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Vocal behavior plays a crucial evolutionary role. In the case of birds, song is critically important in courtship, male-male competition and other key behaviors linked to reproduction. However, under natural conditions, a variety of avian species live in close proximity and share an 'acoustic landscape'. Therefore, they need to be able to differentiate their calls or songs from those of other species and also from those of other individuals of the same species. To do this efficiently, birds display a remarkable diversity of sounds. For example, in the case of vocal learners, such as oscine passerines (i.e. songbirds), complex sequences and subtle acoustic effects are produced through the generation of complex neuromuscular instructions driving the vocal organ, which is remarkably conserved across approximately 4000 oscine species. By contrast, the majority of the sister clade of oscines, the suboscine passerines, are thought not to be vocal learners. Despite this, different suboscine species can generate a rich variety of songs and quite subtle acoustic effects. In the last few years, different suboscine species have been shown to possess morphological adaptations that allow them to produce a diversity of acoustic characteristics. Here, we briefly review the mechanisms of sound production in birds, before considering three suboscine species in more detail. The examples discussed in this Review, integrating biological experiments and biomechanical modeling using non-linear dynamical systems, illustrate how a morphological adaptation can produce complex acoustic properties without the need for complex neuromuscular control.
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Aves Canoras , Som , Masculino , Animais , Aclimatação , Acústica , Evolução Biológica , CorteRESUMO
The brain constitutes a good example of a chaotic, nonlinear biological system where large neuronal networks operate chaotically with random connectivity. This critical state is significantly affected by the anesthetic loss of consciousness induced by drugs whose pharmacological behavior has been classically based on linear kinetics and dynamics. Recent developments in pharmacology and brain monitoring during anesthesia suggest a different view that we tried to explore in this article. The concepts of effect-site for hypnotic drugs modeling a maximum effect, electroencephalographic dynamics during induction, maintenance, and recovery from anesthesia are discussed, integrated into this alternative view, and how it may be applied in daily clinical practice.
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Anestesia , Anestésicos , Humanos , Encéfalo , Anestésicos/farmacologia , Estado de Consciência , EletroencefalografiaRESUMO
The shocking severity of the Covid-19 pandemic has woken up an unprecedented interest and accelerated effort of the scientific community to model and forecast epidemic spreading to find ways to control it regionally and between regions. Here we present a model that in addition to describing the dynamics of epidemic spreading with the traditional compartmental approach takes into account the social behaviour of the population distributed over a geographical region. The region to be modelled is defined as a two-dimensional grid of cells, in which each cell is weighted with the population density. In each cell a compartmental SEIRS system of delay difference equations is used to simulate the local dynamics of the disease. The infections between cells are modelled by a network of connections, which could be terrestrial, between neighbouring cells, or long range, between cities by air, road or train traffic. In addition, since people make trips without apparent reason, noise is considered to account for them to carry contagion between two randomly chosen distant cells. Hence, there is a clear separation of the parameters related to the biological characteristics of the disease from the ones that represent the spatial spread of infections due to social behaviour. We demonstrate that these parameters provide sufficient information to trace the evolution of the pandemic in different situations. In order to show the predictive power of this kind of approach we have chosen three, in a number of ways different countries, Mexico, Finland and Iceland, in which the pandemics have followed different dynamic paths. Furthermore we find that our model seems quite capable of reproducing the path of the pandemic for months with few initial data. Unlike similar models, our model shows the emergence of multiple waves in the case when the disease becomes endemic.
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Objectives: To understand and forecast the evolution of COVID-19 (Coronavirus disease 2019) in Chile, and analyze alternative simulated scenarios to better predict alternative paths, in order to implement policy solutions to stop the spread and minimize damage. Methods: We have specified a novel multi-parameter generalized logistic growth model, which does not only look at the trend of the data, but also includes explanatory covariates, using a quasi-Poisson regression specification to account for overdispersion of the count data. We fitted our model to data from the onset of the disease (February 28) until September 15. Estimating the parameters from our model, we predicted the growth of the epidemic for the evolution of the disease until the end of October 2020. We also evaluated via simulations different fictional scenarios for the outcome of alternative policies (those analyses are included in the Supplementary Material). Results and Conclusions: The evolution of the disease has not followed an exponential growth, but rather, stabilized and moved downward after July 2020, starting to increase again after the implementation of the Step-by-Step policy. The lockdown policy implemented in the majority of the country has proven effective in stopping the spread, and the lockdown-relaxation policies, however gradual, appear to have caused an upward break in the trend.
