Proteomic analysis of membrane rafts of melanoma cells identifies protein patterns characteristic of the tumor progression stage.

The molecular mechanisms controlling the progression of melanoma from a localized tumor to an invasive and metastatic disease are poorly understood. In the attempt to start defining a functional protein profile of melanoma progression, we have analyzed by LC-MS/MS the proteins associated with detergent resistant membranes (DRMs), which are enriched in cholesterol/sphingolipids-containing membrane rafts, of melanoma cell lines derived from tumors at different stages of progression. Since membrane rafts are involved in several biological processes, including signal transduction and protein trafficking, we hypothesized that the association of proteins with rafts can be regulated during melanoma development and affect protein function and disease progression. We have identified a total of 177 proteins in the DRMs of the cell lines examined. Among these, we have found groups of proteins preferentially associated with DRMs of either less malignant radial growth phase/vertical growth phase (VGP) cells, or aggressive VGP and metastatic cells suggesting that melanoma cells with different degrees of malignancy have different DRM profiles. Moreover, some proteins were found in DRMs of only some cell lines despite being expressed at similar levels in all the cell lines examined, suggesting the existence of mechanisms controlling their association with DRMs. We expect that understanding the mechanisms regulating DRM targeting and the activity of the proteins differentially associated with DRMs in relation to cell malignancy will help identify new molecular determinants of melanoma progression.

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Self-potentials in partially saturated media: the importance of explicit modeling of electrode effects

Self-potential (SP) data are of interest to vadose zone hydrology because of their direct sensitivity to water flow and ionic transport. There is unfortunately little consensus in the literature about how to best model SP data under partially saturated conditions, and different approaches (often supported by one laboratory data set alone) have been proposed. We argue that this lack of agreement can largely be traced to electrode effects that have not been properly taken into account. A series of drainage and imbibition experiments were considered in which we found that previously proposed approaches to remove electrode effects were unlikely to provide adequate corrections. Instead, we explicitly modeled the electrode effects together with classical SP contributions using a flow and transport model. The simulated data agreed overall with the observed SP signals and allowed decomposing the different signal contributions to analyze them separately. After reviewing other published experimental data, we suggest that most of them include electrode effects that have not been properly taken into account. Our results suggest that previously presented SP theory works well when considering the modeling uncertainties presently associated with electrode effects. Additional work is warranted to not only develop suitable electrodes for laboratory experiments but also to assure that associated electrode effects that appear inevitable in longer term experiments are predictable, so that they can be incorporated into the modeling framework.

BTLA mediates inhibition of human tumor-specific CD8+ T cells that can be partially reversed by vaccination.

The function of antigen-specific CD8+ T cells, which may protect against both infectious and malignant diseases, can be impaired by ligation of their inhibitory receptors, which include CTL-associated protein 4 (CTLA-4) and programmed cell death 1 (PD-1). Recently, B and T lymphocyte attenuator (BTLA) was identified as a novel inhibitory receptor with structural and functional similarities to CTLA-4 and PD-1. BTLA triggering leads to decreased antimicrobial and autoimmune T cell responses in mice, but its functions in humans are largely unknown. Here we have demonstrated that as human viral antigen-specific CD8+ T cells differentiated from naive to effector cells, their surface expression of BTLA was gradually downregulated. In marked contrast, human melanoma tumor antigen-specific effector CD8+ T cells persistently expressed high levels of BTLA in vivo and remained susceptible to functional inhibition by its ligand herpes virus entry mediator (HVEM). Such persistence of BTLA expression was also found in tumor antigen-specific CD8+ T cells from melanoma patients with spontaneous antitumor immune responses and after conventional peptide vaccination. Remarkably, addition of CpG oligodeoxynucleotides to the vaccine formulation led to progressive downregulation of BTLA in vivo and consequent resistance to BTLA-HVEM-mediated inhibition. Thus, BTLA activation inhibits the function of human CD8+ cancer-specific T cells, and appropriate immunotherapy may partially overcome this inhibition.

Statistical modeling of bowing control applied to violin sound synthesis

Excitation-continuous music instrument control patterns are often not explicitly represented in current sound synthesis techniques when applied to automatic performance. Both physical model-based and sample-based synthesis paradigmswould benefit from a flexible and accurate instrument control model, enabling the improvement of naturalness and realism. Wepresent a framework for modeling bowing control parameters inviolin performance. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing control parameter signals.We model the temporal contour of bow velocity, bow pressing force, and bow-bridge distance as sequences of short Bézier cubic curve segments. Considering different articulations, dynamics, and performance contexts, a number of note classes are defined. Contours of bowing parameters in a performance database are analyzed at note-level by following a predefined grammar that dictates characteristics of curve segment sequences for each of the classes in consideration. As a result, contour analysis of bowing parameters of each note yields an optimal representation vector that is sufficient for reconstructing original contours with significant fidelity. From the resulting representation vectors, we construct a statistical model based on Gaussian mixtures suitable for both the analysis and synthesis of bowing parameter contours. By using the estimated models, synthetic contours can be generated through a bow planning algorithm able to reproduce possible constraints caused by the finite length of the bow. Rendered contours are successfully used in two preliminary synthesis frameworks: digital waveguide-based bowed stringphysical modeling and sample-based spectral-domain synthesis.

