Evaluation of the Computer-based Battery for Oral Language (BILOv3) through the Rasch model for a Brazilian Sample

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Surfactant-based transdermal system for fluconazole skin delivery

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic (data-based) learning of expressive indicators for machine failure detection : a proposal

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Visual histological grading system for the evaluation of in vitro-generated neocartilage

Here we present the development of a visual evaluation system for routine assessment of in vitro-engineered cartilaginous tissue. Neocartilage was produced by culturing human articular chondrocytes in pellet culture systems or in a scaffold-free bioreactor system. All engineered tissues were embedded in paraffin and were sectioned and stained with Safranin O-fast green. The evaluation of each sample was broken into 3 categories (uniformity and intensity of Safranin O stain, distance between cells/amount of matrix produced, and cell morphology), and each category had 4 components with a score ranging from 0 to 3. Three observers evaluated each sample, and the new system was independently tested against an objective computer-based histomorphometry system. Pellets were also assessed biochemically for glycosaminoglycan (GAG) content. Pellet histology scores correlated significantly with GAG contents and were in agreement with the computer-based histomorphometry system. This system allows a valid and rapid assessment of in vitro-generated cartilaginous tissue that has a relevant association with objective parameters indicative of cartilage quality.

The development and evaluation of a computer-based program to test and to teach the recognition of facial affect

Autism is a chronic pervasive neurodevelopmental disorder characterized by the early onset of social and communicative impairments as well as restricted, ritualized, stereotypic behavior. The endophenotype of autism includes neuropsychological deficits, for instance a lack of "Theory of Mind" and problems recognizing facial affect. In this study, we report the development and evaluation of a computer-based program to teach and test the ability to identify basic facially expressed emotions. 10 adolescent or adult subjects with high-functioning autism or Asperger-syndrome were included in the investigation. A priori the facial affect recognition test had shown good psychometric properties in a normative sample (internal consistency: rtt=.91-.95; retest reliability: rtt=.89-.92). In a prepost design, one half of the sample was randomly assigned to receive computer treatment while the other half of the sample served as control group. The training was conducted for five weeks, consisting of two hours training a week. The trained individuals improved significantly on the affect recognition task, but not on any other measure. Results support the usefulness of the program to teach the detection of facial affect. However, the improvement found is limited to a circumscribed area of social-communicative function and generalization is not ensured.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Towards an Open Source Framework for Collaborative Problem-Based Learning Environments

This paper presents our research works in the domain of Collaborative Environments centred on Problem Based Learning (PBL) and taking advantage of existing Electronic Documents. We first present the modelling and engineering problems that we want to address; then we discuss technological issues of such a research particularly the use of OpenUSS and of the Enterprise Java Open Source Architecture (EJOSA) to implement such collaborative PBL environments.