Automated cement segmentation in vertebroplasty

Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.

Geochemical and nannofossil analyses of Cenomanian/Turonian sediments of DSDP Hole 75-530A

One of the key objectives of Deep Sea Drilling Project (DSDP) Leg 75 was to shed light on the underlying causes of Cretaceous oceanic anoxia in the South Atlantic by addressing two major hypotheses: productivity productivity-driven anoxia vs. enhanced ocean stratification leading to preservation of organic matter and black shale deposition. Here we present a detailed geochemical dataset from sediments deposited during the Cenomanian/Turonian (C/T) transition and the global oceanic anoxic event 2 (OAE 2) at DSDP Site 530A, located off-shore Namibia (southeast Angola Basin, north of Walvis Ridge). To characterise the succession of alternating black and green shales at this site and to reconstruct the evolution of their paleoenvironmental setting, we have combined data derived from investigations on bulk organic matter, biomarkers and the inorganic fraction. The location of the C/T boundary itself is biostratigraphically not well constrained due to the carbonate-poor (but organic matter-rich) facies of these sediments. The bulk d13Corg record and compound-specific d13C data, in combination with published as well as new biostratigraphic data, enabled us to locate more precisely the C/T boundary at DSDP Site 530A. The compound-specific d13C record is the first of this kind reported from C/T black shales in the South Atlantic. It is employed for paleoenvironmental reconstructions and chemostratigraphic correlation to other C/T sections in order to discuss the paleoceanographic aspects and implications of the observations at DSDP Site 530A in a broader context, e.g., with regard to the potential trigger mechanisms of OAE 2, global changes in black shale deposition and climate. On a stratigraphic level, an approximation and monitoring of the syndepositional degree of oxygen depletion within the sediments/bottom waters in comparison to the upper water column is achieved by comparing normalised concentrations of redox-sensitive trace elements with the abundance of highly source specific molecular compounds. These biomarkers are derived from photoautotrophic and simultaneously anoxygenic green sulphur bacteria (Chlorobiacea) and are interpreted as paleoindicators for events of photic zone euxinia. In contrast to a number of other OAE 2 sections that are characterised by continuous black shale sequences, DSDP Site 530A represents a highly dynamic setting where newly deposited black shales were repeatedly exposed to conditions of subtle bottom water re-oxidation, presumably leading to their progressive alteration into green shales. The frequent alternation between both facies and the related anoxic to slight oxygenated conditions can be best explained by variations in vertical extent of an oxygen minimum zone in response to changes in a highly productive western continental margin setting driven by upwelling.

Seaweed - epiphyte - mesograzer communities were tested for their responses to elevated seawater temperature and [CO2] in benthic mesocosms experiments across four consecutive seasons of one year in Kiel, Germany

Rising seawater temperature and CO2 concentrations (ocean acidification) represent two of the most influential factors impacting marine ecosystems in the face of global climate change. In ecological climate change research full-factorial experiments across seasons in multi-species, cross-trophic level set-ups are essential as they allow making realistic estimations about direct and indirect effects and the relative importance of both major environmental stressors on ecosystems. In benthic mesocosm experiments we tested the responses of coastal Baltic Sea Fucus vesiculosus communities to elevated seawater temperature and CO2 concentrations across four seasons of one year. While increasing [CO2] levels only had minor effects, warming had strong and persistent effects on grazers which affected the Fucus community differently depending on season. In late summer a temperature-driven collapse of grazers caused a cascading effect from the consumers to the foundation species resulting in overgrowth of Fucus thalli by epiphytes. In fall/ winter, outside the growing season of epiphytes, intensified grazing under warming resulted in a significant reduction of Fucus biomass. Thus, we confirm the prediction that future increasing water temperatures influence marine food-web processes by altering top-down control, but we also show that specific consequences for food-web structure depend on season. Since Fucus vesiculosus is the dominant habitat-forming brown algal system in the Baltic Sea, its potential decline under global warming implicates the loss of key functions and services such as provision of nutrient storage, substrate, food, shelter and nursery grounds for a diverse community of marine invertebrates and fish in Baltic Sea coastal waters.

