Automatic Prognostic Determination and Evolution of Cognitive Decline using Artificial Neural Networks

Abstract. This work tries to go a step further in the development of methods based on automatic learning techniques to parse and interpret data relating to cognitive decline (CD). There have been studied the neuropsychological tests of 267 consultations made over 30 patients by the Alzheimer's Patient Association of Gran Canaria in 2005. The Sanger neural network adaptation for missing values treatment has allowed making a Principal Components Analysis (PCA) on the successfully obtained data. The results show that the first three obtained principal components are able to extract information relating to functional, cognitive and instrumental sintomatology, respectively, from the test. By means of these techniques, it is possible to develop tools that allow physicians to quantify, view and make a better pursuit of the sintomatology associated to the cognitive decline processes, contributing to a better knowledge of these ones

Evaluation of accelerometric and cycling cadence data for motion monitoring

Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well. AuthorResearch through the Development of Advanced Computational Algorithms for Evaluating Post-Surgery Rehabilitation [LTAIN19007]; National Sustainability Programme of the Ministry of Education, Youth and Sports of the Czech Republic [LO1303 (MSMT-7778/2014)]; Ethics commission, Neurocentre Caregroup, Center for Neurological Care in Rychnov nad Kneznou, Czech RepublicMinisterstvo Školství, Mládeže a Tělovýchovy, MŠMT: LO1303, MSMT-7778/201

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

La renovación de la palabra en el bicentenario de la Argentina : los colores de la mirada lingüística

El libro reúne trabajos en los que se exponen resultados de investigaciones presentadas por investigadores de Argentina, Chile, Brasil, España, Italia y Alemania en el XII Congreso de la Sociedad Argentina de Lingüística (SAL), Bicentenario: la renovación de la palabra, realizado en Mendoza, Argentina, entre el 6 y el 9 de abril de 2010. Las temáticas abordadas en los 167 capítulos muestran las grandes líneas de investigación que se desarrollan fundamentalmente en nuestro país, pero también en los otros países mencionados arriba, y señalan además las áreas que recién se inician, con poca tradición en nuestro país y que deberían fomentarse. Los trabajos aquí publicados se enmarcan dentro de las siguientes disciplinas y/o campos de investigación: Fonología, Sintaxis, Semántica y Pragmática, Lingüística Cognitiva, Análisis del Discurso, Psicolingüística, Adquisición de la Lengua, Sociolingüística y Dialectología, Didáctica de la lengua, Lingüística Aplicada, Lingüística Computacional, Historia de la Lengua y la Lingüística, Lenguas Aborígenes, Filosofía del Lenguaje, Lexicología y Terminología

Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach

Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell's membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks

Video processing and 3D modelling of chest movement using MS Kinect depth sensor

General methods of video processing and three dimensional modelling have a wide range of applications in engineering, archaeology and spacial objects study. The paper is devoted to applications of these methods in biomedicine and neurology using MS Kinect depth sensor for non-contact monitoring of breathing. A special attention is paid to visualization of results and motion mapping over the selected chest area. The proposed methodology applies digital signal processing methods and functional transforms for acquired data de-noising, spectral analysis, and feature selection. Suggested method uses further the local polynomial approxima-tion to detect extremal values of spectral components. The results verify the correspondence between the evaluations of the breathing frequency obtained from the thorax movement recorded by the depth sensor. The study proves that simple depth sensors can be used for non-contact detection of breathing frequency and for the three dimensional modelling of the chest movement. The proposed non-contact method enables to analyse breathing for diagnostic purposes and monitoring in the home environment as a component of assisted living technologies. General methodology studied form a contribu-tion to the use of video sequences or sets of images for spacial objects modelling, their recognition, possible three dimen-sional printing or analysis of time evolution of their features. © 2016 IEEE