Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

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.

Limites des procédés d'investigation de la fonction érectile dans le cadre d'expertises médicolégales [Limits of the processes of investigation of the erectile function in forensic expertises].

Résumé La responsabilité d'un expert chargé d'apprécier la capacité érectile d'un prévenu est importante, car la peine infligée par le tribunal peut dépendre de ses conclusions. Sa tâche est en plus ardue car les procédés d'investigations de la fonction érectile ont tous des limites. Malgré toutes ces limites qui sont décrites dans cet article, l'expert peut aider à la recherche de la vérité comme les auteurs le démontrent. Summary The responsability of the expert in charge to appreciate the erectile capacity of an accused is considerable, as the sentence inflicted by the Tribunal could depend directly on his conclusions. His duty is harder as all the investigation procedures of the erectile functions have limits. Despite these limits, described in this article, the expert can help to discover the truth, as demonstrated by the authors.

The role of NFI-C liver regeneration and its potential function as a general regulator of regenerative processes

Collectively, research aimed to understand the regeneration of certain tissues has unveiled the existence of common key regulators. Knockout studies of the murine Nuclear Factor I-C (NFI-C) transcription factor revealed a misregulation of growth factor signaling, in particular that of transforming growth factor ß-1 (TGF-ßl), which led to alterations of skin wound healing and the growth of its appendages, suggesting it may be a general regulator of regenerative processes. We sought to investigate this further by determining whether NFI-C played a role in liver regeneration. Liver regeneration following two-thirds removal of the liver by partial hepatectomy (PH) is a well-established regenerative model whereby changes elicited in hepatocytes following injury lead to a rapid, phased proliferation. However, mechanisms controlling the action of liver proliferative factors such as transforming growth factor-ßl (TGF-ß1) and plasminogen activator inhibitor-1 (PAI-1) remain largely unknown. We show that the absence of NFI-C impaired hepatocyte proliferation due to an overexpression of PAI-1 and the subsequent suppression of urokinase plasminogen (uPA) activity and hepatocyte growth factor (HGF) signaling, a potent hepatocyte mitogen. This indicated that NFI-C first acts to promote hepatocyte proliferation at the onset of liver regeneration in wildtype mice. The subsequent transient down regulation of NFI-C, as can be explained by a self- regulatory feedback loop with TGF-ßl, may limit the number of hepatocytes entering the first wave of cell division and/or prevent late initiations of mitosis. Overall, we conclude that NFI-C acts as a regulator of the phased hepatocyte proliferation during liver regeneration. Taken together with NFI-C's actions in other in vivo models of (re)generation, it is plausible that NFI-C may be a general regulator of regenerative processes. - L'ensemble des recherches visant à comprendre la régénération de certains tissus a permis de mettre en évidence l'existence de régulateurs-clés communs. L'étude des souris, dépourvues du gène codant pour le facteur de transcription NFI-C (Nuclear Factor I-C), a montré des dérèglements dans la signalisation de certains facteurs croissance, en particulier du TGF-ßl (transforming growth factor-ßl), ce qui conduit à des altérations de la cicatrisation de la peau et de la croissance des poils et des dents chez ces souris, suggérant que NFI-C pourrait être un régulateur général du processus de régénération. Nous avons cherché à approfondir cette question en déterminant si NFI-C joue un rôle dans la régénération du foie. La régénération du foie, induite par une hépatectomie partielle correspondant à l'ablation des deux-tiers du foie, constitue un modèle de régénération bien établi dans lequel la lésion induite conduit à la prolifération rapide des hépatocytes de façon synchronisée. Cependant, les mécanismes contrôlant l'action de facteurs de prolifération du foie, comme le facteur de croissance TGF-ßl et l'inhibiteur de l'activateur du plasminogène PAI-1 (plasminogen activator inhibitor-1), restent encore très méconnus. Nous avons pu montrer que l'absence de NFI-C affecte la prolifération des hépatocytes, occasionnée par la surexpression de PAI-1 et par la subséquente suppression de l'activité de la protéine uPA (urokinase plasminogen) et de la signalisation du facteur de croissance des hépatocytes HGF (hepatocyte growth factor), un mitogène puissant des hépatocytes. Cela indique que NFI-C agit en premier lieu pour promouvoir la prolifération des hépatocytes au début de la régénération du foie chez les souris de type sauvage. La subséquente baisse transitoire de NFI-C, pouvant s'expliquer par une boucle rétroactive d'autorégulation avec le facteur TGF-ßl, pourrait limiter le nombre d'hépatocytes qui entrent dans la première vague de division cellulaire et/ou inhiber l'initiation de la mitose tardive. L'ensemble de ces résultats nous a permis de conclure que NFI-C agit comme un régulateur de la prolifération des hépatocytes synchrones au cours de la régénération du foie.

