Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 15.227
Filtrar
1.
BMJ Open ; 11(9): e049581, 2021 09 06.
Artigo em Inglês | MEDLINE | ID: mdl-34489283

RESUMO

OBJECTIVES: To evaluate the cost-effectiveness of four different primary screening strategies: high-risk factor questionnaire (HRFQ) alone, single immunochemical faecal occult blood test (iFOBT), double iFOBT and HRFQ+double iFOBT for colorectal cancer (CRC) screening compared with no screening using the Markov model. METHODS: Treeage Pro V.2011 software was used to simulate the Markov model. The incremental cost-effectiveness ratio, which was compared with the willingness-to-pay (WTP) threshold, was used to reflect the cost-effectiveness of the CRC screening method. One-way sensitivity analysis and probabilistic sensitivity analysis were used for parameter uncertainty. RESULTS: All strategies had greater effectiveness because they had more quality-adjusted life years (QALYs) than no screening. When the WTP was ¥435 762/QALY, all screening strategies were cost-effective compared with no screening. The double iFOBT strategy was the best-buy option compared with all other strategies because it had the most QALYs and the least cost. One-way sensitivity analysis showed that the sensitivity of low-risk adenoma, compliance with colonoscopy and primary screening cost were the main influencing factors comparing single iFOBT, double iFOBT and HRFQ+double iFOBT with no screening. However, within the scope of this study, there was no fundamental impact on cost-effectiveness. Probabilistic sensitivity analysis showed that when the WTP was ¥435 762/QALY, the probabilities of the cost-effectiveness acceptability curve with HRFQ alone, single iFOBT, double iFOBT and HRFQ+double iFOBT were 0.0%, 5.3%, 69.3% and 25.4%, respectively. CONCLUSIONS: All screening strategies for CRC were cost-effective compared with no screening strategy. Double iFOBT was the best-buy option compared with all other strategies. The significant influencing factors were the sensitivity of low-risk polyps, compliance with colonoscopy and cost of primary screening.


Assuntos
Neoplasias Colorretais , Detecção Precoce de Câncer , China , Colonoscopia , Neoplasias Colorretais/diagnóstico , Análise Custo-Benefício , Humanos , Cadeias de Markov , Programas de Rastreamento , Sangue Oculto , Anos de Vida Ajustados por Qualidade de Vida
2.
Chaos ; 31(8): 083114, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34470245

RESUMO

Even simply defined, finite-state generators produce stochastic processes that require tracking an uncountable infinity of probabilistic features for optimal prediction. For processes generated by hidden Markov chains, the consequences are dramatic. Their predictive models are generically infinite state. Until recently, one could determine neither their intrinsic randomness nor structural complexity. The prequel to this work introduced methods to accurately calculate the Shannon entropy rate (randomness) and to constructively determine their minimal (though, infinite) set of predictive features. Leveraging this, we address the complementary challenge of determining how structured hidden Markov processes are by calculating their statistical complexity dimension-the information dimension of the minimal set of predictive features. This tracks the divergence rate of the minimal memory resources required to optimally predict a broad class of truly complex processes.


Assuntos
Algoritmos , Entropia , Cadeias de Markov , Processos Estocásticos
3.
Sci Rep ; 11(1): 17421, 2021 08 31.
Artigo em Inglês | MEDLINE | ID: mdl-34465820

RESUMO

Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean [Formula: see text] of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.


Assuntos
COVID-19/epidemiologia , Brasil/epidemiologia , Aprendizado Profundo , Humanos , Cadeias de Markov , Redes Neurais de Computação , Projetos de Pesquisa , Federação Russa/epidemiologia , Reino Unido/epidemiologia , Estados Unidos
4.
J Res Health Sci ; 21(2): e00517, 2021 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-34465640

RESUMO

BACKGROUND: The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. STUDY DESIGN: Descriptive study. METHODS: This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood. RESULTS: In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41). CONCLUSION: Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.


Assuntos
Número Básico de Reprodução , COVID-19/epidemiologia , Epidemias , COVID-19/prevenção & controle , COVID-19/transmissão , COVID-19/virologia , Estudos Transversais , Humanos , Irã (Geográfico)/epidemiologia , Funções Verossimilhança , Cadeias de Markov , Método de Monte Carlo , Pandemias , SARS-CoV-2
5.
BMC Health Serv Res ; 21(1): 968, 2021 Sep 14.
Artigo em Inglês | MEDLINE | ID: mdl-34521414

RESUMO

BACKGROUND: We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated. METHODS: The model's main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal. RESULTS: The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation. CONCLUSIONS: Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system's cost is minimized.