Correlation of CXCL10, tumor necrosis factor receptor type II, and galectin 9 with disease activity in juvenile dermatomyositis.

OBJECTIVE: Juvenile dermatomyositis (DM) is a systemic autoimmune disorder of unknown immunopathogenesis in which the immune system targets the microvasculature of skeletal muscles, skin, and other organs. The current mainstay of therapy is a steroid regimen in combination with other immunosuppressive treatments. To date, no validated markers for monitoring disease activity have been identified, which hampers personalized treatment. This study was undertaken to identify a panel of proteins specifically related to active disease in juvenile DM. METHODS: We performed a multiplex immunoassay for plasma levels of 45 proteins related to inflammation in 25 patients with juvenile DM in 4 clinically well-defined groups, as determined by clinical activity and treatment. We compared them to 14 age-matched healthy children and 8 age-matched children with nonautoimmune muscle disease. RESULTS: Cluster analysis of circulating proteins showed distinct profiles for juvenile DM patients and controls based on a group of 10 proteins. In addition to CXCL10, tumor necrosis factor receptor type II (TNFRII) and galectin 9 were significantly increased in active juvenile DM. The levels of these 3 proteins were tightly linked to active disease and correlated with clinical scores (as measured by the Childhood Myositis Assessment Scale and physician's global assessment of disease activity on a visual analog scale). CONCLUSION: Our findings indicate that CXCL10, TNFRII, and galectin 9 correspond to disease status in juvenile DM and thus could be helpful in monitoring disease activity and guiding treatment. Furthermore, they might provide new knowledge about the pathogenesis of this autoimmune disease.

Formation of virus-like clusters is an intrinsic property of the tumor necrosis factor family member BAFF (B cell activating factor).

The oligomeric state of BAFF (B cell activing factor), a tumor necrosis factor (TNF) family cytokine that plays a critical role in B cell development and survival, has been the subject of recent debate. Myc-tagged BAFF starting at residue Gln136 was previously reported to crystallize as trimers at pH 4.5, whereas a histidine-tagged construct of BAFF, starting at residue Ala134, formed a virus-like cluster containing 60 monomers when crystallized at pH 9.0. The formation of the BAFF 60-mer was pH dependent, requiring pH >or= 7.0. More recently, 60-mer formation was suggested to be artificially induced by the histidine tag, and it was proposed that BAFF, like all other TNF family members, is trimeric. We report here that a construct of BAFF with no amino-terminal tag (Ala134-BAFF) can form a 60-mer in solution. Using size exclusion chromatography and static light scattering to monitor trimer to 60-mer ratios in BAFF preparations, we find that 60-mer formation is pH-dependent and requires histidine 218 within the DE loop of BAFF. Biacore measurements established that the affinity of Ala134-BAFF for the BAFF receptor BAFFR/BR3 is similar to that of myc-Gln136-BAFF, which is exclusively trimeric in solution. However, Ala134-BAFF is more efficacious than myc-Gln136-BAFF in inducing B cell proliferation in vitro. We additionally show that BAFF that is processed and secreted by 293T cells transfected with full-length BAFF, or by a histiocytic lymphoma cell line (U937) that expresses BAFF endogenously, forms a pH-dependent 60-mer in solution. Our results indicate that the formation of the 60-mer in solution by the BAFF extracellular domain is an intrinsic property of the protein, and therefore that this more active form of BAFF may be physiologically relevant.

Statistical modeling of violin bowing parameter contours

We present a framework for modeling right-hand gestures in bowed-string instrument playing, applied to violin. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing gesture parameter cues. We model the temporal contour of bow transversal velocity, bow pressing force, and bow-bridge distance as sequences of short segments, in particular B´ezier cubic curve segments. Considering different articulations, dynamics, andcontexts, a number of note classes is defined. Gesture parameter contours of a performance database are analyzed at note-level by following a predefined grammar that dictatescharacteristics of curve segment sequences for each of the classes into consideration. Based on dynamic programming, gesture parameter contour analysis provides an optimal curve parameter vector for each note. The informationpresent in such parameter vector is enough for reconstructing original gesture parameter contours with significant fidelity. From the resulting representation vectors, weconstruct a statistical model based on Gaussian mixtures, suitable for both analysis and synthesis of bowing gesture parameter contours. We show the potential of the modelby synthesizing bowing gesture parameter contours from an annotated input score. Finally, we point out promising applicationsand developments.