RESUMEN
During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLE's debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of today's solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment. Results show that a better accuracy can be obtained in outdoor locations with respect to indoor ones, and that indoor proximity estimation can benefit more from the introduction of more features with respect to the outdoor estimation case. Accuracy can be increased about 10% when multiple features are considered if the device is aware of its environment, reaching a performance of up to 83% in indoor spaces and up to 91% in outdoor ones. These results encourage future contact tracing apps to integrate this awareness not only to better assess the associated risk of a given environment but also to improve the proximity accuracy for detecting close contacts.
RESUMEN
A new cross-correlation synchrony index for neural activity is proposed. The index is based on the integration of the kernel estimation of the cross-correlation function. It is used to test for the dynamic synchronization levels of spontaneous neural activity under two induced brain states: sleep-like and awake-like. Two bootstrap resampling plans are proposed to approximate the distribution of the test statistics. The results of the first bootstrap method indicate that it is useful to discern significant differences in the synchronization dynamics of brain states characterized by a neural activity with low firing rate. The second bootstrap method is useful to unveil subtle differences in the synchronization levels of the awake-like state, depending on the activation pathway.
Asunto(s)
Encéfalo/fisiología , Fenómenos Electrofisiológicos/fisiología , Neuronas/fisiología , Potenciales de Acción , Algoritmos , Animales , Gatos , Simulación por Computador , Sincronización Cortical , Electrodos Implantados , Electroencefalografía , Distribución de Poisson , Sueño/fisiología , Corteza Visual/fisiología , Vigilia/fisiologíaRESUMEN
BACKGROUND: Pairwise association between neurons is a key feature in understanding neural coding. Statistical neuroscience provides tools to estimate and assess these associations. In the mammalian brain, activating ascending pathways arise from neuronal nuclei located at the brainstem and at the basal forebrain that regulate the transition between sleep and awake neuronal firing modes in extensive regions of the cerebral cortex, including the primary visual cortex, where neurons are known to be selective for the orientation of a given stimulus. In this paper, the estimation of neural synchrony as a function of time is studied in data obtained from anesthetized cats. A functional data analysis of variance model is proposed. Bootstrap statistical tests are introduced in this context; they are useful tools for the study of differences in synchrony strength regarding 1) transition between different states (anesthesia and awake), and 2) affinity given by orientation selectivity. RESULTS: An analysis of variance model for functional data is proposed for neural synchrony curves, estimated with a cross-correlation based method. Dependence arising from the experimental setting needs to be accounted for. Bootstrap tests allow the identification of differences between experimental conditions (modes of activity) and between pairs of neurons formed by cells with different affinities given by their preferred orientations. In our test case, interactions between experimental conditions and preferred orientations are not statistically significant. CONCLUSIONS: The results reflect the effect of different experimental conditions, as well as the affinity regarding orientation selectivity in neural synchrony and, therefore, in neural coding. A cross-correlation based method is proposed that works well under low firing activity. Functional data statistical tools produce results that are useful in this context. Dependence is shown to be necessary to account for, and bootstrap tests are an appropriate method with which to do so.
Asunto(s)
Potenciales de Acción , Análisis de Varianza , Modelos Neurológicos , Modelos Estadísticos , Neuronas/fisiología , Procesamiento de Señales Asistido por Computador , Algoritmos , Animales , Prosencéfalo Basal/fisiología , Tronco Encefálico/fisiología , Gatos , Estimulación Eléctrica , Microelectrodos , Estimulación Luminosa , Corteza Visual/fisiología , Percepción Visual/fisiologíaRESUMEN
Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.
Asunto(s)
Biomarcadores de Tumor/metabolismo , Puntos de Control del Ciclo Celular/genética , Diferenciación Celular/genética , Daño del ADN/genética , Células Madre Embrionarias/metabolismo , Regulación Neoplásica de la Expresión Génica/genética , Neoplasias/clasificación , Neoplasias/metabolismo , Algoritmos , Biomarcadores de Tumor/genética , Neoplasias de la Mama/genética , Neoplasias de la Mama/metabolismo , Diferenciación Celular/efectos de los fármacos , Línea Celular Tumoral , Proliferación Celular/efectos de los fármacos , Proliferación Celular/genética , Centrosoma/metabolismo , Estudios de Cohortes , Bases de Datos Genéticas , Perfilación de la Expresión Génica , Humanos , Concentración 50 Inhibidora , Proteínas de la Membrana/genética , Proteínas de la Membrana/metabolismo , Mutación , Neoplasias/genética , Neoplasias/mortalidad , Pronóstico , Modelos de Riesgos Proporcionales , Receptor ErbB-2/genética , Receptor ErbB-2/metabolismo , Proteínas de Unión a Retinoblastoma/genética , Proteína p53 Supresora de Tumor/genética , Ubiquitina-Proteína Ligasas/genéticaRESUMEN
La Federación Nacional de Neurocirugía (FNNC) fue fundada en la ciudad de Rosario el 23 de agosto de 2019, con el objetivo de crear una organización que nu-clee a todas las sociedades y asociaciones de neurociru-gía de nuestro país, con un perfil netamente gremial. La FNNC reconoce a la Asociación Argentina de Neuroci-rugía (AANC) como la organización que rige la activi-dad académica-profesional de la neurocirugía en todo el territorio de la República Argentina.Uno de los propósitos principales es lograr condiciones laborales dignas y correctamente remuneradas para to-dos los neurocirujanos que residan en suelo argentino. Pensamos que una de las herramientas necesarias para conseguir dicho propósito es lograr un equilibrio entre el número de neurocirujanos en actividad, el número de plazas de residentes, y el número de habitantes en Ar-gentina.Actualmente no existen datos concretos para realizar un diagnóstico de situación. Es por ello que la FNNC, apoyada y avalada por la AANC, tomó la iniciativa de realizar un análisis descriptivo de la situación actual y su proyección en los próximos años en relación con el nú-mero de neurocirujanos en Argentina.El objetivo del presente trabajo es presentar de forma sintética y clara los aspectos más relevantes de dicho es-tudio.