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COVID-19 , Epidemias , Chile/epidemiologia , Controle de Doenças Transmissíveis , Humanos , SARS-CoV-2RESUMO
Introducción: La evolución de un sistema dinámico se puede caracterizar a partir de la construcción de atractores caóticos. Objetivo: Desarrollar una metodología de evaluación de la saturación venosa de oxígeno, fundamentada en los sistemas dinámicos, para pacientes en Unidad de Cuidados Intensivos. Metodología: Se seleccionaron 10 pacientes con diferentes patologías de la Unidad de Cuidados Intensivos Postquirúrgicos, y registró la saturación venosa de oxígeno durante su tiempo de estancia. Con base en estos valores se construyeron atractores caóticos en el mapa de retardo y se evaluaron los valores mínimos y máximos ocupados por el atractor. Resultados: Se halló que la saturación venosa de oxígeno tiene un comportamiento caótico; los valores máximos y mínimos de los atractores en el mapa de retardo variaron entre 22,10 mmHg y 93,70 mmHg. Conclusiones: Se plantea una nueva metodología capaz de caracterizar el comportamiento de la variable monitorizada para la evaluación del paciente crítico(AU)
Introduction: The evolution of a dynamic system can be characterized from the construction of chaotic attractors. Objective: To develop a methodology based on dynamic systems, for the evaluation of venous oxygen saturation of patients in the Intensive Care Unit. Methodology: 10 patients with different pathologies of the Post-surgical Intensive Care Unit were selected, and recorded venous oxygen saturation during their time of stay. Based on these values, chaotic attractors were constructed on the delay map and the minimum and maximum values occupied by the attractor were evaluated. Results: It was found that venous oxygen saturation has a chaotic behavior; the maximum and minimum values of the attractors on the delay map varied between 22.10 mmHg and 93.70 mmHg. Conclusions: A new methodology is proposed capable of characterizing the behavior of this monitored variable for the evaluation of the critical patient(AU)
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Humanos , Masculino , Feminino , Oximetria , Fractais , Cuidados Críticos , Modelos Teóricos , Oxiemoglobinas/metabolismo , ColômbiaRESUMO
BACKGROUND AND OBJECTIVES: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting analysis can help in supporting the diagnosis and in monitoring the progress of the disease. This paper aims to evaluate the importance of different groups of features to model handwriting deficits that appear due to Parkinson's disease; and how those features are able to discriminate between Parkinson's disease patients and healthy subjects. METHODS: Features based on kinematic, geometrical and non-linear dynamics analyses were evaluated to classify Parkinson's disease and healthy subjects. Classifiers based on K-nearest neighbors, support vector machines, and random forest were considered. RESULTS: Accuracies of up to 93.1% were obtained in the classification of patients and healthy control subjects. A relevance analysis of the features indicated that those related to speed, acceleration, and pressure are the most discriminant. The automatic classification of patients in different stages of the disease shows κ indexes between 0.36 and 0.44. Accuracies of up to 83.3% were obtained in a different dataset used only for validation purposes. CONCLUSIONS: The results confirmed the negative impact of aging in the classification process when we considered different groups of healthy subjects. In addition, the results reported with the separate validation set comprise a step towards the development of automated tools to support the diagnosis process in clinical practice.
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Escrita Manual , Doença de Parkinson/fisiopatologia , Máquina de Vetores de Suporte , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Fenômenos Biomecânicos , Aprendizado Profundo , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Teóricos , Dinâmica não Linear , Reconhecimento Automatizado de Padrão , Reprodutibilidade dos Testes , TremorRESUMO
Neuronal signals are usually characterized in terms of their discharge rate, a description inadequate to account for the complex temporal organization of spike trains. Complex temporal properties, which are characteristic of neuronal systems, can only be described with the appropriate, complex mathematical tools. Here, I apply high order structure functions to the analysis of neuronal signals recorded from parkinsonian patients during functional neurosurgery, recovering multifractal properties. To achieve an accurate model of such multifractality is critical for understanding the basal ganglia, since other non-linear properties, such as entropy, depend on the fractal properties of complex systems. I propose a new approach to the study of neuronal signals: to study spiking activity in terms of the velocity of spikes, defining it as the inverse function of the instantaneous frequency. I introduce a neural field model that includes a non-linear gradient field, representing neuronal excitability, and a diffusive term to consider the physical properties of the electric field. Multifractality is present in the model for a range of diffusion coefficients, and multifractal temporal properties are mirrored into space. The model reproduces the behavior of human basal ganglia neurons and shows that it is like that of turbulent fluids. The results obtained from the model predict that passive electric properties of neuronal activity, including ephaptic coupling, are far more relevant to the human brain than what is usually considered: passive electric properties determine the temporal and spatial organization of neuronal activity in the neural tissue.