Uso de DES y RANS no estacionario para la simulación del flujo alrededor de una carena Wigley - Use of Unsteady RANS and DES to simulate flows around a Wigley Hull)

Computer Fluid Dynamics tools have already become a valuable instrument for Naval Architects during the ship design process, thanks to their accuracy and the available computer power. Unfortunately, the development of RANSE codes, generally used when viscous effects play a major role in the flow, has not reached a mature stage, being the accuracy of the turbulence models and the free surface representation the most important sources of uncertainty. Another level of uncertainty is added when the simulations are carried out for unsteady flows, as those generally studied in seakeeping and maneuvering analysis and URANS equations solvers are used. Present work shows the applicability and the benefits derived from the use of new approaches for the turbulence modeling (Detached Eddy Simulation) and the free surface representation (Level Set) on the URANS equations solver CFDSHIP-Iowa. Compared to URANS, DES is expected to predict much broader frequency contents and behave better in flows where boundary layer separation plays a major role. Level Set methods are able to capture very complex free surface geometries, including breaking and overturning waves. The performance of these improvements is tested in set of fairly complex flows, generated by a Wigley hull at pure drift motion, with drift angle ranging from 10 to 60 degrees and at several Froude numbers to study the impact of its variation. Quantitative verification and validation are performed with the obtained results to guarantee their accuracy. The results show the capability of the CFDSHIP-Iowa code to carry out time-accurate simulations of complex flows of extreme unsteady ship maneuvers. The Level Set method is able to capture very complex geometries of the free surface and the use of DES in unsteady simulations highly improves the results obtained. Vortical structures and instabilities as a function of the drift angle and Fr are qualitatively identified. Overall analysis of the flow pattern shows a strong correlation between the vortical structures and free surface wave pattern. Karman-like vortex shedding is identified and the scaled St agrees well with the universal St value. Tip vortices are identified and the associated helical instabilities are analyzed. St using the hull length decreases with the increase of the distance along the vortex core (x), which is similar to results from other simulations. However, St scaled using distance along the vortex cores shows strong oscillations compared to almost constants for those previous simulations. The difference may be caused by the effect of the free-surface, grid resolution, and interaction between the tip vortex and other vortical structures, which needs further investigations. This study is exploratory in the sense that finer grids are desirable and experimental data is lacking for large α, especially for the local flow. More recently, high performance computational capability of CFDSHIP-Iowa V4 has been improved such that large scale computations are possible. DES for DTMB 5415 with bilge keels at α = 20º were conducted using three grids with 10M, 48M and 250M points. DES analysis for flows around KVLCC2 at α = 30º is analyzed using a 13M grid and compared with the results of DES on the 1.6M grid by. Both studies are consistent with what was concluded on grid resolution herein since dominant frequencies for shear-layer, Karman-like, horse-shoe and helical instabilities only show marginal variation on grid refinement. The penalties of using coarse grids are smaller frequency amplitude and less resolved TKE. Therefore finer grids should be used to improve V&V for resolving most of the active turbulent scales for all different Fr and α, which hopefully can be compared with additional EFD data for large α when it becomes available.

Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Higher-order regularization and morphological techniques for image segmentation

