Error-Correcting Output Codes in the Framework of Deep Ordinal Classification

Automatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen’s Kappa or Spearman’s rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of RMSE=1.0797 for the Retinopathy dataset and RMSE=1.1237 for the Adience dataset averaged over 4 different architectures

An ordinal CNN approach for the assessment of neurological damage in Parkinson’s disease patients

3D image scans are an assessment tool for neurological damage in Parkinson’s disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional Neural Networks (CNNs) are good candidates, because they are effective when applied to spatial data. This paper proposes a 3D CNN ordinal model for assessing the level or neurological damage in PD patients. Given that CNNs need large datasets to achieve acceptable performance, a data augmentation method is adapted to work with spatial data. We consider the Ordinal Graph-based Oversampling via Shortest Paths (OGO-SP) method, which applies a gamma probability distribution for inter-class data generation. A modification of OGO-SP is proposed, the OGO-SP- algorithm, which applies the beta distribution for generating synthetic samples in the inter-class region, a better suited distribution when compared to gamma. The evaluation of the different methods is based on a novel 3D image dataset provided by the Hospital Universitario ‘Reina Sofía’ (Córdoba, Spain). We show how the ordinal methodology improves the performance with respect to the nominal one, and how OGO-SP- yields better performance than OGO-SP

Hackathon in teaching: Machine Learning applied to Life Sciences

La programación ha sido tradicionalmente una competencia perteneciente a las ingenierías, que recientemente está adquiriendo una importancia significativa en áreas como Ciencias de la Vida, donde resulta fundamental para la resolución de problemas de análisis de datos. Este trabajo es un caso de estudio enmarcado en la necesidad de mejorar las habilidades, sobre análisis de datos en el alumnado de Ciencias de la Vida y de la base temática en los estudiantes de ingeniería. Mediante la herramienta del hackathon y el trabajo en equipo, se combinó al alumnado de ambas disciplinas y se le enfrentó a una serie de problemas de análisis de datos. Se establecieron equipos de trabajo que recibieron una formación previa al comienzo de la competición. De cada equipo, se valoró la metodología empleada para la obtención de los datos, su análisis, interpretación de resultados, y exposición de las diversas tareas. Se hizo un análisis descriptivo de los resultados del Proyecto mediante encuestas al alumnado, así como su percepción sobre las actividades realizadas. El Proyecto ha conseguido que el alumnado resuelva los problemas planteados, difícilmente abordables con equipos unidisciplinares, generando un aprendizaje común y una experiencia multidisciplinar altamente satisfactoria tanto para el alumnado como para el profesorado.Programming has traditionally been an engineering competence, but recently it is acquiring significant importance in several areas, such as Life Sciences, which is considered essential for problem-solving based on data analysis. This work is a case study framed within the need to improve not only the data analysis skills of life science students, but also the biological background concerning the given issue of engineering students. Using hackathon and teamwork-based tools, students from both disciplines have been made and challenged with a series of problems in the area of Life Sciences. To solve these problems, we established work teams trained before the competition's beginning. Their results were assessed concerning the approach to obtain the data, perform the analysis, and finally interpret and present the results to solve the challenges. The project outcomes were assessed using structured surveys for students and their overall perception. The project succeeded, meaning students solved the proposed problems and achieved the activity's goals. These goals would have been difficult to address with teams composed of students from the same field of study. The hackathon succeeded in generating a shared learning and a multidisciplinary experience for their professional training, being highly rewarding for both students and faculty members

Monitoring of bluetongue virus in zoo animals in Spain, 2007–2019

Bluetongue (BT) is an emerging and re-emerging communicable vector-borne disease of animal health concern. A serosurvey was performed to assess exposure to BT virus (BTV) in zoo animals in Spain and to determine the dynamics of seropositivity in longitudinally sampled individuals during the study period. Serum samples were collected from 241 zoo animals belonging to 71 different species in five urban zoos (A-E) in Spain between 2007 and 2019. Twenty-four of these animals were longitudinally surveyed at three of the sampled zoos (zoos B, C and E) during the study period. Anti-BTV antibodies were found in 46 (19.1%; 95% CI: 14.1-24.1) of the 241 captive animals analysed by commercial ELISA. A virus neutralization test confirmed specific antibodies against BTV-1 and BTV-4 in 25 (10.7%; 95% CI: 6.7-14.6) and five (3.0%; 95% CI: 0.3-4.0) animals, respectively. Two of the 24 longitudinally sampled individuals (one African elephant (Loxodanta africana) and one aoudad (Ammotragus lervia)) showed anti-BTV antibodies at all samplings, whereas seroconversions were detected in one mouflon (Ovis aries musimon) in 2016, and one Asian elephant (Elephas maximus) in 2019. To the best of the authors' knowledge, this is the first large-scale survey on BTV conducted in both artiodactyl and non-artiodactyl zoo species worldwide. The results confirm BTV exposure in urban zoo parks in Spain, which could be of animal health and conservation concern. Circulation of BTV was detected in yearling animals in years when there were no reports of BTV outbreaks in livestock. Surveillance in artiodactyl and non-artiodactyl zoo species could be a valuable tool for epidemiological monitoring of BTV.info:eu-repo/semantics/acceptedVersio

Copper (II) Metallodendrimers Combined with Pro-Apoptotic siRNAs as a Promising Strategy Against Breast Cancer Cells