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RESUMO Objetivo: refletir sobre a educação de qualidade na Enfermagem como fenômeno complexo e multidimensional. Método: trata-se de um estudo teórico-reflexivo desenvolvido e sistematizado em cinco unidades de reflexão: ambientes de aprendizagem significativa; metodologias ativas; convivência em realidades distintas; Redes colaborativas; e, Abordagens complexas de intervenção. Resultados: a análise das unidades de reflexão, à luz do pensamento complexo, demonstraram que a aprendizagem, na contemporaneidade, não pode mais ser concebida como processo pontual e linear. A educação de qualidade está relacionada à ampliação das interações e associações sistêmicas e à capacidade de fortalecer a interlocução com a realidade complexa e em constante mudança. Conclusão: necessita-se transcender o paradigma da simplificação e considerar a complexidade intrínseca que se encontra no cerne da ciência.
RESUMEN Objetivo: reflexionar sobre la educación de calidad en la Enfermería como fenómeno complejo y multidimensional. Método: se trata de un estudio teórico-reflexivo desarrollado y sistematizado en cinco unidades de reflexión: ambientes de aprendizaje significativo, metodologías activas, convivencia en realidades distintas, redes colaborativas y abordajes complejos de intervención. Resultados: el análisis de las unidades de reflexión a la luz del pensamiento complejo demostró que el aprendizaje, en la contemporaneidad, no puede ser concebido como un proceso puntual y lineal. La educación de calidad está relacionada con la ampliación de las interacciones, las asociaciones sistémicas y con la capacidad de fortalecer la interlocución con la realidad compleja y en constante cambio. Conclusión: se necesita transcender el paradigma de la simplificación y considerar a la complejidad intrínseca que se encuentra en el cerne de la ciencia.
ABSTRACT Objective: to reflect on quality Nursing education as a complex and multidimensional phenomenon. Method: this is a theoretical-reflexive study developed and systematized into five units of reflection: significant learning environments; active methodologies; Interactions in different realities; collaborative networks; and complex intervention approaches. Results: based on complex thinking, an analysis of the units of reflection has shown that learning in the contemporaneity can no longer be conceived as a specific and linear process. Quality education is related to expanding systemic interactions and associations, and to the ability to strengthen its relation with a constantly changing complex reality. Conclusion: it is necessary to transcend the simplification paradigm, taking into account the intrinsic complexity that lies at the heart of science.
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Humanos , Dinâmica não Linear , Gestão da Qualidade Total , Universidades , Educação em EnfermagemRESUMO
Epileptic encephalopathies (EE) is a term coined by the International League Against Epilepsy (ILAE) to refer to a group of epilepsies in which the ictal and interictal abnormalities may contribute to progressive cerebral dysfunction. Among them, two affect mainly children and are very difficult to deal with, Doose and Lennox-Gastaut syndromes, (DS and LGS, respectively). So far (Zavala-Yoe et al., J Integr Neurosci 15(2):205-223, 2015a and works of ours there), quantitative analysis of single case studies of EE have been performed. All of them are manifestations of drug resistant epileptic encephalopathies (DREES) and as known, such disorders require a lot of EEG studies through all patient's life. As a consequence, dozens of EEG records are stored by parents and neurologists as time goes by. However, taking into account all this massive information, our research questions (keeping colloquial wording by parents) arise: a) Which zone of the brain has been the most affected so far? b) On which year was the child better? c) How bad is our child with respect to others? We must reflect that despite clinical assessment of the EEG has undergone standardization by establishment of guidelines such as the recently published guidelines of the American Clinical Neurophysiology Society (Tsuchida et al., J Clin Neurophysiol 4(33):301-302, 2016), qualitative EEG will never be as objective as quantitative EEG, since it depends largely on the education and experience of the conducting neurophysiologist (Grant et al., Epilepsy Behav 2014(32):102-107, 2014, Rating, Z Epileptologie, Springer Med 27(2):139-142, 2014). We already answered quantitatively the above mentioned questions in the references of ours given above where we provided entropy curves and an entropy index which encompasses the complexity of bunches of EEG making possible to deal with massive data and to make objective comparisons among some patients simultaneously. However, we have refined that index here and we also offer another two measures which are spatial and dynamic. Moreover, from those indices we also provide what we call a temporal dynamic complexity path which shows in a standard 10-20 system head diagram the evolution of the lowest complexity per brain zone with respect to the EEG period. These results make it possible to compare quantitatively/graphically the progress of several patients at the same time, answering the questions posed above. The results obtained showed that we can associate low spatio-temporal entropy indices to multiple seizures events in several patients at the same time as well as tracking seizure progress in space and time with our entropy path, coinciding with neurophysiologists observations.