Increased numbers of circulating polyfunctional Th17 memory cells in patients with seronegative spondylarthritides

OBJECTIVE: A distinct subset of proinflammatory CD4+ T cells that produce interleukin-17 was recently identified. These cells are implicated in different autoimmune disease models, such as experimental autoimmune encephalomyelitis and collagen-induced arthritis, but their involvement in human autoimmune disease has not yet been clearly established. The purpose of this study was to assess the frequency and functional properties of Th17 cells in healthy donors and in patients with different autoimmune diseases. METHODS: Peripheral blood was obtained from 10 psoriatic arthritis (PsA), 10 ankylosing spondylitis (AS), 10 rheumatoid arthritis (RA), and 5 vitiligo patients, as well as from 25 healthy donors. Synovial tissue samples from a separate group of patients were also evaluated (obtained as paraffin-embedded sections). Peripheral blood cells were analyzed by multiparameter flow cytometry and immunohistochemistry. Cytokine production was examined by enzyme-linked immunosorbent assay and intracellular cytokine staining using specific monoclonal antibodies. Synovial tissue was examined for infiltrating T cells by immunohistochemical analysis. RESULTS: We found increased numbers of circulating Th17 cells in the peripheral blood of patients with seronegative spondylarthritides (PsA and AS), but not in patients with RA or vitiligo. In addition, Th17 cells from the spondylarthritis patients showed advanced differentiation and were polyfunctional in terms of T cell receptor-driven cytokine production. CONCLUSION: These observations suggest a role of Th17 cells in the pathogenesis of certain human autoimmune disorders, in particular the seronegative spondylarthritides.

Monitoring of human CD4 T cell responses in metastatic melanoma and arthritic patients

SUMMARY : Detailed knowledge of the different components of the immune system is required for the development of new immunotherapeutic strategies. CD4 T lymphocytes represent a highly heterogeneous group of cells characterized by various profiles of cytokine production and effector vs. regulatory functions. They are central players in orchestrating adaptive immune responses: unbalances between the different subtypes can lead either to aggressive autoimmune disorders or can favour the uncontrolled growth of malignancies. In this study we focused on the characterization of human CD4 T cells in advanced stage melanoma patients as well as in patients affected by various forms of autoimmune inflammatory spondyloarthropathies. In melanoma patients we report that a population of FOXP3 CD4 T cells, known as regulatory T cells, is overrepresented in peripheral blood, and even more in tumor-infitrated lymph nodes as well as at tumor sites, as compared to healthy donors. In tumor-infiltrated lymph nodes, but not in normal lymph nodes or in peripheral blood, FOXP3 CD4 T cells feature a highly differentiated phenotype (CD45RA-CCR7+/-), which suggests for a recent encounter with their cognate antigen. FOXP3 CD4 T cells have been described to be an important component of the several known immune escape mechanisms. We demonstrated that FOXP3 CD4 T cells isolated from melanoma patients exert an in vitro suppressive action on autologous CD4 T cells, thus possibly inhibiting an efficient anti-tumor response. Next, we aimed to analyse CD4 T cells at antigen-specific level. In advanced stage melanoma patients, we identified for the first time, using pMHCII multimers, circulating CD4 T cells specific for the melanoma antigen Melan-A, presented by HLA-DQB1 *0602. Interestingly, in a cohort of melanoma patients enrolled in an immunotherapy trails consisting of injection of a Melan-A derived peptide, we did not observe signif cant variations in the ex vivo frequencies of Melan-A specific CD4 T cells, but important differences in the quality of the specific CD4 T cells. In fact, up to 50% of the ex vivo Melan-A/DQ6 specific CD4 T cells displayed a regulatory phenotype and were hypoproliferative before vaccination, while more effector, cytokine-secreting Melan-A/DQ6 specific CD4 T cells were observed after immunization. These observations suggest that peptide vaccination may favourably modify the balance between regulatory and effector tumor-specific CD4 T cells. Finally, we identified another subset of CD4 T cells as possible mediator of pathology in a group of human autoimmune spondyloarthropathies, namely Th17 cells. These cells were recently described to play a critical role in the pathogenesis of some marine models of autommunity. We document an elevated presence of circulating Th17 cells in two members of seronegative spondyloarthropathies, e.g. psoriatic arthritis and ankylosing spondylitis, while we do not observe increased frequencies of Th17 cells in peripheral blood of rheumatoid arthritic patients. In addition, Th17 cells with a more advanced differentiation state (CD45RA-CCR7-CD27-) and polyfunctionality (concomitant secretion of IL-17, IL-2 and TNFα) were observed exclusively in patients with seronegative spondylarthropathies. Together, our observations emphasize the importance of CD4 T cells in various diseases and suggest that immunotherapeutic approaches considering CD4 T cells as targets should be evaluated in the future.

Rates of cultural change and patterns of cultural accumulation in stochastic models of social transmission.

