Comparación entre el análisis 2-D y el Método de la Densidad de Fuerzas (discreto) para el equilibrio en estructuras de membrana

This paper deals with the equilibrium problem of a membrane, presenting a comparison between the well-known 1-D discrete Force Density Method, using spatial cable networks as membrane surface approximations, and the 2-D continuous analysis of such a surface. Although Force Density is a practical and powerful method in structural membrane design, it will be checked that the 2-D continuous analysis is not only more accurate and general but necessary for new structural membrane applications such as footbridges. In this way, once summarized the discrete Density Force Method, the continuous approach is presented. Then, a comparison process between both methods is proposed, being developed for specific membrane examples. Finally, some conclusions are pointed out.Este trabajo analiza el problema del equilibrio de una membrana y propone una comparación entre el conocido Método de la Densidad de Fuerzas, discreto y unidimensional (1-D), que aproxima la superficie de la membrana mediante una red espacial de cables, y el método continuo y bidimensional (2-D) sobre la propia superficie. Aunque el Método de la Densidad de Fuerzas representa una estrategia práctica y útil en el diseño de estructuras de membrana, se comprobará que el análisis continuo bidimensional no solo es más preciso y general sino que es más fiable, especialmente en aquellos casos en los que la membrana es el mismo tablero de una estructura portante (por ejemplo, una pasarela). En particular, una vez resumido el Método de la Densidad de Fuerzas, se planteará el problema continuo del equilibrio de membrana. A continuación se definirá un proceso de comparación entre ambos métodos, analizándolo por medio de ejemplos concretos. Finalmente, se señalarán algunas conclusiones

A Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's Disease

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3\% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).TIC218, MINECO TEC2008-02113 and TEC2012-34306 projects, Consejería de Economía, Innovación, Ciencia y Empleo de la Junta de Andalucía P09-TIC-4530 and P11-TIC-71

Mammalian Adaptation of an Avian Influenza A Virus Involves Stepwise Changes in NS1

Influenza A viruses (IAVs) are common pathogens of birds that occasionally establish endemic infections in mammals. The processes and mechanisms that result in IAV mammalian adaptation are poorly understood. The viral non-structural 1 (NS1) protein counteracts the interferon (IFN) response, a central component of the host-species barrier. We characterised the NS1 proteins of equine influenza virus (EIV), a mammalian IAV lineage of avian origin. We showed that evolutionary distinct NS1s counteract the IFN response using different and mutually exclusive mechanisms: while the NS1s of early EIVs block general gene expression by binding to the cellular polyadenylation specific factor 30 (CPSF30), NS1s from more evolved EIVs specifically block the induction of IFN-stimulated genes by interfering with the JAK/STAT pathway. These contrasting anti-IFN strategies are associated with two mutations that appeared sequentially and became rapidly selected during EIV evolution, highlighting the importance of evolutionary processes on immune evasion mechanisms during IAV adaptation

Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Number of years of participation in some, but not all, types of physical activity during adolescence predicts level of physical activity in adulthood: Results from a 13-year study

Abstract: Background: Adolescent physical activity (PA) levels track into adulthood. However it is not known if type of PA participated in during adolescence is associated with PA levels later in life. We aimed to identify natural groupings of types of PA and to assess whether number of years participating in these different groupings during adolescence is related to PA level in early adulthood. Methods: 673 adolescents in Montreal, Canada, age 12–13 years at baseline (54 % female), reported participation in 29 physical activities every 3 months over 5 years (1999–2005). They also reported their PA level at age 24 years (2011–12). PA groupings among the 29 physical activities were identified using factor analysis. The association between number of years participating in each grouping during adolescence and PA level at age 24 was estimated using linear regression within a general estimating equation framework. Results: Three PA groupings were identified: “sports”, “fitness and dance”, and “running”. There was a positive linear relationship between number of years participating in sports and running in adolescence and PA level at age 24 years (β (95 % confidence interval) = 0.09 (0.04-0.15); 0.08 (0.01-0.15), respectively). There was no relationship between fitness and dance in adolescence and PA level at age 24. Conclusions: The association between PA participation in adolescence and PA levels in young adulthood may be specific to certain PA types and to consistency of participation during adolescence. Results suggest that efforts to establish the habit of participation in sports and running in adolescence may promote higher PA levels in adulthood