BRAF Gene and Melanoma: Back to the Future

As widely acknowledged, 40–50% of all melanoma patients harbour an activating BRAF mutation (mostly BRAF V600E). The identification of the RAS–RAF–MEK–ERK (MAP kinase) signalling pathway and its targeting has represented a valuable milestone for the advanced and, more recently, for the completely resected stage III and IV melanoma therapy management. However, despite progress in BRAF-mutant melanoma treatment, the two different approaches approved so far for metastatic disease, immunotherapy and BRAF+MEK inhibitors, allow a 5-year survival of no more than 60%, and most patients relapse during treatment due to acquired mechanisms of resistance. Deep insight into BRAF gene biology is fundamental to describe the acquired resistance mechanisms (primary and secondary) and to understand the molecular pathways that are now being investigated in preclinical and clinical studies with the aim of improving outcomes in BRAF-mutant patients.     

Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...     

Use of scanning probe microscopy to study the evolution of nanometer sized liquid structures

The evolution of the profile of nanometer sized water drops on a mica surface has been studied through hydration scanning probe microscopy. A time range from a few seconds down to a fraction of millisecond after the formation of the drop has been explored. This high time resolution has been obtained by sampling a series of statistically equivalent drops. This approach also avoids any probe interference during the drop evolution process.     

Empirical evaluation of an educational game on software measurement

Software measurement is considered important in improving the software process. However, teaching software measurement remains a challenging issue. Although, games and simulations are regarded powerful tools for learning, their learning effectiveness is not rigorously established. This paper describes the results of an explorative study to investigate the learning effectiveness of a game prototype on software measurement in order to make an initial judgment about its potential as an educational tool as well as to analyze its appropriateness, engagement and strengths & weaknesses as guidance for further evolution. Within the study, a series of experiments was conducted in parallel in three master courses in Brazil. Results of the study reveal that the participants consider the content and structure of the game appropriate, but no indication for a significant difference on learning effectiveness could be shown.

Optimal-tuning PID control of adaptive materials for structural efficiency

The objective of this paper was solving the optimization problem of lightweight stiffened structures modelled as a two-dimensional domain in an efficient computational way. The underlying premise was that mass should be distributed in an efficient way, so as to use a minimum amount of material to accomplish the mechanical function. This premise was expressed as a global, multi-objective optimization problem in which stiffness and mass were conflicting objectives. Alternative local evolution rules were implemented to update mass density or Young’s modulus at each step of the iterative procedure. The solution of the structural optimization problem was accomplished by a novel automatic procedure consisting of two consecutive stages of control and optimization. In the first stage of Proportional Integral Derivative (PID) control gains were manually selected whereas in the second stage the finding of optimal values of control gains, target, and cost indices was allowed. In this study a bone-like material was adopted and a thin slab was analysed as a sample problem.     

Graph-based quadratic optimization: A fast evolutionary approach

Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, etc., and it is therefore of crucial importance to devise algorithmic solutions that are both efficient and effective. As it turns out, a large class of quadratic optimization problems can be formulated in terms of so-called “standard quadratic programs” (StQPs), which ask for finding the extrema of a quadratic polynomial over the standard simplex. Computationally, the standard approach for attacking this class of problems is to use replicator dynamics, a well-known family of algorithms from evolutionary game theory inspired by Darwinian selection processes. Despite their effectiveness in finding good solutions in a variety of applications, however, replicator dynamics suffer from being computationally expensive, as they require a number of operations per step which grows quadratically with the dimensionality of the problem being solved. In order to avoid this drawback, in this paper we propose a new population game dynamics (InImDyn) which is motivated by the analogy with infection and immunization processes within a population of “players.” We prove that the evolution of our dynamics is governed by a quadratic Lyapunov function, representing the average population payoff, which strictly increases along non-constant trajectories and that local solutions of StQPs are asymptotically stable (i.e., attractive) points. Each step of InImDyn is shown to have a linear time/space complexity, thereby allowing us to use it as a more efficient alternative to standard approaches for solving StQPs and related optimization problems. Indeed, we demonstrate experimentally that InImDyn is orders of magnitude faster than, and as accurate as, replicator dynamics on various applications ranging from tree matching to image registration, matching and segmentation.     

Preserving correctness during business process model configuration

A configurable process model captures a family of related process models in a single artifact. Such models are intended to be configured to fit the requirements of specific organizations or projects, leading to individualized process models that are subsequently used for domain analysis or solution design. This article proposes a formal foundation for individualizing configurable process models incrementally, while preserving correctness, both with respect to syntax and behavioral semantics. Specifically, assuming the configurable process model is behaviorally sound, the individualized process models are guaranteed to be sound. The theory is first developed in the context of Petri nets and then extended to a process modeling notation widely used in practice, namely Event-driven Process Chains.     

Sensitivity analysis of the rice model WARM in Europe: Exploring the effects of different locations,climates and methods of analysis on model sensitivity to crop parameters

Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation. This issue is addressed, in this study, as an application of sensitivity analysis techniques to a crop model in the Mediterranean region. In particular, an application of Morris and Sobol' sensitivity analysis methods to the rice model WARM is presented. The output considered is aboveground biomass at maturity, simulated at five rice districts of different countries (France, Greece, Italy, Portugal, and Spain) for years characterized by low, intermediate, and high continentality. The total effect index of Sobol' (that accounts for the total contribution to the output variation due a given parameter) and two Morris indices (mean μ and standard deviation σ of the ratios output changes/parameter variations) were used as sensitivity metrics. Radiation use efficiency (RUE), optimum temperature (T_opt), and leaf area index at emergence (LAI_ini) ranked in most of the combinations site × year as first, second and third most relevant parameters. Exceptions were observed, depending on the sensitivity method (e.g. LAI_ini resulted not relevant by the Morris method), or site-continentality pattern (e.g. with intermediate continentality in Spain, LAI_ini and T_opt were second and third ranked; with low continentality in Portugal, RUE was outranked by T_opt). Low σ values associated with the most relevant parameters indicated limited parameter interactions. The importance of sensitivity analyses by exploring site × climate combinations is discussed as pre-requisite to evaluate either novel crop-modelling approaches or the application of known modelling solutions to conditions not explored previously. The need of developing tools for sensitivity analysis within the modelling environment is also emphasized.     

APROMORE: An advanced process model repository

Business process models are becoming available in large numbers due to their widespread use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: how can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionality and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.