Abstract
The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of vehicles, in a simulated, augmented, or real environment. Such a framework brings forward the need for formal languages in which to specify the vehicles that compose a team, the scenario in which they will operate, and the mission to be performed. This paper introduces the Scenario Description Language (SDL) and the Team Description Language (TDL), two Extensible Markup Language based dialects that compose the static components necessary for representing scenario and mission knowledge. SDL provides a specification of physical scenario and global operational constraints, while TDL defines the team of vehicles, as well as team-specific operational restrictions. The dialects were defined using Extensible Markup Language schemas, with all required information being integrated in the definitions. An interface was developed and incorporated into the framework, allowing for the creation and edition of SDL and TDL files. Once the information is specified, it can be used in the framework, thus facilitating environment and team specification and deployment. A survey answered by practitioners and researchers shows that the satisfaction with SDL+TDL is elevated (the overall evaluation of SDL+TDL achieved a score of 4 out of 5, with 81%/78.6% of the answers 4); in addition, the usability of the interface was evaluated, achieving a score of 86.7 in the System Usability Scale survey. These results imply that SDL+TDL is flexible enough to represent scenarios and teams, through a user-friendly interface.

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Background: Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response- to-treatment classes.

Abstract
Clinical decisions are sometimes based on a variety of patient's information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors-computational algorithms that present a summarized description of image regions-became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures-e.g., intensity, texture, shape-in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer's disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.

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