurons. Proteins with neuroprotective effects in the adult brain have been identified, among them ciliary neurotrophic factor (CNTF), which protected striatal neurons in animal models of HD. Accordingly, we have carried out a phase I study evaluating the safety of intracerebral administration of this protein in subjects with HD, using a device formed by a semipermeable membrane encapsulating a BHK cell line engineered to synthesize CNTF. Six subjects with stage 1 or 2 HD had one capsule implanted into the right lateral ventricle; the capsule was retrieved and exchanged for a new one every 6 months, over a total period of 2 years. No sign of CNTF-induced toxicity was observed; however, depression occurred in three subjects after removal of the last capsule, which may have correlated with the lack of any future therapeutic option. All retrieved capsules were intact but contained variable numbers of surviving cells, and CNTF release was low in 13 of 24 cases. Improvements in electrophysiological results were observed, and were correlated with capsules releasing the largest amount of CNTF. This phase I study shows the safety, feasibility, and tolerability of this gene therapy procedure. Heterogeneous cell survival, however, stresses the need for improving the technique.

Swain: Stata module to correct the SEM chi-square overidentification test in small sample sizes or complex models. Statistical Software Components S457617, Boston College Department of Economics.

Swain corrects the chi-square overidentification test (i.e., likelihood ratio test of fit) for structural equation models whethr with or without latent variables. The chi-square statistic is asymptotically correct; however, it does not behave as expected in small samples and/or when the model is complex (cf. Herzog, Boomsma, & Reinecke, 2007). Thus, particularly in situations where the ratio of sample size (n) to the number of parameters estimated (p) is relatively small (i.e., the p to n ratio is large), the chi-square test will tend to overreject correctly specified models. To obtain a closer approximation to the distribution of the chi-square statistic, Swain (1975) developed a correction; this scaling factor, which converges to 1 asymptotically, is multiplied with the chi-square statistic. The correction better approximates the chi-square distribution resulting in more appropriate Type 1 reject error rates (see Herzog & Boomsma, 2009; Herzog, et al., 2007).

Identification of a new murine tumor necrosis factor receptor locus that contains two novel murine receptors for tumor necrosis factor-related apoptosis-inducing ligand (TRAIL).

Tumor necrosis factor (TNF) ligand and receptor superfamily members play critical roles in diverse developmental and pathological settings. In search for novel TNF superfamily members, we identified a murine chromosomal locus that contains three new TNF receptor-related genes. Sequence alignments suggest that the ligand binding regions of these murine TNF receptor homologues, mTNFRH1, -2 and -3, are most homologous to those of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptors. By using a number of in vitro ligand-receptor binding assays, we demonstrate that mTNFRH1 and -2, but not mTNFRH3, bind murine TRAIL, suggesting that they are indeed TRAIL receptors. This notion is further supported by our demonstration that both mTNFRH1:Fc and mTNFRH2:Fc fusion proteins inhibited mTRAIL-induced apoptosis of Jurkat cells. Unlike the only other known murine TRAIL receptor mTRAILR2, however, neither mTNFRH2 nor mTNFRH3 has a cytoplasmic region containing the well characterized death domain motif. Coupled with our observation that overexpression of mTNFRH1 and -2 in 293T cells neither induces apoptosis nor triggers NFkappaB activation, we propose that the mTnfrh1 and mTnfrh2 genes encode the first described murine decoy receptors for TRAIL, and we renamed them mDcTrailr1 and -r2, respectively. Interestingly, the overall sequence structures of mDcTRAILR1 and -R2 are quite distinct from those of the known human decoy TRAIL receptors, suggesting that the presence of TRAIL decoy receptors represents a more recent evolutionary event.

PACIC Instrument: disentangling dimensions using published validation models.

OBJECTIVE: To better understand the structure of the Patient Assessment of Chronic Illness Care (PACIC) instrument. More specifically to test all published validation models, using one single data set and appropriate statistical tools. DESIGN: Validation study using data from cross-sectional survey. PARTICIPANTS: A population-based sample of non-institutionalized adults with diabetes residing in Switzerland (canton of Vaud). MAIN OUTCOME MEASURE: French version of the 20-items PACIC instrument (5-point response scale). We conducted validation analyses using confirmatory factor analysis (CFA). The original five-dimension model and other published models were tested with three types of CFA: based on (i) a Pearson estimator of variance-covariance matrix, (ii) a polychoric correlation matrix and (iii) a likelihood estimation with a multinomial distribution for the manifest variables. All models were assessed using loadings and goodness-of-fit measures. RESULTS: The analytical sample included 406 patients. Mean age was 64.4 years and 59% were men. Median of item responses varied between 1 and 4 (range 1-5), and range of missing values was between 5.7 and 12.3%. Strong floor and ceiling effects were present. Even though loadings of the tested models were relatively high, the only model showing acceptable fit was the 11-item single-dimension model. PACIC was associated with the expected variables of the field. CONCLUSIONS: Our results showed that the model considering 11 items in a single dimension exhibited the best fit for our data. A single score, in complement to the consideration of single-item results, might be used instead of the five dimensions usually described.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Pharmacological inhibition of fibroblast growth factor (FGF) receptor signaling ameliorates FGF23-mediated hypophosphatemic rickets.