La segmentación de imágenes es un campo importante de la visión computacional y una de las áreas de investigación más activas, con aplicaciones en comprensión de imágenes, detección de objetos, reconocimiento facial, vigilancia de vídeo o procesamiento de imagen médica. La segmentación de imágenes es un problema difícil en general, pero especialmente en entornos científicos y biomédicos, donde las técnicas de adquisición imagen proporcionan imágenes ruidosas. Además, en muchos de estos casos se necesita una precisión casi perfecta. En esta tesis, revisamos y comparamos primero algunas de las técnicas ampliamente usadas para la segmentación de imágenes médicas. Estas técnicas usan clasificadores a nivel de pixel e introducen regularización sobre pares de píxeles que es normalmente insuficiente. Estudiamos las dificultades que presentan para capturar la información de alto nivel sobre los objetos a segmentar. Esta deficiencia da lugar a detecciones erróneas, bordes irregulares, configuraciones con topología errónea y formas inválidas. Para solucionar estos problemas, proponemos un nuevo método de regularización de alto nivel que aprende información topológica y de forma a partir de los datos de entrenamiento de una forma no paramétrica usando potenciales de orden superior. Los potenciales de orden superior se están popularizando en visión por computador, pero la representación exacta de un potencial de orden superior definido sobre muchas variables es computacionalmente inviable. Usamos una representación compacta de los potenciales basada en un conjunto finito de patrones aprendidos de los datos de entrenamiento que, a su vez, depende de las observaciones. Gracias a esta representación, los potenciales de orden superior pueden ser convertidos a potenciales de orden 2 con algunas variables auxiliares añadidas. Experimentos con imágenes reales y sintéticas confirman que nuestro modelo soluciona los errores de aproximaciones más débiles. Incluso con una regularización de alto nivel, una precisión exacta es inalcanzable, y se requeire de edición manual de los resultados de la segmentación automática. La edición manual es tediosa y pesada, y cualquier herramienta de ayuda es muy apreciada. Estas herramientas necesitan ser precisas, pero también lo suficientemente rápidas para ser usadas de forma interactiva. Los contornos activos son una buena solución: son buenos para detecciones precisas de fronteras y, en lugar de buscar una solución global, proporcionan un ajuste fino a resultados que ya existían previamente. Sin embargo, requieren una representación implícita que les permita trabajar con cambios topológicos del contorno, y esto da lugar a ecuaciones en derivadas parciales (EDP) que son costosas de resolver computacionalmente y pueden presentar problemas de estabilidad numérica. Presentamos una aproximación morfológica a la evolución de contornos basada en un nuevo operador morfológico de curvatura que es válido para superficies de cualquier dimensión. Aproximamos la solución numérica de la EDP de la evolución de contorno mediante la aplicación sucesiva de un conjunto de operadores morfológicos aplicados sobre una función de conjuntos de nivel. Estos operadores son muy rápidos, no sufren de problemas de estabilidad numérica y no degradan la función de los conjuntos de nivel, de modo que no hay necesidad de reinicializarlo. Además, su implementación es mucho más sencilla que la de las EDP, ya que no requieren usar sofisticados algoritmos numéricos. Desde un punto de vista teórico, profundizamos en las conexiones entre operadores morfológicos y diferenciales, e introducimos nuevos resultados en este área. Validamos nuestra aproximación proporcionando una implementación morfológica de los contornos geodésicos activos, los contornos activos sin bordes, y los turbopíxeles. En los experimentos realizados, las implementaciones morfológicas convergen a soluciones equivalentes a aquéllas logradas mediante soluciones numéricas tradicionales, pero con ganancias significativas en simplicidad, velocidad y estabilidad. ABSTRACT Image segmentation is an important field in computer vision and one of its most active research areas, with applications in image understanding, object detection, face recognition, video surveillance or medical image processing. Image segmentation is a challenging problem in general, but especially in the biological and medical image fields, where the imaging techniques usually produce cluttered and noisy images and near-perfect accuracy is required in many cases. In this thesis we first review and compare some standard techniques widely used for medical image segmentation. These techniques use pixel-wise classifiers and introduce weak pairwise regularization which is insufficient in many cases. We study their difficulties to capture high-level structural information about the objects to segment. This deficiency leads to many erroneous detections, ragged boundaries, incorrect topological configurations and wrong shapes. To deal with these problems, we propose a new regularization method that learns shape and topological information from training data in a nonparametric way using high-order potentials. High-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher order potential defined over many variables is computationally infeasible. We use a compact representation of the potentials based on a finite set of patterns learned fromtraining data that, in turn, depends on the observations. Thanks to this representation, high-order potentials can be converted into pairwise potentials with some added auxiliary variables and minimized with tree-reweighted message passing (TRW) and belief propagation (BP) techniques. Both synthetic and real experiments confirm that our model fixes the errors of weaker approaches. Even with high-level regularization, perfect accuracy is still unattainable, and human editing of the segmentation results is necessary. The manual edition is tedious and cumbersome, and tools that assist the user are greatly appreciated. These tools need to be precise, but also fast enough to be used in real-time. Active contours are a good solution: they are good for precise boundary detection and, instead of finding a global solution, they provide a fine tuning to previously existing results. However, they require an implicit representation to deal with topological changes of the contour, and this leads to PDEs that are computationally costly to solve and may present numerical stability issues. We present a morphological approach to contour evolution based on a new curvature morphological operator valid for surfaces of any dimension. We approximate the numerical solution of the contour evolution PDE by the successive application of a set of morphological operators defined on a binary level-set. These operators are very fast, do not suffer numerical stability issues, and do not degrade the level set function, so there is no need to reinitialize it. Moreover, their implementation is much easier than their PDE counterpart, since they do not require the use of sophisticated numerical algorithms. From a theoretical point of view, we delve into the connections between differential andmorphological operators, and introduce novel results in this area. We validate the approach providing amorphological implementation of the geodesic active contours, the active contours without borders, and turbopixels. In the experiments conducted, the morphological implementations converge to solutions equivalent to those achieved by traditional numerical solutions, but with significant gains in simplicity, speed, and stability.