Assuntos
Modelos Teóricos , Radiologia , Brasil , Humanos , Cadeias de Markov
6.
Nat Commun ; 12(1): 4721, 2021 08 05.
Artigo em Inglês | MEDLINE | ID: mdl-34354057

RESUMO

G protein-coupled receptors (GPCRs) are the most common proteins targeted by approved drugs. A complete mechanistic elucidation of large-scale conformational transitions underlying the activation mechanisms of GPCRs is of critical importance for therapeutic drug development. Here, we apply a combined computational and experimental framework integrating extensive molecular dynamics simulations, Markov state models, site-directed mutagenesis, and conformational biosensors to investigate the conformational landscape of the angiotensin II (AngII) type 1 receptor (AT1 receptor) - a prototypical class A GPCR-activation. Our findings suggest a synergistic transition mechanism for AT1 receptor activation. A key intermediate state is identified in the activation pathway, which possesses a cryptic binding site within the intracellular region of the receptor. Mutation of this cryptic site prevents activation of the downstream G protein signaling and ß-arrestin-mediated pathways by the endogenous AngII octapeptide agonist, suggesting an allosteric regulatory mechanism. Together, these findings provide a deeper understanding of AT1 receptor activation at an atomic level and suggest avenues for the design of allosteric AT1 receptor modulators with a broad range of applications in GPCR biology, biophysics, and medicinal chemistry.


Assuntos
Receptor Tipo 1 de Angiotensina/química , Receptor Tipo 1 de Angiotensina/metabolismo , Regulação Alostérica , Sítio Alostérico , Sítios de Ligação/genética , Desenho de Fármacos , Humanos , Cadeias de Markov , Simulação de Dinâmica Molecular , Mutagênese Sítio-Dirigida , Conformação Proteica , Receptor Tipo 1 de Angiotensina/genética , Transdução de Sinais , beta-Arrestinas/metabolismo
7.
PLoS One ; 16(8): e0251378, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34383784

RESUMO

BACKGROUND: The benefit of tocilizumab on mortality and time to recovery in people with severe COVID pneumonia may depend on appropriate timing. The objective was to estimate the impact of tocilizumab administration on switching respiratory support states, mortality and time to recovery. METHODS: In an observational study, a continuous-time Markov multi-state model was used to describe the sequence of respiratory support states including: no respiratory support (NRS), oxygen therapy (OT), non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV), OT in recovery, NRS in recovery. RESULTS: Two hundred seventy-one consecutive adult patients were included in the analyses contributing to 695 transitions across states. The prevalence of patients in each respiratory support state was estimated with stack probability plots, comparing people treated with and without tocilizumab since the beginning of the OT state. A positive effect of tocilizumab on the probability of moving from the invasive and non-invasive mechanical NIV/IMV state to the OT in recovery state (HR = 2.6, 95% CI = 1.2-5.2) was observed. Furthermore, a reduced risk of death was observed in patients in NIV/IMV (HR = 0.3, 95% CI = 0.1-0.7) or in OT (HR = 0.1, 95% CI = 0.0-0.8) treated with tocilizumab. CONCLUSION: To conclude, we were able to show the positive impact of tocilizumab used in different disease stages depicted by respiratory support states. The use of the multi-state Markov model allowed to harmonize the heterogeneous mortality and recovery endpoints and summarize results with stack probability plots. This approach could inform randomized clinical trials regarding tocilizumab, support disease management and hospital decision making.


