Base de dados : MEDLINE
Pesquisa : E05.959 [Categoria DeCS]
Referências encontradas : 1566 [refinar]
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[PMID]:27774876
[Au] Autor:Lu S; Qiu X; Shi J; Li N; Lu ZH; Chen P; Yang MM; Liu FY; Jia WJ; Zhang Y
[Ti] Título:A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.
[So] Source:CNS Neurol Disord Drug Targets;16(1):23-29, 2017.
[Is] ISSN:1996-3181
[Cp] País de publicação:United Arab Emirates
[La] Idioma:eng
[Ab] Resumo:AIM: It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. METHOD: Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. RESULT: The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. CONCLUSION: The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection.
[Mh] Termos MeSH primário: Algoritmos
Encefalopatias/diagnóstico por imagem
Encéfalo/diagnóstico por imagem
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Automação
Encéfalo/patologia
Encefalopatias/patologia
Entropia
Seres Humanos
Processamento de Imagem Assistida por Computador
Imagem Tridimensional
Imagem por Ressonância Magnética
Neuroimagem
Reconhecimento Automatizado de Padrão
Reprodutibilidade dos Testes
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180209
[Lr] Data última revisão:
180209
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161025
[St] Status:MEDLINE
[do] DOI:10.2174/1871527315666161019153259


  2 / 1566 MEDLINE  
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[PMID]:27775596
[Au] Autor:Li H; Yuan D; Wang Y; Cui D; Cao L
[Ad] Endereço:School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China. lihongqiang@tjpu.edu.cn.
[Ti] Título:Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.
[So] Source:Sensors (Basel);16(10), 2016 Oct 20.
[Is] ISSN:1424-8220
[Cp] País de publicação:Switzerland
[La] Idioma:eng
[Ab] Resumo:Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.
[Mh] Termos MeSH primário: Arritmias Cardíacas/diagnóstico
Eletrocardiografia/métodos
[Mh] Termos MeSH secundário: Algoritmos
Arritmias Cardíacas/complicações
Bases de Dados Factuais
Frequência Cardíaca/fisiologia
Seres Humanos
Processamento de Sinais Assistido por Computador
Máquina de Vetores de Suporte
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180205
[Lr] Data última revisão:
180205
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161025
[St] Status:MEDLINE


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[PMID]:29205225
[Au] Autor:Czaplicki A; Kuniszyk-Józkowiak W; Jaszczuk J; Jarocka M; Walawski J
[Ad] Endereço:Józef Pilsudski University of Physical Education, Faculty of Physical Education and Sport, Department of Biomechanics and Computer Science, Biala Podlaska, Poland.
[Ti] Título:Using the discrete wavelet transform in assessing the effectiveness of rehabilitation in patients after ACL reconstruction.
[So] Source:Acta Bioeng Biomech;19(3):139-146, 2017.
[Is] ISSN:1509-409X
[Cp] País de publicação:Poland
[La] Idioma:eng
[Ab] Resumo:PURPOSE: The purpose of the current study was to assess the effectiveness of rehabilitation in patients after anterior cruciate ligament reconstruction (ACLR) using a wavelet analysis of the torque-time curve patterns of the extensors of the affected knee. The analysis aimed at the quantitative evaluation of irregularities in these torque-time patterns. METHODS: The study involved a group of 22 men who had had ACL reconstruction. The torque-time characteristics were recorded 3, 6 and 12 months after the surgery by an isokinetic dynamometer. They were then examined using the orthogonal Daubechies 4 (Db 4) and biorthogonal Bior 3.1 wavelets. RESULTS: A statistical analysis of the results revealed significant differences in values of the high-frequency energy stored in the details of the signal from the dynamometer between the first and last measurements, both for the Db 4 ( p ≤ 0.023) and Bior 3.1 ( p ≤ 0.01) wavelets. These differences were found in 73% of patients whose curve patterns were analysed using the Db 4 wavelet and in 82% of patients in the case of the Bior 3.1 wavelet. CONCLUSIONS: The wavelet transform proved to be an effective research tool in the qualitative evaluation of irregularities occurring in the curve patterns of the torque generated by the extensors of the ACL reconstructed knee. The findings of the study suggest that time-frequency analyses of these characteristics can be of practical importance, as they help assess the state of the patient's knee joint and his progress in rehabilitation after ACLR.
[Mh] Termos MeSH primário: Lesões do Ligamento Cruzado Anterior/diagnóstico
Lesões do Ligamento Cruzado Anterior/terapia
Reconstrução do Ligamento Cruzado Anterior/reabilitação
Diagnóstico por Computador/métodos
Teste de Esforço/métodos
Análise de Ondaletas
[Mh] Termos MeSH secundário: Adulto
Algoritmos
Lesões do Ligamento Cruzado Anterior/fisiopatologia
Seres Humanos
Articulação do Joelho/fisiopatologia
Masculino
Contração Muscular
Força Muscular
Músculo Esquelético/fisiopatologia
Avaliação de Resultados (Cuidados de Saúde)/métodos
Reprodutibilidade dos Testes
Sensibilidade e Especificidade
Processamento de Sinais Assistido por Computador
Terapia Assistida por Computador/métodos
Resultado do Tratamento
[Pt] Tipo de publicação:CLINICAL TRIAL; JOURNAL ARTICLE
[Em] Mês de entrada:1801
[Cu] Atualização por classe:180129
[Lr] Data última revisão:
180129
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171206
[St] Status:MEDLINE


