Building an ontology catalogue for smart cities

Apart from providing semantics and reasoning power to data, ontologies enable and facilitate interoperability across heterogeneous systems or environments. A good practice when developing ontologies is to reuse as much knowledge as possible in order to increase interoperability by reducing heterogeneity across models and to reduce development effort. Ontology registries, indexes and catalogues facilitate the task of finding, exploring and reusing ontologies by collecting them from different sources. This paper presents an ontology catalogue for the smart cities and related domains. This catalogue is based on curated metadata and incorporates ontology evaluation features. Such catalogue represents the first approach within this community and it would be highly useful for new ontology developments or for describing and annotating existing ontologies

Semi-supervised regression using diffusion on graphs

Indexación ScopusIn real-world machine learning applications, unlabeled training data are readily available, but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning algorithms have gathered much attention. Previous studies in this area mainly focused on a semi-supervised classification problem, whereas semi-supervised regression has received less attention. In this paper, we proposed a novel semi-supervised regression algorithm using heat diffusion with a boundary-condition that guarantees a closed-form solution. Experiments from artificial and real datasets from business, biomedical, physical, and social domain show that the boundary-based heat diffusion method can effectively outperform the top state of the art methods. © 2021 The Author(s)https://www-sciencedirect-com.recursosbiblioteca.unab.cl/science/article/pii/S1568494621001113?via%3Dihu

c-Maf-positive spinal cord neurons are critical elements of a dorsal horn circuit for mechanical hypersensitivity in neuropathy

Corticospinal tract (CST) neurons innervate the deep spinal dorsal horn to sustain chronic neuropathic pain. The majority of neurons targeted by the CST are interneurons expressing the transcription factor c-Maf. Here, we used intersectional genetics to decipher the function of these neurons in dorsal horn sensory circuits. We find that excitatory c-Maf (c-Maf(EX)) neurons receive sensory input mainly from myelinated fibers and target deep dorsal horn parabrachial projection neurons and superficial dorsal horn neurons, thereby connecting non-nociceptive input to nociceptive output structures. Silencing c-Maf(EX) neurons has little effect in healthy mice but alleviates mechanical hypersensitivity in neuropathic mice. c-Maf(EX) neurons also receive input from inhibitory c-Maf and parvalbumin neurons, and compromising inhibition by these neurons caused mechanical hypersensitivity and spontaneous aversive behaviors reminiscent of c-Maf(EX) neuron activation. Our study identifies c-Maf(EX) neurons as normally silent second-order nociceptors that become engaged in pathological pain signaling upon loss of inhibitory control

Adaptation knowledge acquisition: a case study for case-based decision support in oncology

KASIMIR is a case-based decision support system in the domain of breast cancer treatment. For this system, a problem is given by the description of a patient and a solution is a set of therapeutic decisions. Given a target problem, KASIMIR provides several suggestions of solutions, based on several justified adaptations of source cases. Such adaptation processes are based on adaptation knowledge. The acquisition of this kind of knowledge from experts is presented in this paper. It is shown how the decomposition of adaptation processes by introduction of intermediate problems can highlight simple and generalizable adaptation steps. Moreover, some adaptation knowledge units that are generalized from the ones acquired for KASIMIR are presented. This knowledge can be instantiated in other case- based decision support systems, in particular in medicine