Assuntos
Anticorpos Monoclonais Humanizados/uso terapêutico , COVID-19/tratamento farmacológico , Terapia Respiratória/métodos , Idoso , Feminino , Humanos , Masculino , Cadeias de Markov , Pessoa de Meia-Idade , Ventilação não Invasiva , Oxigenoterapia , Respiração Artificial , Fatores de Tempo , Resultado do Tratamento
8.
Sci Rep ; 11(1): 17328, 2021 08 30.
Artigo em Inglês | MEDLINE | ID: mdl-34462499

RESUMO

Public health officials discouraged travel and non-household gatherings for Thanksgiving, but data suggests that travel increased over the holidays. The objective of this analysis was to assess associations between holiday gatherings and SARS-CoV-2 positivity in the weeks following Thanksgiving. Using an online survey, we sampled 7770 individuals across 10 US states from December 4-18, 2020, about 8-22 days post-Thanksgiving. Participants were asked about Thanksgiving, COVID-19 symptoms, and SARS-CoV-2 testing and positivity in the prior 2 weeks. Logistic regression was used to identify factors associated with SARS-CoV-2 positivity and COVID-19 symptoms in the weeks following Thanksgiving. An activity score measured the total number of non-essential activities an individual participated in the prior 2 weeks. The probability of community transmission was estimated using Markov Chain Monte Carlo (MCMC) methods. While 47.2% had Thanksgiving at home with household members, 26.9% had guests and 25.9% traveled. There was a statistically significant interaction between how people spent Thanksgiving, the frequency of activities, and SARS-CoV-2 test positivity in the prior 2 weeks (p < 0.05). Those who had guests for Thanksgiving or traveled were only more likely to test positive for SARS-CoV-2 if they also had high activity (e.g., participated in > one non-essential activity/day in the prior 2 weeks). Had individuals limited the number and frequency of activities post-Thanksgiving, cases in surveyed individuals would be reduced by > 50%. As travel continues to increase and the more contagious Delta variant starts to dominate transmission, it is critical to promote how to gather in a "low-risk" manner (e.g., minimize other non-essential activities) to mitigate the need for nationwide shelter-at-home orders.


Assuntos
COVID-19/epidemiologia , COVID-19/transmissão , Viagem/estatística & dados numéricos , Adulto , Teste para COVID-19 , Feminino , Férias e Feriados , Humanos , Masculino , Cadeias de Markov , Pessoa de Meia-Idade , Método de Monte Carlo , Saúde Pública , Estados Unidos/epidemiologia
9.
J Chem Phys ; 155(5): 054102, 2021 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-34364321

RESUMO

Markov state models (MSMs) have become one of the preferred methods for the analysis and interpretation of molecular dynamics (MD) simulations of conformational transitions in biopolymers. While there is great variation in terms of implementation, a well-defined workflow involving multiple steps is often adopted. Typically, molecular coordinates are first subjected to dimensionality reduction and then clustered into small "microstates," which are subsequently lumped into "macrostates" using the information from the slowest eigenmodes. However, the microstate dynamics is often non-Markovian, and long lag times are required to converge the relevant slow dynamics in the MSM. Here, we propose a variation on this typical workflow, taking advantage of hierarchical density-based clustering. When applied to simulation data, this type of clustering separates high population regions of conformational space from others that are rarely visited. In this way, density-based clustering naturally implements assignment of the data based on transitions between metastable states, resulting in a core-set MSM. As a result, the state definition becomes more consistent with the assumption of Markovianity, and the timescales of the slow dynamics of the system are recovered more effectively. We present results of this simplified workflow for a model potential and MD simulations of the alanine dipeptide and the FiP35 WW domain.


Assuntos
Dipeptídeos/química , Cadeias de Markov , Simulação de Dinâmica Molecular/estatística & dados numéricos , Proteínas/química , Análise por Conglomerados , Conformação Proteica , Domínios WW
10.
BMC Med Res Methodol ; 21(1): 151, 2021 07 24.
Artigo em Inglês | MEDLINE | ID: mdl-34303362