Cultural variation in a population is affected by the rate of occurrence of cultural innovations, whether such innovations are preferred or eschewed, how they are transmitted between individuals in the population, and the size of the population. An innovation, such as a modification in an attribute of a handaxe, may be lost or may become a property of all handaxes, which we call "fixation of the innovation." Alternatively, several innovations may attain appreciable frequencies, in which case properties of the frequency distribution-for example, of handaxe measurements-is important. Here we apply the Moran model from the stochastic theory of population genetics to study the evolution of cultural innovations. We obtain the probability that an initially rare innovation becomes fixed, and the expected time this takes. When variation in cultural traits is due to recurrent innovation, copy error, and sampling from generation to generation, we describe properties of this variation, such as the level of heterogeneity expected in the population. For all of these, we determine the effect of the mode of social transmission: conformist, where there is a tendency for each naïve newborn to copy the most popular variant; pro-novelty bias, where the newborn prefers a specific variant if it exists among those it samples; one-to-many transmission, where the variant one individual carries is copied by all newborns while that individual remains alive. We compare our findings with those predicted by prevailing theories for rates of cultural change and the distribution of cultural variation.

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).

Visual functions in the healthy and schizophrenic brain : from stimulus control to illusory perceptions using electrical neuroimaging

ABSTRACT (FRENCH)Ce travail de thèse basé sur le système visuel chez les sujets sains et chez les patients schizophrènes, s'articule autour de trois articles scientifiques publiés ou en cours de publication. Ces articles traitent des sujets suivants : le premier article présente une nouvelle méthode de traitement des composantes physiques des stimuli (luminance et fréquence spatiale). Le second article montre, à l'aide d'analyses de données EEG, un déficit de la voie magnocellulaire dans le traitement visuel des illusions chez les patients schizophrènes. Ceci est démontré par l'absence de modulation de la composante PI chez les patients schizophrènes contrairement aux sujets sains. Cette absence est induite par des stimuli de type illusion Kanizsa de différentes excentricités. Finalement, le troisième article, également à l'aide de méthodes de neuroimagerie électrique (EEG), montre que le traitement des contours illusoires se trouve dans le complexe latéro-occipital (LOC), à l'aide d'illusion « misaligned gratings ». De plus il révèle que les activités démontrées précédemment dans les aires visuelles primaires sont dues à des inférences « top- down ».Afin de permettre la compréhension de ces trois articles, l'introduction de ce manuscrit présente les concepts essentiels. De plus des méthodes d'analyses de temps-fréquence sont présentées. L'introduction est divisée en quatre parties : la première présente le système visuel depuis les cellules retino-corticales aux deux voix du traitement de l'information en passant par les régions composant le système visuel. La deuxième partie présente la schizophrénie par son diagnostic, ces déficits de bas niveau de traitement des stimuli visuel et ces déficits cognitifs. La troisième partie présente le traitement des contours illusoires et les trois modèles utilisés dans le dernier article. Finalement, les méthodes de traitement des données EEG seront explicitées, y compris les méthodes de temps-fréquences.Les résultats des trois articles sont présentés dans le chapitre éponyme (du même nom). De plus ce chapitre comprendra les résultats obtenus à l'aide des méthodes de temps-fréquenceFinalement, la discussion sera orientée selon trois axes : les méthodes de temps-fréquence ainsi qu'une proposition de traitement de ces données par une méthode statistique indépendante de la référence. La discussion du premier article en montrera la qualité du traitement de ces stimuli. La discussion des deux articles neurophysiologiques, proposera de nouvelles d'expériences afin d'affiner les résultats actuels sur les déficits des schizophrènes. Ceci pourrait permettre d'établir un marqueur biologique fiable de la schizophrénie.ABSTRACT (ENGLISH)This thesis focuses on the visual system in healthy subjects and schizophrenic patients. To address this research, advanced methods of analysis of electroencephalographic (EEG) data were used and developed. This manuscript is comprised of three scientific articles. The first article showed a novel method to control the physical features of visual stimuli (luminance and spatial frequencies). The second article showed, using electrical neuroimaging of EEG, a deficit in spatial processing associated with the dorsal pathway in chronic schizophrenic patients. This deficit was elicited by an absent modulation of the PI component in terms of response strength and topography as well as source estimations. This deficit was orthogonal to the preserved ability to process Kanizsa-type illusory contours. Finally, the third article resolved ongoing debates concerning the neural mechanism mediating illusory contour sensitivity by using electrical neuroimaging to show that the first differentiation of illusory contour presence vs. absence is localized within the lateral occipital complex. This effect was subsequent to modulations due to the orientation of misaligned grating stimuli. Collectively, these results support a model where effects in V1/V2 are mediated by "top-down" modulation from the LOC.To understand these three articles, the Introduction of this thesis presents the major concepts used in these articles. Additionally, a section is devoted to time-frequency analysis methods not presented in the articles themselves. The introduction is divided in four parts. The first part presents three aspects of the visual system: cellular, regional, and its functional interactions. The second part presents an overview of schizophrenia and its sensoiy-cognitive deficits. The third part presents an overview of illusory contour processing and the three models examined in the third article. Finally, advanced analysis methods for EEG are presented, including time- frequency methodology.The Introduction is followed by a synopsis of the main results in the articles as well as those obtained from the time-frequency analyses.Finally, the Discussion chapter is divided along three axes. The first axis discusses the time frequency analysis and proposes a novel statistical approach that is independent of the reference. The second axis contextualizes the first article and discusses the quality of the stimulus control and direction for further improvements. Finally, both neurophysiologic articles are contextualized by proposing future experiments and hypotheses that may serve to improve our understanding of schizophrenia on the one hand and visual functions more generally.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

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.