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[PMID]:29176895
[Au] Autor:Xu G; Zhang M; Wang Y; Liu Z; Huo C; Li Z; Huo M
[Ad] Endereço:Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, P.R. China.
[Ti] Título:Functional connectivity analysis of distracted drivers based on the wavelet phase coherence of functional near-infrared spectroscopy signals.
[So] Source:PLoS One;12(11):e0188329, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:The present study aimed to evaluate the functional connectivity (FC) in relevant cortex areas during simulated driving with distraction based on functional near-infrared spectroscopy (fNIRS) method. Twelve subjects were recruited to perform three types of driving tasks, namely, straight driving, straight driving with secondary auditory task, and straight driving with secondary visual vigilance task, on a driving simulator. The wavelet amplitude (WA) and wavelet phase coherence (WPCO) of the fNIRS signals were calculated in six frequency intervals: I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; and V, 0.0095-0.021 Hz, VI, 0.005-0.0095Hz. Results showed that secondary tasks during driving led to worse driving performance, brain activity changes, and dynamic configuration of the connectivity. The significantly lower WA value in the right motor cortex in interval IV, and higher WPCO values in intervals II, V, and VI were found with additional auditory task. Significant standard deviation of speed and lower WA values in the left prefrontal cortex and right prefrontal cortex in interval VI, and lower WPCO values in intervals I, IV, V, and VI were found under the additional visual vigilance task. The results suggest that the changed FC levels in intervals IV, V, and VI were more likely to reflect the driver's distraction condition. The present study provides new insights into the relationship between distracted driving behavior and brain activity. The method may be used for the evaluation of drivers' attention level.
[Mh] Termos MeSH primário: Direção Distraída
Rede Nervosa/fisiologia
Espectroscopia de Luz Próxima ao Infravermelho
Análise de Ondaletas
[Mh] Termos MeSH secundário: Adulto
Feminino
Seres Humanos
Masculino
Córtex Motor/fisiologia
Córtex Pré-Frontal/fisiologia
Análise e Desempenho de Tarefas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1712
[Cu] Atualização por classe:171219
[Lr] Data última revisão:
171219
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171128
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0188329


  5 / 1566 MEDLINE  
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[PMID]:29049414
[Au] Autor:Toplak T; Palmieri B; Juanes-García A; Vicente-Manzanares M; Grant M; Wiseman PW
[Ad] Endereço:Department of Physics, McGill University, Montréal, Québec, Canada.
[Ti] Título:Wavelet Imaging on Multiple Scales (WIMS) reveals focal adhesion distributions, dynamics and coupling between actomyosin bundle stability.
[So] Source:PLoS One;12(10):e0186058, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:We introduce and use Wavelet Imaging on Multiple Scales (WIMS) as an improvement to fluorescence correlation spectroscopy to measure physical processes and features that occur across multiple length scales. In this study, wavelet transforms of cell images are used to characterize molecular dynamics at the cellular and subcellular levels (i.e. focal adhesions). We show the usefulness of the technique by applying WIMS to an image time series of a migrating osteosarcoma cell expressing fluorescently labelled adhesion proteins, which allows us to characterize different components of the cell ranging from optical resolution scale through to focal adhesion and whole cell size scales. Using WIMS we measured focal adhesion numbers, orientation and cell boundary velocities for retraction and protrusion. We also determine the internal dynamics of individual focal adhesions undergoing assembly, disassembly or elongation. Thus confirming as previously shown, WIMS reveals that the number of adhesions and the area of the protruding region of the cell are strongly correlated, establishing a correlation between protrusion size and adhesion dynamics. We also apply this technique to characterize the behavior of adhesions, actin and myosin in Chinese hamster ovary cells expressing a mutant form of myosin IIB (1935D) that displays decreased filament stability and impairs front-back cell polarity. We find separate populations of actin and myosin at each adhesion pole for both the mutant and wild type form. However, we find these populations move rapidly inwards toward one another in the mutant case in contrast to the cells that express wild type myosin IIB where those populations remain stationary. Results obtained with these two systems demonstrate how WIMS has the potential to reveal novel correlations between chosen parameters that belong to different scales.
[Mh] Termos MeSH primário: Actomiosina/química
Adesões Focais
Análise de Ondaletas
[Mh] Termos MeSH secundário: Animais
Células CHO
Cricetinae
Cricetulus
Microscopia de Fluorescência
Mutação Puntual
Estabilidade Proteica
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
9013-26-7 (Actomyosin)
[Em] Mês de entrada:1711
[Cu] Atualização por classe:171107
[Lr] Data última revisão:
171107
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171020
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0186058