Analysis of the discharge capacity of radial-gated spillways using CFD and ANN - Oliana Dam case study.

The paper focuses on the analysis of radial-gated spillways, which is carried out by the solution of a numerical model based on the finite element method (FEM). The Oliana Dam is considered as a case study and the discharge capacity is predicted both by the application of a level-set-based free-surface solver and by the use of traditional empirical formulations. The results of the analysis are then used for training an artificial neural network to allow real-time predictions of the discharge in any situation of energy head and gate opening within the operation range of the reservoir. The comparison of the results obtained with the different methods shows that numerical models such as the FEM can be useful as a predictive tool for the analysis of the hydraulic performance of radial-gated spillways.

An O(N log N) algorithm for shape modeling.

We present a shape-recovery technique in two dimensions and three dimensions with specific applications in modeling anatomical shapes from medical images. This algorithm models extremely corrugated structures like the brain, is topologically adaptable, and runs in O(N log N) time, where N is the total number of points in the domain. Our technique is based on a level set shape-recovery scheme recently introduced by the authors and the fast marching method for computing solutions to static Hamilton-Jacobi equations.

The electron-furfural scattering dynamics for 63 energetically open electronic states

We report on integral-, momentum transfer-and differential cross sections for elastic and electronically inelastic electron collisions with furfural (C5H4O2). The calculations were performed with two different theoretical methodologies, the Schwinger multichannel method with pseudopotentials (SMCPP) and the independent atom method with screening corrected additivity rule (IAM-SCAR) that now incorporates a further interference (I) term. The SMCPP with N energetically open electronic states (N-open) at either the static-exchange (N-open ch-SE) or the static-exchange-plus-polarisation (N-open ch-SEP) approximation was employed to calculate the scattering amplitudes at impact energies lying between 5 eV and 50 eV, using a channel coupling scheme that ranges from the 1ch-SEP up to the 63ch-SE level of approximation depending on the energy considered. For elastic scattering, we found very good overall agreement at higher energies among our SMCPP cross sections, our IAM-SCAR+I cross sections and the experimental data for furan (a molecule that differs from furfural only by the substitution of a hydrogen atom in furan with an aldehyde functional group). This is a good indication that our elastic cross sections are converged with respect to the multichannel coupling effect for most of the investigated intermediate energies. However, although the present application represents the most sophisticated calculation performed with the SMCPP method thus far, the inelastic cross sections, even for the low lying energy states, are still not completely converged for intermediate and higher energies. We discuss possible reasons leading to this discrepancy and point out what further steps need to be undertaken in order to improve the agreement between the calculated and measured cross sections. (C) 2016 AIP Publishing LLC.

Calmness modulus of linear semi-infinite programs

Our main goal is to compute or estimate the calmness modulus of the argmin mapping of linear semi-infinite optimization problems under canonical perturbations, i.e., perturbations of the objective function together with continuous perturbations of the right-hand side of the constraint system (with respect to an index ranging in a compact Hausdorff space). Specifically, we provide a lower bound on the calmness modulus for semi-infinite programs with unique optimal solution which turns out to be the exact modulus when the problem is finitely constrained. The relationship between the calmness of the argmin mapping and the same property for the (sub)level set mapping (with respect to the objective function), for semi-infinite programs and without requiring the uniqueness of the nominal solution, is explored, too, providing an upper bound on the calmness modulus of the argmin mapping. When confined to finitely constrained problems, we also provide a computable upper bound as it only relies on the nominal data and parameters, not involving elements in a neighborhood. Illustrative examples are provided.

Kidney dysfunction and hypertension: Role for cadmium, P450 and heme oxygenases?