RESUMO

BACKGROUND: Converting electronic health record (EHR) entries to useful clinical inferences requires one to address the poor scalability of existing implementations of Generalized Linear Mixed Models (GLMM) for repeated measures. The major computational bottleneck concerns the numerical evaluation of multivariable integrals, which even for the simplest EHR analyses may involve millions of dimensions (one for each patient). The hierarchical likelihood (h-lik) approach to GLMMs is a methodologically rigorous framework for the estimation of GLMMs that is based on the Laplace Approximation (LA), which replaces integration with numerical optimization, and thus scales very well with dimensionality. METHODS: We present a high-performance, direct implementation of the h-lik for GLMMs in the R package TMB. Using this approach, we examined the relation of repeated serum potassium measurements and survival in the Cerner Real World Data (CRWD) EHR database. Analyzing this data requires the evaluation of an integral in over 3 million dimensions, putting this problem beyond the reach of conventional approaches. We also assessed the scalability and accuracy of LA in smaller samples of 1 and 10% size of the full dataset that were analyzed via the a) original, interconnected Generalized Linear Models (iGLM), approach to h-lik, b) Adaptive Gaussian Hermite (AGH) and c) the gold standard for multivariate integration Markov Chain Monte Carlo (MCMC). RESULTS: Random effects estimates generated by the LA were within 10% of the values obtained by the iGLMs, AGH and MCMC techniques. The H-lik approach was 4-30 times faster than AGH and nearly 800 times faster than MCMC. The major clinical inferences in this problem are the establishment of the non-linear relationship between the potassium level and the risk of mortality, as well as estimates of the individual and health care facility sources of variations for mortality risk in CRWD. CONCLUSIONS: We found that the direct implementation of the h-lik offers a computationally efficient, numerically accurate approach for the analysis of extremely large, real world repeated measures data via the h-lik approach to GLMMs. The clinical inference from our analysis may guide choices of treatment thresholds for treating potassium disorders in the clinic.


Assuntos
Registros Eletrônicos de Saúde , Potássio , Teorema de Bayes , Humanos , Modelos Lineares , Cadeias de Markov , Método de Monte Carlo , Valores de Referência
11.
J Surg Oncol ; 124(5): 801-809, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34231222

RESUMO

INTRODUCTION: Neoadjuvant therapy (NAT) is an emerging strategy for operable pancreatic ductal adenocarcinoma (PDAC). While NAT increases multimodal therapy completion, it risks functional decline and treatment dropout. We used decision analysis to determine optimal management of localized PDAC and consider risks faced by elderly patients. METHODS: A Markov cohort decision analysis model evaluated treatment options for a 60-year-old patient with resectable PDAC: (1) upfront pancreaticoduodenectomy or (2) NAT. One-way and probabilistic sensitivity analyses were performed. A subanalysis considered the scenario of a 75-year-old patient. RESULTS: For the base case, NAT offered an incremental survival gain of 4.6 months compared with SF (overall survival: 26.3 vs. 21.7 months). In one-way sensitivity analyses, findings were sensitive to recurrence-free survival for NAT patients undergoing adjuvant, probability of completing NAT, and probability of being resectable at exploration after NAT. On probabilistic analysis, NAT was favored in a majority of trials (97%) with a median survival benefit of 5.1 months. In altering the base case for the 75-year-old scenario, NAT had a survival benefit of 3.8 months. CONCLUSIONS: This analysis demonstrates a significant benefit to NAT in patients with localized PDAC. This benefit persists even in the elderly cohort.


Assuntos
Adenocarcinoma/terapia , Carcinoma Ductal Pancreático/terapia , Técnicas de Apoio para a Decisão , Cadeias de Markov , Terapia Neoadjuvante/mortalidade , Pancreatectomia/mortalidade , Neoplasias Pancreáticas/terapia , Adenocarcinoma/patologia , Idoso , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Carcinoma Ductal Pancreático/patologia , Terapia Combinada , Seguimentos , Humanos , Pessoa de Meia-Idade , Neoplasias Pancreáticas/patologia , Prognóstico , Estudos Retrospectivos , Taxa de Sobrevida
12.
Eur J Neurosci ; 54(4): 5404-5416, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34250639

RESUMO

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition.


Assuntos
Encéfalo , Imageamento por Ressonância Magnética , Algoritmos , Encéfalo/diagnóstico por imagem , Humanos , Cadeias de Markov , Distribuição Normal
13.
Chaos ; 31(4): 043129, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-34251266

RESUMO

The standard model of visual search dynamics is Brownian motion. However, recent research in cognitive science reveals that standard diffusion processes seem not to be the appropriate models of human looking behavior. In particular, experimental results confirm that the superdiffusive Lévy-type dynamics appears in this context. In this paper, we analyze the diffusive properties of human eye movement in a language comprehension task. We propose a model that is a combination of a Markov chain with a finite number of states and a Lévy walk. Our model fits well the experimental data and allows one to investigate the properties of the visual search dynamics using numerical simulations.