  6 / 1566 MEDLINE  
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[PMID]:29040305
[Au] Autor:Dong Z; Wang R; Fan M; Fu X
[Ad] Endereço:College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China.
[Ti] Título:Switching and optimizing control for coal flotation process based on a hybrid model.
[So] Source:PLoS One;12(10):e0186553, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance.
[Mh] Termos MeSH primário: Fracionamento Químico/métodos
Minas de Carvão
Máquina de Vetores de Suporte
[Mh] Termos MeSH secundário: Seres Humanos
Análise de Componente Principal
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171031
[Lr] Data última revisão:
171031
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171018
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0186553


  7 / 1566 MEDLINE  
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[PMID]:28961273
[Au] Autor:Wang Y; Ding Y; Guo F; Wei L; Tang J
[Ad] Endereço:School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
[Ti] Título:Improved detection of DNA-binding proteins via compression technology on PSSM information.
[So] Source:PLoS One;12(9):e0185587, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix-Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix-Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.
[Mh] Termos MeSH primário: Proteínas de Ligação a DNA/metabolismo
Matrizes de Pontuação de Posição Específica
[Mh] Termos MeSH secundário: Proteínas de Ligação a DNA/análise
Máquina de Vetores de Suporte
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (DNA-Binding Proteins)
[Em] Mês de entrada:1711
[Cu] Atualização por classe:171103
[Lr] Data última revisão:
171103
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170930
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0185587


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[PMID]:28778057
[Au] Autor:Anastasiadou MN; Christodoulakis M; Papathanasiou ES; Papacostas SS; Mitsis GD
[Ad] Endereço:McGill University, Department of Bioengineering, 817 Sherbrooke St. W., Macdonald Engineering Building, Room 270, Montreal, QC H3A 0C3, Canada.
[Ti] Título:Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.
[So] Source:Clin Neurophysiol;128(9):1755-1769, 2017 Sep.
[Is] ISSN:1872-8952
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:OBJECTIVE: This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). METHODS: The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. RESULTS: We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). CONCLUSION: The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. SIGNIFICANCE: Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection.
[Mh] Termos MeSH primário: Algoritmos
Artefatos
Eletroencefalografia/métodos
Couro Cabeludo/fisiologia
Análise de Ondaletas
[Mh] Termos MeSH secundário: Eletroencefalografia/normas
Seres Humanos
Distribuição Aleatória
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1708
[Cu] Atualização por classe:170831
[Lr] Data última revisão:
170831
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170805
[St] Status:MEDLINE


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[PMID]:28708865
[Au] Autor:Bao W; Yue J; Rao Y
[Ad] Endereço:Business School, Central South University, Changsha, China.
[Ti] Título:A deep learning framework for financial time series using stacked autoencoders and long-short term memory.
[So] Source:PLoS One;12(7):e0180944, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
[Mh] Termos MeSH primário: Investimentos em Saúde/economia
[Mh] Termos MeSH secundário: Bases de Dados Factuais
Previsões
Seres Humanos
Memória de Longo Prazo
Memória de Curto Prazo
Redes Neurais (Computação)
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170927
[Lr] Data última revisão:
170927
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170715
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0180944


  10 / 1566 MEDLINE  
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[PMID]:28499157
[Au] Autor:Ouyang Y; Parajuli PB; Li Y; Leininger TD; Feng G
[Ad] Endereço:USDA Forest Service, Center for Bottomland Hardwoods Research, 775 Stone Blvd., Thompson Hall, Room 309, Mississippi State, MS, 39762, USA. Electronic address: youyang@fs.fed.us.
[Ti] Título:Identify temporal trend of air temperature and its impact on forest stream flow in Lower Mississippi River Alluvial Valley using wavelet analysis.
[So] Source:J Environ Manage;198(Pt 2):21-31, 2017 Aug 01.
[Is] ISSN:1095-8630
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Characterization of stream flow is essential to water resource management, water supply planning, environmental protection, and ecological restoration; while air temperature variation due to climate change can exacerbate stream flow and add instability to the flow. In this study, the wavelet analysis technique was employed to identify temporal trend of air temperature and its impact upon forest stream flows in Lower Mississippi River Alluvial Valley (LMRAV). Four surface water monitoring stations, which locate near the headwater areas with very few land use disturbances and the long-term data records (60-90 years) in the LMRAV, were selected to obtain stream discharge and air temperature data. The wavelet analysis showed that air temperature had an increasing temporal trend around its mean value during the past several decades in the LMRAV, whereas stream flow had a decreasing temporal trend around its average value at the same time period in the same region. Results of this study demonstrated that the climate in the LMRAV did get warmer as time elapsed and the streams were drier as a result of warmer air temperature. This study further revealed that the best way to estimate the temporal trends of air temperature and stream flow was to perform the wavelet transformation around their mean values.
[Mh] Termos MeSH primário: Mudança Climática
Florestas
Temperatura Ambiente
[Mh] Termos MeSH secundário: Monitoramento Ambiental
Mississippi
Rios
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1711
[Cu] Atualização por classe:171113
[Lr] Data última revisão:
171113
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170513
[St] Status:MEDLINE



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