Abstract
Nowadays, there are several communication gaps that isolate deaf people in several social activities. This work studies the expressiveness of gestures in Portuguese Sign Language (PSL) speakers and their differences between deaf and hearing people. It is a first effort towards the ultimate goal of understanding emotional and behaviour patterns among such populations. In particular, our work designs solutions for the following problems: (i) differentiation between deaf and hearing people, (ii) identification of different conversational topics based on body expressiveness, (iii) identification of different levels of mastery of PSL speakers through feature analysis. With these aims, we build up a complete and novel dataset that reveals the duo-interaction between deaf and hearing people under several conversational topics. Results show high recognition and classification rates.

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Locally advanced breast cancer (LABC) constitutes a heterogeneous entity that includes advanced-stage primary tumours, cancers with extensive nodal involvement and inflammatory breast carcinomas. Although the definition of LABC can be broadened to include some large operable breast tumours, we use this term to strictly refer to inoperable cancers that are included in the above-mentioned categories. The prognosis of such tumours is often unfavourable; despite aggressive treatment, many patients eventually develop distant metastases and die from the disease. Advances in systemic therapy, including radiation treatment, surgical techniques and the development of new targeted agents have significantly improved clinical outcomes for patients with this disease. Notwithstanding these advances, LABC remains an important clinical problem, particularly in developing countries and those without widely adapted breast cancer awareness programmes. The optimal management of LABC requires a multidisciplinary approach, a well-coordinated treatment schedule and close cooperation between medical, surgical and radiation oncologists. In this Review, we discuss the current state of the art and possible future treatment strategies for patients with LABC.

Abstract
Common breast cancer treatments, as the removal of axillary lymph nodes, cause severe impairments in women's upper-body function. As a result, several daily activities are affected which contributes to a decreased QOL. Thus, the assessment of functional restrictions after treatment is essential to avoid further complications. This paper presents a pioneer work, which aims to develop an upper-body function evaluation method, traduced by the identification of lymphedema. Using the Kinect, features of the upper-limbs motion are extracted and supervised learning algorithms are used to construct a predictive classification model. Very promising results are obtained, with high classification accuracy.