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.

Astrocytic αVβ3 integrin inhibits neurite outgrowth and promotes retraction of neuronal processes by clustering Thy-1.

Thy-1 is a membrane glycoprotein suggested to stabilize or inhibit growth of neuronal processes. However, its precise function has remained obscure, because its endogenous ligand is unknown. We previously showed that Thy-1 binds directly to α(V)β(3) integrin in trans eliciting responses in astrocytes. Nonetheless, whether α(V)β(3) integrin might also serve as a Thy-1-ligand triggering a neuronal response has not been explored. Thus, utilizing primary neurons and a neuron-derived cell line CAD, Thy-1-mediated effects of α(V)β(3) integrin on growth and retraction of neuronal processes were tested. In astrocyte-neuron co-cultures, endogenous α(V)β(3) integrin restricted neurite outgrowth. Likewise, α(V)β(3)-Fc was sufficient to suppress neurite extension in Thy-1(+), but not in Thy-1(-) CAD cells. In differentiating primary neurons exposed to α(V)β(3)-Fc, fewer and shorter dendrites were detected. This effect was abolished by cleavage of Thy-1 from the neuronal surface using phosphoinositide-specific phospholipase C (PI-PLC). Moreover, α(V)β(3)-Fc also induced retraction of already extended Thy-1(+)-axon-like neurites in differentiated CAD cells as well as of axonal terminals in differentiated primary neurons. Axonal retraction occurred when redistribution and clustering of Thy-1 molecules in the plasma membrane was induced by α(V)β(3) integrin. Binding of α(V)β(3)-Fc was detected in Thy-1 clusters during axon retraction of primary neurons. Moreover, α(V)β(3)-Fc-induced Thy-1 clustering correlated in time and space with redistribution and inactivation of Src kinase. Thus, our data indicates that α(V)β(3) integrin is a ligand for Thy-1 that upon binding not only restricts the growth of neurites, but also induces retraction of already existing processes by inducing Thy-1 clustering. We propose that these events participate in bi-directional astrocyte-neuron communication relevant to axonal repair after neuronal damage.

Effect of orthographic processes on letter identity and letter-position encoding in dyslexic children.

The ability to identify letters and encode their position is a crucial step of the word recognition process. However and despite their word identification problem, the ability of dyslexic children to encode letter identity and letter-position within strings was not systematically investigated. This study aimed at filling this gap and further explored how letter identity and letter-position encoding is modulated by letter context in developmental dyslexia. For this purpose, a letter-string comparison task was administered to French dyslexic children and two chronological age (CA) and reading age (RA)-matched control groups. Children had to judge whether two successively and briefly presented four-letter strings were identical or different. Letter-position and letter identity were manipulated through the transposition (e.g., RTGM vs. RMGT) or substitution of two letters (e.g., TSHF vs. TGHD). Non-words, pseudo-words, and words were used as stimuli to investigate sub-lexical and lexical effects on letter encoding. Dyslexic children showed both substitution and transposition detection problems relative to CA-controls. A substitution advantage over transpositions was only found for words in dyslexic children whereas it extended to pseudo-words in RA-controls and to all type of items in CA-controls. Letters were better identified in the dyslexic group when belonging to orthographically familiar strings. Letter-position encoding was very impaired in dyslexic children who did not show any word context effect in contrast to CA-controls. Overall, the current findings point to a strong letter identity and letter-position encoding disorder in developmental dyslexia.

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.

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.