Examinando por Autor "Figueroa, A."

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    Dopaminergic stimulation of myeloid antigen-presenting cells attenuates signal transducer and activator of transcription 3-activation favouring the development of experimental autoimmune encephalomyelitis
    (Frontiers Media, 2018-03) Prado, C.; Gaiazzi, M.; González, H.; Ugalde, V.; Figueroa, A.; Osorio-Barrios, F.J.; López, E.; Lladser, A.; Rasini, E.; Marino, F.; Zaffaroni, M.; Cosentino, M.; Pacheco, R.
    The dual potential to promote tolerance or inflammation to self-antigens makes dendritic cells (DCs) fundamental players in autoimmunity. Previous results have shown that stimulation of dopamine receptor D5 (DRD5) in DCs potentiates their inflammatory behaviour, favouring the development of experimental autoimmune encephalomyelitis (EAE). Here, we aimed to decipher the underlying mechanism and to test its relevance in multiple sclerosis (MS) patients. Our data shows that DRD5-deficiency confined to DCs in EAE mice resulted in reduced frequencies of CD4+ T-cell subsets with inflammatory potential in the central nervous system, including not only Th1 and Th17 cells but also granulocyte-macrophage colony-stimulating factor producers. Importantly, ex vivo depletion of dopamine from DCs resulted in a dramatic reduction of EAE severity, highlighting the relevance of an autocrine loop promoting inflammation in vivo. Mechanistic analyses indicated that DRD5-signalling in both mouse DCs and human monocytes involves the attenuation of signal transducer and activator of transcription 3-activation, a transcription factor that limits the production of the inflammatory cytokines interleukin (IL)-12 and IL-23. Furthermore, we found an exacerbated expression of all dopamine receptors in peripheral blood pro-inflammatory monocytes obtained from MS patients. These findings illustrate a novel mechanism by which myeloid antigen-presenting cells may trigger the onset of their inflammatory behaviour promoting the development of autoimmunity. © 2018 Prado, Gaiazzi, González, Ugalde, Figueroa, Osorio-Barrios, López, Lladser, Rasini, Marino, Zaffaroni, Cosentino and Pacheco.
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    Semi-supervised regression using diffusion on graphs
    (Elsevier Ltd, 2021-06) Timilsina, M.; Figueroa, A.; d'Aquin, M.; Yang, H.
    In 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)
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    Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members
    (IEEE, 2023) Schwarzenberg, P.; Figueroa, A.
    Promoting engagement and participation is vital for online social networks such as community Question-Answering (cQA) sites. One way of increasing the contribution of their members is by connecting their content with the right target audience. To achieve this goal, demographic analysis is pivotal in deciphering the interest of each community fellow. Indeed, demographic factors such as gender are fundamental in reducing the gender disparity across distinct topics. This work assesses the classification rate of assorted state-of-the-art transformer-based models (e.g., BERT and FNET) on the task of gender identification across cQA fellows. For this purpose, it benefited from a massive text-oriented corpus encompassing 548,375 member profiles including their respective full-questions, answers and self-descriptions. This assisted in conducting large-scale experiments considering distinct combinations of encoders and sources. Contrary to our initial intuition, in average terms, self-descriptions were detrimental due to their sparseness. In effect, the best transformer models achieved an AUC of 0.92 by taking full-questions and answers into account (i.e., DeBERTa and MobileBERT). Our qualitative results reveal that fine-tuning on user-generated content is affected by pre-training on clean corpora, and that this adverse effect can be mitigated by correcting the case of words.
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    The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members
    (Instituto Politecnico Nacional, 2018) Olivares, N.; Vivanco, L.M.; Figueroa, A.
    In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.