Home monitoring for older singles: A gas sensor array system

Many residential environments have been equipped with sensing technologies both to provide assistance to older people who have opted for aging-in-place and to provide information to caregivers and family. However, such technologies are often accompanied by physical discomfort, privacy concerns, and complexity of use. We explored the feasibility of monitoring home activity using chemical sensors that pose fewer privacy concerns than, for example, video-cameras and which do not suffer from blind spots. We built a monitoring device that integrates a sensor array and IoT capabilities to gather the necessary data about a resident in his/her living space. Over a period of 3 months, we uninterruptedly measured the living space of a typical elder person living on his/her own. To record the level of activity during the same period and obtain a ground truth for the activity, a set of motion sensors were also deployed in the house. Home activity was extracted from a PCA space moving-window which translated sensor data into the event space; this also compensated for environmental and sensor drift. Our results show that it is possible to monitor the person’s home activity and detect sudden deviations from it using a low-cost, non-invasive, system based on gas sensors that gather data on the air composition in the living space. We made the dataset publicly available at https://archive.ics.uci.edu/ml/index.php2.This work was supported by the Spanish Ministry of Economy and Competitiveness (www.mineco.gob.es) PID2021-122952OB-I00, DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII), Share4Rare project (Grant Agreement 780262), ISCIII (grant AC22/00035), ACCIÓ (grant Innotec ACE014/20/000018) and Pla de Doctorats Industrials de la Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (2022 DI 014), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (grant No. 101029808). JF also acknowledges the CERCA Program/Generalitat de Catalunya and the Serra Húnter Program. B2SLab is certified as 2017 SGR 952.Peer ReviewedPostprint (author's final draft

EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups—WNT, SHH, and non-WNT/non-SHH—directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application—EpiGe-App—that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.The study was supported by Associations of Parents and Families of Children with Cancer and by funding of the Spanish Ministry of for Science, Innovation and University (grant PI20/00519; PI CL) and the Foundation La Marató TV3 (grant 201921-30; PI CL). We acknowledge the multidisciplinary team who helped in the molecular analyses and care of patients, and the BioBank Hospital Sant Joan de Déu of the Spanish BioBank Network for sample procurement. We also acknowledge Marta Fortuny for communication strategy advice and Eduard Puig for legal assistance and data protection regulations. Authors acknowledge the SJD Fundraising Team.Peer ReviewedArticle signat per 23 autors/es: Soledad Gómez-González, Joshua Llano, Marta Garcia, Alicia Garrido-Garcia, Mariona Suñol, Isadora Lemos, Sara Perez-Jaume, Noelia Salvador, Nagore Gene-Olaciregui, Raquel Arnau Galán, Vicente Santa-María, Marta Perez-Somarriba, Alicia Castañeda, José Hinojosa, Ursula Winter, Francisco Barbosa Moreira, Fabiana Lubieniecki, Valeria Vazquez, Jaume Mora, Ofelia Cruz, Andrés Morales La Madrid, Alexandre Perera, Cinzia Lavarino.Postprint (published version