Abstract
The urge to improve the life of older adults grows as this segment of society expands. Computers have an enormous potential to benefit the lives of older adults, however, the unawareness or disregard of their characteristics, renders technology, many times, impossible to use. Peripherals are a common obstacle when learning to operate computers, because the most common ones do not directly map the input in the user interface. It has been argued that touch- and gesture-based user interfaces, due to their direct mapping of input, can reduce the obstacles that older adults face, when using the computer. To assess this, this paper presents a project that uses a multi-touch tabletop system as a gaming platform for older adults. Specifically, it reports on the low-fidelity prototype that was built to test whether tangible objects can be used. Conclusions regarding the viability of tangible objects for that purpose are also drawn.

Abstract
Locating components which are responsible for observed failures is the most expensive, error-prone phase in the software development life cycle. We present an Eclipse Plug-in that aims to fill some of the automatic debugging tools gaps: the lack of a visualization tool that provides intuitive feedback about the defect distribution over the code base, and easy access to the faulty locations. Copyright 2011 ACM.

Abstract
The use of traditional input devices such as keyboards and mice can become a huge obstacle for older adults in interacting with computer systems. Using Natural User Interfaces (NUI's), more specifically using gestures or movements on a multi-touch device, can be a good alternative to overcome these dificulties. This paper analyses the state of the art and identifies a set of criteria relevant to classify the projects in this area. The resulting classification enables us to recognize research opportunities on Natural User Interfaces, and namely multi-touch interfaces for elderly.

Abstract
Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic. This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography (before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical model capable of estimating, with satisfactory results, the postsurgical outcome.

Abstract
The relation between patient and physician in most modern Health Care Systems is sparse, limited in time and very inflexible. On the other hand, and in contradiction with several recent studies, most physicians do not rely their patient diagnostics evaluations on intertwined psychological and social nature factors. Facing these problems and trying to improve the patient/physician relation we present a mobile health care solution to improve the interaction between the physician and his patients. The solution serves not only as a privileged mean of communication between physicians and patients but also as an evolutionary intelligent platform delivering a mobile rule based system.

Abstract
Protein aggregation became a widely accepted marker of many polyQ disorders, including Machado-Joseph disease (MJD), and is often used as readout for disease progression and development of therapeutic strategies. The lack of good platforms to rapidly quantify protein aggregates in a wide range of disease animal models prompted us to generate a novel image processing application that automatically identifies and quantifies the aggregates in a standardized and operator-independent manner. We propose here a novel image processing tool to quantify the protein aggregates in a Caenorhabditis elegans (C. elegans) model of MJD. Confocal microscopy images were obtained from animals of different genetic conditions. The image processing application was developed using MeVisLab as a platform to process, analyse and visualize the images obtained from those animals. All segmentation algorithms were based on intensity pixel levels. The quantification of area or numbers of aggregates per total body area, as well as the number of aggregates per animal were shown to be reliable and reproducible measures of protein aggregation in C. elegans. The results obtained were consistent with the levels of aggregation observed in the images. In conclusion, this novel imaging processing application allows the non-biased, reliable and high throughput quantification of protein aggregates in a C. elegans model of MJD, which may contribute to a significant improvement on the prognosis of treatment effectiveness for this group of disorders.