Cadmium (Cd) is a metal toxin of continuing worldwide concern. Daily intake of Cd, albeit in small quantities, is associated with a number of adverse health effects which are attributable to distinct pathological changes in a variety of tissues and organs. In the present review, we focus on its renal tubular effects in people who have been exposed environmentally to Cd at levels below the provisional tolerable intake level set for the toxin. We highlight the data linking such low-level Cd intake with tubular injury, altered abundance of cytochromes P450 (CYPs) in the kidney and an expression of a hypertensive phenotype. We provide updated knowledge on renal and vascular effects of the eicosanoids 20-hydroxyeicosatetraenoic acid (20-HETE) and eicosatrienoic acids (EETs), which are biologically active metabolites from arachidonate metabolism mediated by certain CYPs in the kidney. We note the ability of Cd to elicit oxidative stress and to alter metal homeostasis notably of zinc which may lead to augmentation of the defense mechanisms involving induction of the antioxidant enzyme heme oxygenase-1 (HO-1) and the metal binding protein metallothionein (MT) in the kidney. We hypothesize that renal Cd accumulation triggers the host responses mediated by HO-I and MT in an attempt to protect the kidney against injurious oxidative stress and to resist a rise in blood pressure levels. This hypothesis predicts that individuals with less active HO-1 (caused by the HO-1 genetic polymorphisms) are more likely to have renal injury and express a hypertensive phenotype following chronic ingestion of low-level Cd, compared with those having more active HO-1. Future analytical and molecular epidemiologic research should pave the way to the utility of induction of heme oxygenases together with dietary antioxidants in reducing the risk of kidney injury and hypertension in susceptible people.

Evaluation of degree of conversion and hardness of dental composites photo-activated with different light guide tips

Objective: The aim of this study was to evaluate the degree of conversion and hardness of different composite resins, photo-activated for 40 s with two different light guide tips, fiber optic and polymer. Methods: Five specimens were made for each group evaluated. The percentage of unreacted carbon double bonds (% C═C) was determined from the ratio of absorbance intensities of aliphatic C═C (peak at 1637 cm−1) against internal standard before and after curing of the specimen: aromatic C-C (peak at 1610 cm−1). The Vickers hardness measurements were performed in a universal testing machine. A 50 gf load was used and the indenter with a dwell time of 30 seconds. The degree of conversion and hardness mean values were analyzed separately by ANOVA and Tukey’s test, with a significance level set at 5%. Results: The mean values of degree of conversion for the polymer and fiber optic light guide tip were statistically different (P<.001). The hardness mean values were statistically different among the light guide tips (P<.001), but also there was difference between top and bottom surfaces (P<.001). Conclusions: The results showed that the resins photo-activated with the fiber optic light guide tip promoted higher values for degree of conversion and hardness.

Expressão imuno-histoquímica das proteínas E-Caderina e BCL2 e nevos melanociticos orais e cutâneos

Melanocytic nevi (MNs) are benign melanocytic proliferations of cells, which can be found in the skin and mucous coat, including the oral mucosa. However, skin NMs are more common when compared to those that affect the oral mucosa. The molecular mechanisms involved in the development of nevi and the factors that can influence the migration pattern of the nevus cells are little explored. The aim of this study was to analyze the immunohistochemical expression of E-cadherin protein and Bcl-2 in oral / skin NMs and relate them to the clinical characteristics (gender, age, location, exposure to solar radiation) and histopathological types. 36 cases of oral NMs and 34 Skin NMs were analyzed. The immunohistochemistry was used of the protein E-cadherin and bcl-2, which were analyzed the intensity (weak, moderate and strong) and distribution marking (diffuse and focal). The immunoreactivity also analyzed as to the types of nevus cells (epithelioid cells -A, -B lymphocyte and fibroblast-like -C). Statistical analysis was performed using the chi-square tests of Pearson and Spearman correlation with significance level set at 5%. Of the 70 cases of NMs, 82.9% were female, 48.6% aged 26-50 years, 51.4% were diagnosed histologically as intradermal / intramucosal nevi and 80% were NMs acquired. Immunohistochemical expression of BCL2 and E-cadherin were variables in the sample and showed no association with clinical parameters. The expression of bcl-2 and E-cadherin were variable according to the types of nevus cells (A, B and C) (P = 0.001). The expression of bcl-2 was more diffuse in congenital MNs (p = 0.002). E-cadherin was positive in 83.3% of MNs <1cm (p = 0.001) and exhibited weak staining in 73.9% of MNs that were in exposed areas (p = 0.010). Based on these results, it is suggested that the E-cadherin has a modulating effect on the migratory properties of NMs, and bcl-2 is a marker of MNs with increased proliferative capacity.

Simultaneous material and structural optimization by multiscale topology optimization

Acknowledgments The authors acknowledge the support from Engineering and Physical Sciences Research Council, grant number EP/M002322/1. The authors would also like to thank Numerical Analysis Group at the Rutherford Appleton Laboratory for their FORTRAN HSL packages (HSL, a collection of Fortran codes for large-scale scientific computation. See http://www.hsl.rl.ac.uk/).