Aerodynamic drag modeling of alpine skiers performing giant slalom turns.

PURPOSE: Aerodynamic drag plays an important role in performance for athletes practicing sports that involve high-velocity motions. In giant slalom, the skier is continuously changing his/her body posture, and this affects the energy dissipated in aerodynamic drag. It is therefore important to quantify this energy to understand the dynamic behavior of the skier. The aims of this study were to model the aerodynamic drag of alpine skiers in giant slalom simulated conditions and to apply these models in a field experiment to estimate energy dissipated through aerodynamic drag. METHODS: The aerodynamic characteristics of 15 recreational male and female skiers were measured in a wind tunnel while holding nine different skiing-specific postures. The drag and the frontal area were recorded simultaneously for each posture. Four generalized and two individualized models of the drag coefficient were built, using different sets of parameters. These models were subsequently applied in a field study designed to compare the aerodynamic energy losses between a dynamic and a compact skiing technique. RESULTS: The generalized models estimated aerodynamic drag with an accuracy of between 11.00% and 14.28%, and the individualized models estimated aerodynamic drag with an accuracy between 4.52% and 5.30%. The individualized model used for the field study showed that using a dynamic technique led to 10% more aerodynamic drag energy loss than using a compact technique. DISCUSSION: The individualized models were capable of discriminating different techniques performed by advanced skiers and seemed more accurate than the generalized models. The models presented here offer a simple yet accurate method to estimate the aerodynamic drag acting upon alpine skiers while rapidly moving through the range of positions typical to turning technique.

Explicit ruin formulas for models with dependence among risks

We show that a simple mixing idea allows one to establish a number of explicit formulas for ruin probabilities and related quantities in collective risk models with dependence among claim sizes and among claim inter-occurrence times. Examples include compound Poisson risk models with completely monotone marginal claim size distributions that are dependent according to Archimedean survival copulas as well as renewal risk models with dependent inter-occurrence times.

SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realised properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate assemblage properties. Here, we propose to unify the two approaches in a single 'spatially-explicit species assemblage modelling' (SESAM) framework. This framework uses relevant species source pool designations, macroecological factors, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.

Absence of progression as assessed by response evaluation criteria in solid tumors predicts survival in advanced GI stromal tumors treated with imatinib mesylate: the intergroup EORTC-ISG-AGITG phase III trial.

PURPOSE: From February 2001 to February 2002, 946 patients with advanced GI stromal tumors (GISTs) treated with imatinib were included in a controlled EORTC/ISG/AGITG (European Organisation for Research and Treatment of Cancer/Italian Sarcoma Group/Australasian Gastro-Intestinal Trials Group) trial. This analysis investigates whether the response classification assessed by RECIST (Response Evaluation Criteria in Solid Tumors), predicts for time to progression (TTP) and overall survival (OS). PATIENTS AND METHODS: Per protocol, the first three disease assessments were done at 2, 4, and 6 months. For the purpose of the analysis (landmark method), disease response was subclassified in six categories: partial response (PR; > 30% size reduction), minor response (MR; 10% to 30% reduction), no change (NC) as either NC- (0% to 10% reduction) or NC+ (0% to 20% size increase), progressive disease (PD; > 20% increase/new lesions), and subjective PD (clinical progression). RESULTS: A total of 906 patients had measurable disease at entry. At all measurement time points, complete response (CR), PR, and MR resulted in similar TTP and OS; this was also true for NC- and NC+, and for PD and subjective PD. Patients were subsequently classified as responders (CR/PR/MR), NC (NC+/NC-), or PD. This three-class response categorization was found to be highly predictive of further progression or survival for the first two measurement points. After 6 months of imatinib, responders (CR/PR/MR) had the same survival prognosis as patients classified as NC. CONCLUSION: RECIST perfectly enables early discrimination between patients who benefited long term from imatinib and those who did not. After 6 months of imatinib, if the patient is not experiencing PD, the pattern of radiologic response by tumor size criteria has no prognostic value for further outcome. Imatinib needs to be continued as long as there is no progression according to RECIST.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Exploring models for the length of stay distribution.

Diagnosis Related Groups (DRG) are frequently used to standardize the comparison of consumption variables, such as length of stay (LOS). In order to be reliable, this comparison must control for the presence of outliers, i.e. values far removed from the pattern set by the majority of the data. Indeed, outliers can distort the usual statistical summaries, such as means and variances. A common practice is to trim LOS values according to various empirical rules, but there is little theoretical support for choosing between alternative procedures. This pilot study explores the possibility of describing LOS distributions with parametric models which provide the necessary framework for the use of robust methods.