Cancer treatment with small interfering RNA (siRNA) is one of the most promising new strategies; however, transfection systems that increase its bioavailability and ensure its delivery to the target cell are necessary. Transfection systems may be just vehicular or could contain fragments with anticancer activity that achieves a synergistic effect with siRNA. Cationic carbosilane dendrimers have proved to be powerful tools as non-viral vectors for siRNA in cancer treatment, and their activity might be potentiated by the inclusion of metallic complexes in its dendritic structure. We have herein explored the interaction between Schiff-base carbosilane copper (II) metallodendrimers, and pro-apoptotic siRNAs. The nanocomplexes formed by metallodendrimers and different siRNA have been examined for their zeta potential and size, and by transmission electron microscopy, fluorescence polarisation, circular dichroism, and electrophoresis. The internalisation of dendriplexes has been estimated by flow cytometry and confocal microscopy in a human breast cancer cell line (MCF-7), following the ability of these metallodendrimers to deliver the siRNA into the cell. Finally, in vitro cell viability experiments have indicated effective interactions between Cu (II) dendrimers and pro-apoptotic siRNAs: Mcl-1 and Bcl-2 in breast cancer cells. Combination of the first-generation derivatives with chloride counterions and with siRNA increases the anticancer activity of the dendriplex constructs and makes them a promising non-viral vector.Polish National Agency for Academic Exchange (NAWA)European CommissionMinisterio de Economía y CompetitividadComunidad de MadridJunta de Comunidades de Castilla-La Manch

Empowering the Data Scientist professional profile through competition dynamics

La Ciencia de Datos es el área que comprende el desarrollo de métodos científicos, procesos y sistemas para extraer conocimiento a partir de datos recopilados previamente, con el objetivo de analizar los procedimientos llevados a cabo actualmente. El perfil profesional asociado a este campo es el del Científico de Datos, generalmente llevado a cabo por Ingenieros Informáticos gracias a que las aptitudes y competencias adquiridas durante su formación se ajustan perfectamente a lo requerido en este puesto laboral. Debido a la necesidad de formación de nuevos Científicos de Datos, entre otros fines, surgen plataformas en las que éstos pueden adquirir una amplia experiencia, como es el caso de Kaggle. El principal objetivo de esta experiencia docente es proporcionar al alumnado una experiencia práctica con un problema real, así como la posibilidad de cooperar y competir al mismo tiempo. Así, la adquisición y el desarrollo de las competencias necesarias en Ciencia de Datos se realiza en un entorno altamente motivador. La realización de actividades relacionadas con este perfil ha tenido una repercusión directa sobre el alumnado, siendo fundamental la motivación, la capacidad de aprendizaje y el reciclaje continuo de conocimientos a los que se someten los Ingenieros Informáticos.Data Science is the area that comprises the development of scientific methods, processes, and systems for extracting knowledge from previously collected data, aiming to analyse the procedures being carried out currently. The professional profile associated with this field is the Data Scientist, generally carried out by Computer Engineers as the skills and competencies acquired during their training are perfectly suited to what this job requires. Due to the need for training new Data Scientists, among other goals, there are different emerging platforms where they can acquire extensive experience, such as Kaggle. The main objective of this teaching experience is to provide students with practical experience on a real problem, as well as the possibility of cooperating and competing at the same time. Thus, the acquisition and development of the necessary competencies in Data Science are carried out in a highly motivating environment. The development of activities related to this profile has had a direct impact on the students, being fundamental the motivation, the learning capacity and the continuous recycling of knowledge to which Computer Engineers are subjected

Real-life outcomes in biotypes of psychotic disorders based on neurocognitive performance

Producción CientíficaAiming at discerning potential biotypes within the psychotic syndrome, we have recently reported the possible existence of two clusters or biotypes across schizophrenia and bipolar disorder characterized by their cognitive performance using the Brief Assessment of Cognition in Schizophrenia (BACS) instrument and validated with independent biological and clinical indexes (Fernández-Linsenbarth et al. in Schizophr Res 229:102–111, 2021). In this previous work, the group with larger cognitive deficits (N = 93, including 69 chronic schizophrenia, 17 first episodes (FE) of schizophrenia and 7 bipolar disorder patients) showed smaller thalamus and hippocampus volume and hyper-synchronic electroencephalogram than the group with milder deficits (N = 105, including 58 chronic schizophrenia, 25 FE and 22 bipolar disorder patients). We predicted that if these biotypes indeed corresponded to different cognitive and biological substrates, their adaptation to real life would be different. To this end, in the present work we have followed up the patients’ population included in that work at 1st and 3rd years after the date of inclusion in the 2021 study and we report on the statistical comparisons of each clinical and real-life outcomes between them. The first cluster, with larger cognitive deficits and more severe biological alterations, showed during that period a decreased capacity for job tenure (1st and 3rd years), more admissions to a psychiatric ward (1st year) and a higher likelihood for quitting psychiatric follow-up (3rd year). Patients in the second cluster, with moderate cognitive deficits, were less compliant with prescribed treatment at the 3rd year. The differences in real-life outcomes may give additional external validity to that yielded by biological measurements to the described biotypes based on neurocognition.Instituto de Salud Carlos III (grant ID PI18/00178)Dirección Regional de Salud de Castilla y León (grant ID GRS 2121/A/2020)Junta de Castilla y León - predoctoral grants from the Consejería de Educación and the European Social Fund (grant IDs VA-183-18 to IFL and VA- 223-19 to RMBRS)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

El recurso Pixton como herramienta TIC de innovación docente desde una perspectiva multidisciplinar: historia, geografía, lengua y matemáticas

Memoria ID-051 Ayudas de la Universidad de Salamanca para la innovación docente, curso 2020-2021

Segmentación y clasificación en la toma de decisiones

Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEpu