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1.
Anticancer Res ; 40(9): 5181-5189, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32878806

RESUMO

BACKGROUND/AIM: Mathematical models have long been considered as important tools in cancer biology and therapy. Herein, we present an advanced non-linear mathematical model that can predict accurately the effect of an anticancer agent on the growth of a solid tumor. MATERIALS AND METHODS: Advanced non-linear mathematical optimization techniques and human-to-mouse experimental data were used to develop a tumor growth inhibition (TGI) estimation model. RESULTS: Using this mathematical model, we could accurately predict the tumor mass in a human-to-mouse pancreatic ductal adenocarcinoma (PDAC) xenograft under gemcitabine treatment up to five time periods (points) ahead of the last treatment. CONCLUSION: The ability of the identified TGI dynamic model to perform satisfactory short-term predictions of the tumor growth for up to five time periods ahead was investigated, evaluated and validated for the first time. Such a prediction model could not only assist the pre-clinical testing of putative anticancer agents, but also the early modification of a chemotherapy schedule towards increased efficacy.


Assuntos
Antineoplásicos/farmacologia , Modelos Teóricos , Dinâmica não Linear , Ensaios Antitumorais Modelo de Xenoenxerto , Algoritmos , Animais , Antineoplásicos/administração & dosagem , Antineoplásicos/farmacocinética , Carcinoma Ductal Pancreático/tratamento farmacológico , Carcinoma Ductal Pancreático/patologia , Proliferação de Células/efeitos dos fármacos , Modelos Animais de Doenças , Humanos , Camundongos , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/patologia
2.
Nonlinear Dynamics Psychol Life Sci ; 24(4): 475-497, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32960758

RESUMO

This study explored the chaotic properties of human emotions as expressed in social media and its implications for attainable forecasting horizons. Three human emotional states extracted from Twitter were analyzed using the nonlinear dynamics approach. The greatest positive Lyapunov exponent (LE) and 0-1 test methods were applied to a time series set consisting of over 25,000 data points reflecting the hourly recorded data of over 1.3 million tweets. The results suggest that the examined emotional time series data represent a nonlinear dynamical system with deterministic chaos properties. Therefore, by utilizing traditional linear methods of social media data analysis, one may not be able to fully understand and forecast critical transition trends over time or beyond a limited duration. It was concluded that the nonlinear dynamics approach is useful to determine a feasible forecasting horizon and to assess the prediction accuracy of social media data in general.


Assuntos
Emoções , Dinâmica não Linear , Mídias Sociais , Humanos
3.
PLoS One ; 15(8): e0235668, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32776932

RESUMO

In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.


Assuntos
Algoritmos , Eletricidade , Dinâmica não Linear , Simulação por Computador , Centrais Elétricas , Software
4.
Sci Total Environ ; 746: 141261, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-32745866

RESUMO

Although injury is a leading cause of death worldwide, the association between ambient temperature and injury has received little research attention compared to the association of temperature with mortality and morbidity from non-external causes. With current climate change and increases in weather extremes, assessing the association between temperature and injury is important for determining public health priorities. Therefore, the present study examined the association between ambient temperature and injury risk with a focus on the intentions and mechanisms of injury. Using the national emergency database, we identified a total of 703,503 injured patients who had visited emergency departments in Seoul, South Korea from 2008 to 2016. We conducted a time-stratified case-crossover study using a conditional Poisson regression model, and applied a distributed lag nonlinear model to explore possible nonlinear and delayed effects of daily mean temperature on injury risk. Injury risk was significantly associated with ambient temperature, and temperature-injury association curves markedly differed with respect to intentions and mechanisms of injury. Although unintentional injuries increased significantly at both high and low temperatures, intentional injuries - including self-harm and assault - significantly increased only at high temperatures. The mechanism-specific analyses showed that injuries caused by traffic accidents and burns significantly increased at both high and low temperatures. However, injuries caused by all other mechanisms (i.e., fall, blunt object, machinery, penetration, and poisoning) significantly increased only at high temperatures, while injury due to slipping increased at low temperatures. Our study provides evidence that ambient temperature is associated with risk of injury, and this association differs depending on the intentions and mechanisms of injury. Overall, our findings help foster a more comprehensive understanding of the association between temperature and injury that can be used to establish appropriate public health policies and targeted interventions.


Assuntos
Intenção , Dinâmica não Linear , Temperatura Baixa , Estudos Cross-Over , Temperatura Alta , Humanos , República da Coreia/epidemiologia , Seul , Temperatura
5.
Medicine (Baltimore) ; 99(32): e21469, 2020 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-32769879

RESUMO

Influenza is an acute respiratory infectious disease that poses a threat to public health. We assessed the association between atmospheric visibility and influenza and influenza-like illness (ILI) in Wuxi city, China.Daily meteorological data, ILI activity, and influenza virus infection rates were collected between 31 December 2012 and 31 December 2017. A distributed lag non-linear model (DLNM) was used to analyze the exposure-lag-response of ILI and influenza activity and daily average visibility.A total of 12,800 cases were detected; 1046 cases (8.17%) were of Flu-A and 527 (4.12%) were of Flu-B infection. Our analysis suggested a non-linear relationship between atmospheric visibility and influenza: U-shaped for ILI, and L-shaped for Flu-A and Flu-B. Comparing low visibility (2.5 km) to ILI cases, the risk appeared between day 1 and day 2. For Flu-A, the risk appeared between days 5 and 9, whereas for Flu-B, the risk effect was much stronger and had a longer reaction delay, staying above zero until day 9. The protective effects of high visibility (14 km) on ILI and Flu-B occurred the same day or one day later. However, we found no association between high visibility and Flu-A.In conclusion, our study contributes novel evidence for the effects of atmospheric visibility on influenza. These findings are important for the development of influenza surveillance and early warning systems in Wuxi city.


Assuntos
Influenza Humana/epidemiologia , Influenzavirus A , Influenzavirus B , Conceitos Meteorológicos , China , Humanos , Influenza Humana/diagnóstico , Dinâmica não Linear , Estações do Ano
6.
Phys Rev Lett ; 125(5): 058103, 2020 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-32794851

RESUMO

Many complex systems, ranging from migrating cells to animal groups, exhibit stochastic dynamics described by the underdamped Langevin equation. Inferring such an equation of motion from experimental data can provide profound insight into the physical laws governing the system. Here, we derive a principled framework to infer the dynamics of underdamped stochastic systems from realistic experimental trajectories, sampled at discrete times and subject to measurement errors. This framework yields an operational method, Underdamped Langevin Inference, which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems, including flocks with Viscek-like alignment interactions. Our method is robust to experimental measurement errors, and includes a self-consistent estimate of the inference error.


Assuntos
Modelos Teóricos , Movimento , Animais , Comportamento Animal/fisiologia , Movimento Celular/fisiologia , Poeira , Modelos Biológicos , Modelos Químicos , Movimento/fisiologia , Dinâmica não Linear , Densidade Demográfica
7.
Nat Commun ; 11(1): 4229, 2020 08 25.
Artigo em Inglês | MEDLINE | ID: mdl-32843631

RESUMO

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011-2018 was twice that in 2004-2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29-2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO2 (r = 0.21) and O3 (r = 0.11). A 10 µg/m3 increase of NO2 and O3 was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02-1.10) and 1.04 (95% CI: 1.01-1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO2 and O3 may be associated with an increased risk of scarlet fever incidence, but direct causality is not established.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Exposição Ambiental/análise , Escarlatina/diagnóstico , Poluição do Ar/efeitos adversos , China/epidemiologia , Exposição Ambiental/efeitos adversos , Geografia , Humanos , Incidência , Dióxido de Nitrogênio/análise , Dinâmica não Linear , Ozônio/análise , Material Particulado/análise , Fatores de Risco , Escarlatina/epidemiologia , Escarlatina/etiologia , Estações do Ano , Análise Espaço-Temporal
8.
Nat Commun ; 11(1): 3854, 2020 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-32782263

RESUMO

The synchronization of human networks is essential for our civilization and understanding its dynamics is important to many aspects of our lives. Human ensembles were investigated, but in noisy environments and with limited control over the network parameters which govern the network dynamics. Specifically, research has focused predominantly on all-to-all coupling, whereas current social networks and human interactions are often based on complex coupling configurations. Here, we study the synchronization between violin players in complex networks with full and accurate control over the network connectivity, coupling strength, and delay. We show that the players can tune their playing period and delete connections by ignoring frustrating signals, to find a stable solution. These additional degrees of freedom enable new strategies and yield better solutions than are possible within current models such as the Kuramoto model. Our results may influence numerous fields, including traffic management, epidemic control, and stock market dynamics.


Assuntos
Relações Interpessoais , Modelos Psicológicos , Comportamento Social , Rede Social , Feminino , Frustração , Humanos , Masculino , Dinâmica não Linear
9.
Braz J Microbiol ; 51(3): 1109-1115, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32809115

RESUMO

COVID-19 has killed more than 500,000 people worldwide and more than 60,000 in Brazil. Since there are no specific drugs or vaccines, the available tools against COVID-19 are preventive, such as the use of personal protective equipment, social distancing, lockdowns, and mass testing. Such measures are hindered in Brazil due to a restrict budget, low educational level of the population, and misleading attitudes from the federal authorities. Predictions for COVID-19 are of pivotal importance to subsidize and mobilize health authorities' efforts in applying the necessary preventive strategies. The Weibull distribution was used to model the forecast prediction of COVID-19, in four scenarios, based on the curve of daily new deaths as a function of time. The date in which the number of daily new deaths will fall below the rate of 3 deaths per million - the average level in which some countries start to relax the stay-at-home measures - was estimated. If the daily new deaths curve was bending today (i.e., about 1250 deaths per day), the predicted date would be on July 5. Forecast predictions allowed the estimation of overall death toll at the end of the outbreak. Our results suggest that each additional day that lasts to bend the daily new deaths curve may correspond to additional 1685 deaths at the end of COVID-19 outbreak in Brazil (R2 = 0.9890). Predictions of the outbreak can be used to guide Brazilian health authorities in the decision-making to properly fight COVID-19 pandemic.


Assuntos
Infecções por Coronavirus/epidemiologia , Previsões/métodos , Pneumonia Viral/epidemiologia , Algoritmos , Brasil/epidemiologia , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/prevenção & controle , Detergentes/provisão & distribução , Educação/estatística & dados numéricos , Humanos , Análise dos Mínimos Quadrados , Dinâmica não Linear , Pandemias/prevenção & controle , Pneumonia Viral/mortalidade , Pneumonia Viral/prevenção & controle , Política , Densidade Demográfica , Pobreza , Fatores Socioeconômicos , Estatística como Assunto , Fatores de Tempo , Abastecimento de Água/normas
10.
Rev Med Virol ; 30(5): e2140, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32686248

RESUMO

A knowledge-based cybernetic framework model representing the dynamics of SARS-CoV-2 inside the human body has been studied analytically and in silico to explore the pathophysiologic regulations. The following modeling methodology was developed as a platform to introduce a predictive tool supporting a therapeutic approach to Covid-19 disease. A time-dependent nonlinear system of ordinary differential equations model was constructed involving type-I cells, type-II cells, SARS-CoV-2 virus, inflammatory mediators, interleukins along with host pulmonary gas exchange rate, thermostat control, and mean pressure difference. This formalism introduced about 17 unknown parameters. Estimating these unknown parameters requires a mathematical association with the in vivo sparse data and the dynamic sensitivities of the model. The cybernetic model can simulate a dynamic response to the reduced pulmonary alveolar gas exchange rate, thermostat control, and mean pressure difference under a very critical condition based on equilibrium (steady state) values of the inflammatory mediators and system parameters. In silico analysis of the current cybernetical approach with system dynamical modeling can provide an intellectual framework to help experimentalists identify more active therapeutic approaches.


Assuntos
Betacoronavirus/patogenicidade , Infecções por Coronavirus/imunologia , Interações Hospedeiro-Patógeno/imunologia , Pulmão/imunologia , Dinâmica não Linear , Pneumonia Viral/imunologia , Proteínas da Fase Aguda/antagonistas & inibidores , Proteínas da Fase Aguda/genética , Proteínas da Fase Aguda/imunologia , Anti-Inflamatórios/uso terapêutico , Antivirais/uso terapêutico , Betacoronavirus/efeitos dos fármacos , Betacoronavirus/crescimento & desenvolvimento , Temperatura Corporal , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/patologia , Infecções por Coronavirus/virologia , Citocinas/antagonistas & inibidores , Citocinas/genética , Citocinas/imunologia , Células Epiteliais/efeitos dos fármacos , Células Epiteliais/imunologia , Células Epiteliais/virologia , Regulação da Expressão Gênica , Interações Hospedeiro-Patógeno/efeitos dos fármacos , Interações Hospedeiro-Patógeno/genética , Humanos , Pulmão/efeitos dos fármacos , Pulmão/virologia , Macrófagos Alveolares/efeitos dos fármacos , Macrófagos Alveolares/imunologia , Macrófagos Alveolares/virologia , Pandemias , Peptidil Dipeptidase A/genética , Peptidil Dipeptidase A/imunologia , Pneumonia Viral/tratamento farmacológico , Pneumonia Viral/patologia , Pneumonia Viral/virologia , Troca Gasosa Pulmonar/efeitos dos fármacos , Troca Gasosa Pulmonar/imunologia , Glicoproteína da Espícula de Coronavírus/antagonistas & inibidores , Glicoproteína da Espícula de Coronavírus/genética , Glicoproteína da Espícula de Coronavírus/imunologia
11.
BMC Bioinformatics ; 21(1): 324, 2020 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-32693778

RESUMO

BACKGROUND: Modern developments in single-cell sequencing technologies enable broad insights into cellular state. Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden our understanding of cellular heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data. However, methods have yet to be developed for unfiltered and unnormalized count data that estimate uncertainty in the low-dimensional space. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data. RESULTS: Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student's t-distribution to estimate a manifold that is robust to technical and biological noise found in normalized scRNA-seq data. We compare our approach to common dimension reduction tools across a diverse set of scRNA-seq data sets to highlight our model's ability to enable important downstream tasks such as clustering, inferring cell developmental trajectories, and visualizing high throughput experiments on available experimental data. CONCLUSION: We show that our adaptive robust statistical approach to estimate a nonlinear manifold is well suited for raw, unfiltered gene counts from high-throughput sequencing technologies for visualization, exploration, and uncertainty estimation of cell states.


Assuntos
Dinâmica não Linear , RNA-Seq , Análise de Célula Única/métodos , Células Sanguíneas/metabolismo , Regulação da Expressão Gênica , Humanos , Modelos Genéticos , Neurônios/metabolismo , Distribuição Normal , Análise de Componente Principal , Fatores de Tempo
12.
J Chromatogr A ; 1625: 461326, 2020 Aug 16.
Artigo em Inglês | MEDLINE | ID: mdl-32709355

RESUMO

Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential fatty acids for human body, which are widely used in the field of healthy food and medicine. Meanwhile, there are some differences in their physiological functions, such as "scavenger for blood vessel" of EPA and "brain protector" of DHA. In order to make full use of EPA and DHA, it is necessary to prepare their high-purity component. In this paper, EPA and DHA were separated and purified by three-zone simulated moving bed (SMB) chromatography with C18 used as stationary phase and ethanol-water as mobile phase. For the single column experiment, a separation unit of SMB, the effects of the ratio of ethanol to water, pH value and temperature on the separation were investigated. The equilibrium dispersion (ED) model was used to obtain the adsorption parameters of EPA and DHA by inverse method and genetic algorithm, and the accuracy of the adsorption parameters was verified by fitting the overloaded elution curves under different conditions. Based on the acquired nonlinear adsorption isotherms the complete separation region was found according to triangle theory. The effects of sample concentration, flow ratios of adsorption zone and rectification zone, and column distribution mode of SMB on the separation were investigated. Under the optimized SMB conditions, the experimental result was that without regard to the other components, the chromatographic purity and recovery values of EPA and DHA exceeded 99% with the productivity of 4.15 g/L/h, and the solvent consumption of 1.11 L/g.


Assuntos
Cromatografia/métodos , Ácidos Docosa-Hexaenoicos/isolamento & purificação , Ácido Eicosapentaenoico/isolamento & purificação , Adsorção , Cromatografia Líquida de Alta Pressão , Ácidos Docosa-Hexaenoicos/química , Ácido Eicosapentaenoico/química , Dinâmica não Linear , Reologia , Solventes/química , Temperatura
13.
Phys Rev Lett ; 125(2): 028101, 2020 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-32701351

RESUMO

We propose an analytically tractable neural connectivity model with power-law distributed synaptic strengths. When threshold neurons with biologically plausible number of incoming connections are considered, our model features a continuous transition to chaos and can reproduce biologically relevant low activity levels and scale-free avalanches, i.e., bursts of activity with power-law distributions of sizes and lifetimes. In contrast, the Gaussian counterpart exhibits a discontinuous transition to chaos and thus cannot be poised near the edge of chaos. We validate our predictions in simulations of networks of binary as well as leaky integrate-and-fire neurons. Our results suggest that heavy-tailed synaptic distribution may form a weakly informative sparse-connectivity prior that can be useful in biological and artificial adaptive systems.


Assuntos
Modelos Neurológicos , Rede Nervosa/fisiologia , Sinapses/fisiologia , Animais , Encéfalo/anatomia & histologia , Encéfalo/fisiologia , Simulação por Computador , Rede Nervosa/anatomia & histologia , Vias Neurais/anatomia & histologia , Vias Neurais/fisiologia , Neurônios/citologia , Neurônios/fisiologia , Dinâmica não Linear
14.
J Biol Dyn ; 14(1): 590-607, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32696723

RESUMO

In this paper, we apply optimal control theory to a novel coronavirus (COVID-19) transmission model given by a system of non-linear ordinary differential equations. Optimal control strategies are obtained by minimizing the number of exposed and infected population considering the cost of implementation. The existence of optimal controls and characterization is established using Pontryagin's Maximum Principle. An expression for the basic reproduction number is derived in terms of control variables. Then the sensitivity of basic reproduction number with respect to model parameters is also analysed. Numerical simulation results demonstrated good agreement with our analytical results. Finally, the findings of this study shows that comprehensive impacts of prevention, intensive medical care and surface disinfection strategies outperform in reducing the disease epidemic with optimum implementation cost.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Modelos Biológicos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Número Básico de Reprodução/estatística & dados numéricos , Simulação por Computador , Infecções por Coronavirus/epidemiologia , Epidemias/prevenção & controle , Epidemias/estatística & dados numéricos , Humanos , Controle de Infecções , Conceitos Matemáticos , Dinâmica não Linear , Pneumonia Viral/epidemiologia , Fatores de Risco , Biologia de Sistemas
15.
PLoS Comput Biol ; 16(7): e1008053, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32673311

RESUMO

The estimation of parameters controlling the electrical properties of biological neurons is essential to determine their complement of ion channels and to understand the function of biological circuits. By synchronizing conductance models to time series observations of the membrane voltage, one may construct models capable of predicting neuronal dynamics. However, identifying the actual set of parameters of biological ion channels remains a formidable theoretical challenge. Here, we present a regularization method that improves convergence towards this optimal solution when data are noisy and the model is unknown. Our method relies on the existence of an offset in parameter space arising from the interplay between model nonlinearity and experimental error. By tuning this offset, we induce saddle-node bifurcations from sub-optimal to optimal solutions. This regularization method increases the probability of finding the optimal set of parameters from 67% to 94.3%. We also reduce parameter correlations by implementing adaptive sampling and stimulation protocols compatible with parameter identifiability requirements. Our results show that the optimal model parameters may be inferred from imperfect observations provided the conditions of observability and identifiability are fulfilled.


Assuntos
Canais Iônicos/fisiologia , Neurônios/fisiologia , Algoritmos , Biologia Computacional , Humanos , Íons , Modelos Neurológicos , Modelos Estatísticos , Dinâmica não Linear , Distribuição Normal , Probabilidade , Reprodutibilidade dos Testes
16.
PLoS Comput Biol ; 16(7): e1008075, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32730255

RESUMO

We previously proposed, on theoretical grounds, that the cerebellum must regulate the dimensionality of its neuronal activity during motor learning and control to cope with the low firing frequency of inferior olive neurons, which form one of two major inputs to the cerebellar cortex. Such dimensionality regulation is possible via modulation of electrical coupling through the gap junctions between inferior olive neurons by inhibitory GABAergic synapses. In addition, we previously showed in simulations that intermediate coupling strengths induce chaotic firing of inferior olive neurons and increase their information carrying capacity. However, there is no in vivo experimental data supporting these two theoretical predictions. Here, we computed the levels of synchrony, dimensionality, and chaos of the inferior olive code by analyzing in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions: carbenoxolone (gap junctions blocker), control, and picrotoxin (GABA-A receptor antagonist). To examine the effect of electrical coupling on dimensionality and chaotic dynamics, we first determined the physiological range of effective coupling strengths between inferior olive neurons in the three conditions using a combination of a biophysical network model of the inferior olive and a novel Bayesian model averaging approach. We found that effective coupling co-varied with synchrony and was inversely related to the dimensionality of inferior olive firing dynamics, as measured via a principal component analysis of the spike trains in each condition. Furthermore, for both the model and the data, we found an inverted U-shaped relationship between coupling strengths and complexity entropy, a measure of chaos for spiking neural data. These results are consistent with our hypothesis according to which electrical coupling regulates the dimensionality and the complexity in the inferior olive neurons in order to optimize both motor learning and control of high dimensional motor systems by the cerebellum.


Assuntos
Neurônios/fisiologia , Núcleo Olivar/fisiologia , Potenciais de Ação , Animais , Teorema de Bayes , Cerebelo/fisiologia , Simulação por Computador , Feminino , Junções Comunicantes/fisiologia , Modelos Neurológicos , Modelos Estatísticos , Dinâmica não Linear , Picrotoxina/farmacologia , Probabilidade , Células de Purkinje/fisiologia , Ratos , Ratos Sprague-Dawley , Sinapses/fisiologia , Ácido gama-Aminobutírico/fisiologia
17.
Chaos ; 30(5): 051107, 2020 May.
Artigo em Inglês | MEDLINE | ID: covidwho-508545

RESUMO

Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.


Assuntos
Infecções Assintomáticas/epidemiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Processos Estocásticos , Betacoronavirus , China , Saúde Global , Humanos , Modelos Estatísticos , Dinâmica não Linear , Pandemias
18.
Chaos ; 30(5): 051107, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32491888

RESUMO

Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.


Assuntos
Infecções Assintomáticas/epidemiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Processos Estocásticos , Betacoronavirus , China , Saúde Global , Humanos , Modelos Estatísticos , Dinâmica não Linear , Pandemias
19.
Environ Sci Pollut Res Int ; 27(27): 34107-34120, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32557044

RESUMO

Measurements of contaminant concentrations inevitably contain noise because of accidental and systematic errors. However, groundwater contamination sources identification (GCSI) is highly dependent on the data measurements, which directly affect the accuracy of the identification results. Thus, in the present study, the wavelet hierarchical threshold denoising method was employed to denoise concentration measurements and the denoised measurements were then used for GCSI. A 0-1 mixed-integer nonlinear programming optimization model (0-1 MINLP) based on a kernel extreme learning machine (KELM) was applied to identify the location and release history of a contamination source. The results showed the following. (1) The wavelet hierarchical threshold denoising method was not very effective when applied to concentration measurements observed every 2 months (the number of measurements is small and relatively discrete) compared with those obtained every 2 days (the number of measurements is large and relatively continuous). (2) When the concentration measurements containing noise were employed for GCSI, the identifications results were further from the true values when the measurements contained more noise. The approximation of the identification results to the true values improved when the denoised concentration measurements were employed for GCSI. (3) The 0-1 MINLP based on the surrogate KELM model could simultaneously identify the location and release history of contamination sources, as well reducing the computational load and decreasing the calculation time by 96.5% when solving the 0-1 MINLP.


Assuntos
Algoritmos , Água Subterrânea , Aprendizagem , Dinâmica não Linear
20.
PLoS One ; 15(6): e0234356, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32555656

RESUMO

In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multi-kernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.


Assuntos
Processamento Eletrônico de Dados/métodos , Algoritmos , Simulação por Computador , Redes Neurais de Computação , Dinâmica não Linear , Máquina de Vetores de Suporte
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