Assuntos
Movimentos Oculares , Difusão , Humanos , Cadeias de Markov , Movimento (Física) , Processos Estocásticos
14.
Chem Pharm Bull (Tokyo) ; 69(7): 674-680, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34193716

RESUMO

Quality by design (QbD) is an essential concept for modern manufacturing processes of pharmaceutical products. Understanding the science behind manufacturing processes is crucial; however, the complexity of the manufacturing processes makes implementing QbD challenging. In this study, structural equation modeling (SEM) was applied to understand the causal relationships between variables such as process parameters, material attributes, and quality attributes. Based on SEM analysis, we identified a model composed of the above-mentioned variables and their latent factors without including observational data. Difficulties in fitting the observed data to the proposed model are often encountered in SEM analysis. To address this issue, we adopted Bayesian estimation with Markov chain Monte Carlo simulation. The tableting process involving the wet-granulation process for acetaminophen was employed as a model case for the manufacturing process. The results indicate that SEM analysis could be useful for implementing QbD for the manufacturing processes of pharmaceutical products.


Assuntos
Análise de Classes Latentes , Comprimidos/química , Acetaminofen/química , Teorema de Bayes , Composição de Medicamentos/métodos , Cadeias de Markov , Método de Monte Carlo , Análise de Componente Principal
15.
Methods Mol Biol ; 2328: 191-202, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34251627

RESUMO

The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage "master" regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)-target interactions, microRNA (miRNA)-target interaction, and protein-protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host-pathogen interactions for comprehensive understanding of manipulated biological pathways.


Assuntos
Biologia Computacional/métodos , Mineração de Dados/métodos , Redes Reguladoras de Genes , Interações Hospedeiro-Patógeno/genética , RNA-Seq/métodos , Epigenômica , Regulação da Expressão Gênica de Plantas/genética , Genômica , Interações Hospedeiro-Patógeno/imunologia , Cadeias de Markov , Metabolômica , MicroRNAs/genética , MicroRNAs/metabolismo , Plantas/genética , Plantas/imunologia , Plantas/metabolismo , Linguagens de Programação , Transdução de Sinais/genética , Software , Análise Espaço-Temporal , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo
16.
PLoS Comput Biol ; 17(7): e1009211, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34310593

RESUMO

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).


Assuntos
Número Básico de Reprodução , COVID-19/epidemiologia , COVID-19/transmissão , Pandemias , SARS-CoV-2 , Algoritmos , Número Básico de Reprodução/estatística & dados numéricos , Teorema de Bayes , Biologia Computacional , Epidemias/estatística & dados numéricos , França/epidemiologia , Humanos , Irlanda/epidemiologia , Cadeias de Markov , Modelos Estatísticos , Método de Monte Carlo , Pandemias/estatística & dados numéricos , Estudos Soroepidemiológicos , Processos Estocásticos , Fatores de Tempo
17.
Sensors (Basel) ; 21(14)2021 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-34300460

RESUMO

Human action recognition methods in videos based on deep convolutional neural networks usually use random cropping or its variants for data augmentation. However, this traditional data augmentation approach may generate many non-informative samples (video patches covering only a small part of the foreground or only the background) that are not related to a specific action. These samples can be regarded as noisy samples with incorrect labels, which reduces the overall action recognition performance. In this paper, we attempt to mitigate the impact of noisy samples by proposing an Auto-augmented Siamese Neural Network (ASNet). In this framework, we propose backpropagating salient patches and randomly cropped samples in the same iteration to perform gradient compensation to alleviate the adverse gradient effects of non-informative samples. Salient patches refer to the samples containing critical information for human action recognition. The generation of salient patches is formulated as a Markov decision process, and a reinforcement learning agent called SPA (Salient Patch Agent) is introduced to extract patches in a weakly supervised manner without extra labels. Extensive experiments were conducted on two well-known datasets UCF-101 and HMDB-51 to verify the effectiveness of the proposed SPA and ASNet.


Assuntos
Redes Neurais de Computação , Reconhecimento Psicológico , Atividades Humanas , Humanos , Aprendizagem , Cadeias de Markov
18.
Sensors (Basel) ; 21(14)2021 Jul 14.
Artigo em Inglês | MEDLINE | ID: mdl-34300553

RESUMO

This paper deals with bistatic track association and deghosting in the classical frequency modulation (FM)-based multi-static primary surveillance radar (MSPSR). The main contribution of this paper is a novel algorithm for bistatic track association and deghosting. The proposed algorithm is based on a hierarchical model which uses the Indian buffet process (IBP) as the prior probability distribution for the association matrix. The inference of the association matrix is then performed using the classical reversible jump Markov chain Monte Carlo (RJMCMC) algorithm with the usage of a custom set of the moves proposed by the sampler. A detailed description of the moves together with the underlying theory and the whole model is provided. Using the simulated data, the algorithm is compared with the two alternative ones and the results show the significantly better performance of the proposed algorithm in such a simulated setup. The simulated data are also used for the analysis of the properties of Markov chains produced by the sampler, such as the convergence or the posterior distribution. At the end of the paper, further research on the proposed method is outlined.


