This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects’ circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.

Many deaf women face the lack of numerous resources related to their personal development. The unavailability of proper information on Sexual and Reproductive Health (SRH), in particular, causes problems of sexually transmitted infections, unwanted pregnancy in adolescence, sexual violence, complications during pregnancy, etc. In response to this, we have created a social network that delivers SRH content (verified and validated by experts) to women with different degrees of hearing loss. The site features a recommender system that selects the most relevant pieces of content to deliver to each woman, driven by her individual preferences, needs and levels of knowledge on the different subjects. We report experiments conducted in Cuenca, Ecuador, between 2017 and 2018 with 98 volunteers from low- and middle-income settings, aiming to evaluate the quality and appeal of the contents, the coherence of the methodology followed to create them, and the effectiveness of the content recommendations. The positive results encourage the frequent creation of new content and the refinement of the recommendation logic as the cohort of users expands over time.

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This report presents a review of the accessibility models in Learning Resources and MOOCs with the aim of establishing common terms in the research of the EduTech project and other projects associated with virtual accessibility in member HEIs. This study is based on the search and analysis of articles and publications related to the subject following the MLR format. The results showed a lack of applicability and data that support the current situation in Latin America, however, the experiences of European projects and regulations that support their sustainability, establish guidelines that could guide implementation processes in higher education institutions in partner countries

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This paper presents an approach to automatically generate personalized plans for Speech and Language Therapy on children, based on five speech and language areas: linguistic formulation, oral structure and function, receptive language, expressive language and hearing. The approach relies on an implementation of the Partition Around Medoids (PAM) algorithm, which takes into account the profile of each child, and generates multi-level (hierarchical) clusters based on several distance measures. The new plans are created as per a K-Nearest Neighbors (KNN) criterion, combining elements from the five areas. This approach has been used in the assessment of 53 children with different disabilities, with promising results verified by expert therapists.