Fibroblast growth factor 23 (FGF23) is a circulating factor secreted by osteocytes that is essential for phosphate homeostasis. In kidney proximal tubular cells FGF23 inhibits phosphate reabsorption and leads to decreased synthesis and enhanced catabolism of 1,25-dihydroxyvitamin D3 (1,25[OH]2 D3 ). Excess levels of FGF23 cause renal phosphate wasting and suppression of circulating 1,25(OH)2 D3 levels and are associated with several hereditary hypophosphatemic disorders with skeletal abnormalities, including X-linked hypophosphatemic rickets (XLH) and autosomal recessive hypophosphatemic rickets (ARHR). Currently, therapeutic approaches to these diseases are limited to treatment with activated vitamin D analogues and phosphate supplementation, often merely resulting in partial correction of the skeletal aberrations. In this study, we evaluate the use of FGFR inhibitors for the treatment of FGF23-mediated hypophosphatemic disorders using NVP-BGJ398, a novel selective, pan-specific FGFR inhibitor currently in Phase I clinical trials for cancer therapy. In two different hypophosphatemic mouse models, Hyp and Dmp1-null mice, resembling the human diseases XLH and ARHR, we find that pharmacological inhibition of FGFRs efficiently abrogates aberrant FGF23 signaling and normalizes the hypophosphatemic and hypocalcemic conditions of these mice. Correspondingly, long-term FGFR inhibition in Hyp mice leads to enhanced bone growth, increased mineralization, and reorganization of the disturbed growth plate structure. We therefore propose NVP-BGJ398 treatment as a novel approach for the therapy of FGF23-mediated hypophosphatemic diseases.

Essential role of platelet activation via protease activated receptor 4 in tissue factor-initiated inflammation.

INTRODUCTION: Tissue factor (TF) activation of the coagulation proteases enhances inflammation in animal models of arthritis and endotoxemia, but the mechanism of this effect is not yet fully understood - in particular, whether this is primarily due to fibrin formation or through activation of protease activated receptors (PARs). METHODS: We induced extravascular inflammation by injection of recombinant soluble murine TF (sTF1-219) in the hind paw. The effects of thrombin inhibition, fibrinogen and platelet depletion were evaluated, as well as the effects of PAR deficiency using knockout mice deficient for each of the PARs. RESULTS: Injection of soluble TF provoked a rapid onset of paw swelling. Inflammation was confirmed histologically and by increased serum IL-6 levels. Inflammation was significantly reduced by depletion of fibrinogen (P < 0.05) or platelets (P = 0.015), and by treatment with hirudin (P = 0.04) or an inhibitor of activated factor VII (P < 0.001) compared with controls. PAR-4-deficient mice exhibited significantly reduced paw swelling (P = 0.003). In contrast, a deficiency in either PAR-1, PAR-2 or PAR-3 did not affect the inflammatory response to soluble TF injection. CONCLUSION: Our results show that soluble TF induces acute inflammation through a thrombin-dependent pathway and both fibrin deposition and platelet activation are essential steps in this process. The activation of PAR-4 on platelets is crucial and the other PARs do not play a major role in soluble TF-induced inflammation.

Testing new identity models and processes in French-speaking adolescents and emerging adults students

Developing a sense of identity is a crucial psychosocial task for young people. The purpose of this study was to evaluate identity development in French-speaking adolescents and emerging adults (in France and Switzerland) using a process-oriented model of identity formation including five dimensions (i.e., exploration in breadth, commitment making, exploration in depth, identification with commitment, and ruminative exploration). The study included participants from three different samples (total N = 2239, 66.7% women): two samples of emerging adult student and one sample of adolescents. Results confirmed the hypothesized five-factor dimensional model of identity in our three samples and provided evidence for convergent validity of the model. The results also indicated that exploration in depth might be subdivided in two aspects: a first form of exploration in depth leading to a better understanding and to an increase of the strength of current commitments and a second form of exploration in depth leading to a re-evaluation and a reconsideration of current commitments. Further, the identity status cluster solution that emerged is globally in line with previous literature (i.e., achievement, foreclosure, moratorium, carefree diffusion, diffused diffusion, undifferentiated). However, despite a structural similarity, we found variations in identity profiles because identity development is shaped by cultural context. These specific variations are discussed in light of social, educational and economic differences between France and the French-speaking part of Switzerland. Implications and suggestions for future research are offered.