Assuntos
Algoritmos , Radar , Teorema de Bayes , Cadeias de Markov , Método de Monte Carlo
19.
Phys Chem Chem Phys ; 23(32): 17158-17165, 2021 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-34318824

RESUMO

Due to its unique structure, recent years have witnessed the use of apo-ferritin to accumulate various non-natural metal ions as a scaffold for nanomaterial synthesis. However, the transport mechanism of metal ions into the cavity of apo-ferritin is still unclear, limiting the rational design and controllable preparation of nanomaterials. Here, we conducted all-atom classical molecular dynamics (MD) simulations combined with Markov state models (MSMs) to explore the transportation behavior of Au(iii) ions. We exhibited the complete transportation paths of Au(iii) from solution into the apo-ferritin cage at the atomic level. We also revealed that the transportation of Au(iii) ions is accompanied by coupled protein structural changes. It is shown that the 3-fold axis channel serves as the only entrance with the longest residence time of Au(iii) ions. Besides, there are eight binding clusters and five 3-fold structural metastable states, which are important during Au(iii) transportation. The conformational changes of His118, Asp127, and Glu130, acting as doors, were observed to highly correlate with the Au(iii) ion's position. The MSM analysis and Potential Mean Force (PMF) calculation suggest a remarkable energy barrier near Glu130, making it the rate-limiting step of the whole process. The dominant transportation pathway is from cluster 3 in the 3-fold channel to the inner cavity to cluster 5 on the inner surface, and then to cluster 6. These findings provide inspiration and theoretical guidance for the further rational design and preparation of new nanomaterials using apo-ferritin.


Assuntos
Apoferritinas/metabolismo , Ouro/metabolismo , Cadeias de Markov , Simulação de Dinâmica Molecular/estatística & dados numéricos , Animais , Apoferritinas/química , Sítios de Ligação , Ouro/química , Cavalos , Ligação de Hidrogênio , Ligação Proteica , Conformação Proteica , Eletricidade Estática
20.
Parasitol Res ; 120(7): 2569-2584, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34137949

RESUMO

One of the challenges in studies of parasite community ecology is whether the input data for analyses should be parasite abundances/counts, i.e. count data (CD), or parasite incidences (presences/absences), i.e. incidence data (ID). We analysed species responses to environmental factors and species associations in the infracommunities of helminths and ectoparasites in four hosts from Europe (Sorex araneus and Myodes glareolus) and South Africa (Rhabdomys pumilio and Rhabdomys dilectus) and compared the results of four analyses [redundancy analysis (RD), RLQ analysis, joint species distribution modelling (JSDM) and Markov random fields (MRF)] that used either CD or ID as an input. In addition, we compared the differences between the CD and ID results of two analyses (JSDM and MRF) across parasite species between (a) host species within helminths and ectoparasites; (b) helminths and ectoparasites within a host species; and (c) parasite species with contrasting levels of intensity. The results of most analyses for the majority of parasite-host associations were qualitatively similar. However, models based on the ID input performed better than models based on the CD input in three out of four types of analyses (RDA, JSDM and MRF). The differences between the CD and ID models varied between host species (being the lowest in R. pumilio for JSDM and in S. araneus for MRF). However, they were not affected by the level of parasite intensity.


Assuntos
Interações Hospedeiro-Parasita , Parasitos/fisiologia , Doenças Parasitárias/epidemiologia , Animais , Biota , Europa (Continente)/epidemiologia , Feminino , Helmintos/crescimento & desenvolvimento , Helmintos/fisiologia , Especificidade de Hospedeiro , Incidência , Masculino , Cadeias de Markov , Modelos Biológicos , Murinae/parasitologia , Parasitos/crescimento & desenvolvimento , Doenças Parasitárias/parasitologia , África do Sul/epidemiologia
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...