Three essays in dynamic corporate finance

Summary Throughout my thesis, I elaborate on how real and financing frictions affect corporate decision making under uncertainty, and I explore how firms time their investment and financing decisions given such frictions. While the macroeconomics literature has focused on the impact of real frictions on investment decisions assuming all equity financed firms, the financial economics literature has mainly focused on the study of financing frictions. My thesis therefore assesses the join interaction of real and financing frictions in firms' dynamic investment and financing decisions. My work provides a rationale for the documented poor empirical performance of neoclassical investment models based on the joint effect of real and financing frictions on investment. A major observation relies in how the infrequency of corporate decisions may affect standard empirical tests. My thesis suggests that the book to market sorts commonly used in the empirical asset pricing literature have economic content, as they control for the lumpiness in firms' optimal investment policies. My work also elaborates on the effects of asymmetric information and strategic interaction on firms' investment and financing decisions. I study how firms time their decision to raise public equity when outside investors lack information about their future investment prospects. I derive areal-options model that predicts either cold or hot markets for new stock issues conditional on adverse selection, and I provide a rational approach to study jointly the market timing of corporate decisions and announcement effects in stock returns. My doctoral dissertation therefore contributes to our understanding of how under real and financing frictions may bias standard empirical tests, elaborates on how adverse selection may induce hot and cold markets in new issues' markets, and suggests how the underlying economic behaviour of firms may induce alternative patterns in stock prices.

Neutralization of Macrophage Migration Inhibitory Factor (MIF) by Fully Human Antibodies Correlates with Their Specificity for the β-Sheet Structure of MIF.

The macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that recently emerged as an attractive therapeutic target for a variety of diseases. A diverse panel of fully human anti-MIF antibodies was generated by selection from a phage display library and extensively analyzed in vitro. Epitope mapping studies identified antibodies specific for linear as well as structural epitopes. Experimental animal studies revealed that only those antibodies binding epitopes within amino acids 50-68 or 86-102 of the MIF molecule exerted protective effects in models of sepsis or contact hypersensitivity. Within the MIF protein, these two binding regions form a β-sheet structure that includes the MIF oxidoreductase motif. We therefore conclude that this β-sheet structure is a crucial region for MIF activity and a promising target for anti-MIF antibody therapy.

Psychogenic seizures and frontal disconnection: EEG synchronisation study.

Objective Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. Methods The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. Results In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. Interpretation PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.

A unifying approach to the empirical evaluation of asset pricing models

Two main approaches are commonly used to empirically evaluate linear factor pricingmodels: regression and SDF methods, with centred and uncentred versions of the latter.We show that unlike standard two-step or iterated GMM procedures, single-step estimatorssuch as continuously updated GMM yield numerically identical values for prices of risk,pricing errors, Jensen s alphas and overidentifying restrictions tests irrespective of the modelvalidity. Therefore, there is arguably a single approach regardless of the factors being tradedor not, or the use of excess or gross returns. We illustrate our results by revisiting Lustigand Verdelhan s (2007) empirical analysis of currency returns.

Dynamic price competition with fixed capacities

Many revenue management (RM) industries are characterized by (a) fixed capacities in theshort term (e.g., hotel rooms, seats on an airline flight), (b) homogeneous products (e.g., twoairline flights between the same cities at similar times), and (c) customer purchasing decisionslargely influenced by price. Competition in these industries is also very high even with just twoor three direct competitors in a market. However, RM competition is not well understood andpractically all known implementations of RM software and most published models of RM donot explicitly model competition. For this reason, there has been considerable recent interestand research activity to understand RM competition. In this paper we study price competitionfor an oligopoly in a dynamic setting, where each of the sellers has a fixed number of unitsavailable for sale over a fixed number of periods. Demand is stochastic, and depending on howit evolves, sellers may change their prices at any time. This reflects the fact that firms constantly,and almost costlessly, change their prices (alternately, allocations at a price in quantity-basedRM), reacting either to updates in their estimates of market demand, competitor prices, orinventory levels. We first prove existence of a unique subgame-perfect equilibrium for a duopoly.In equilibrium, in each state sellers engage in Bertrand competition, so that the seller withthe lowest reservation value ends up selling a unit at a price that is equal to the equilibriumreservation value of the competitor. This structure hence extends the marginal-value conceptof bid-price control, used in many RM implementations, to a competitive model. In addition,we show that the seller with the lowest capacity sells all its units first. Furthermore, we extendthe results transparently to n firms and perform a number of numerical comparative staticsexploiting the uniqueness of the subgame-perfect equilibrium.