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| Registros recuperados: 57 | |
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| Silva,Gabi Nunes; Nascimento,Moysés; Sant’Anna,Isabela de Castro; Cruz,Cosme Damião; Caixeta,Eveline Teixeira; Carneiro,Pedro Crescêncio Souza; Rosado,Renato Domiciano Silva; Pestana,Kátia Nogueira; Almeida,Dênia Pires de; Oliveira,Marciane da Silva. |
| Abstract: The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped,... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Coffea arabica; Hemileia vastatrix; Artificial intelligence; Molecular markers; Prediction. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2017000300186 |
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| Peterson, Garry D; McGill University; garry.peterson@mcgill.ca; Beard Jr., T. Douglas; Wisconsin Department of Natural Resources; BEARDT@dnr.state.wi.us; Beisner, Beatrix E; University of Wisconsin-Madison; bebeisner@facstaff.wisc.edu; Bennett, Elena M; University of Wisconsin-Madison; embennett@wisc.edu; Carpenter, Stephen R; University of Wisconsin-Madison; srcarpen@wisc.edu; Cumming, Graeme; University of Florida; cummingg@wec.ufl.edu; Dent, C. Lisa; University of Wisconsin-Madison; ldent@facstaff.wisc.edu,; Havlicek, Tanya D; University of Wisconsin-Madison; TDHAVLIC@students.wisc.edu. |
| The Northern Highlands Lake District of Wisconsin is in transition from a sparsely settled region to a more densely populated one. Expected changes offer benefits to northern Wisconsin residents but also threaten to degrade the ecological services they rely on. Because the future of this region is uncertain, it is difficult to make decisions that will avoid potential risks and take advantage of potential opportunities. We adopt a scenario planning approach to cope with this problem of prediction. We use an ecological assessment framework developed by the Millennium Ecosystem Assessment to determine key social and ecological driving forces in the Northern Highlands Lake District. From these, we describe three alternative scenarios to the year 2025 in which... |
| Tipo: Peer-Reviewed Reports |
Palavras-chave: Northern Highlands Lake District; Wisconsin; Assessment; Ecosystem services; Freshwater; Futures; Prediction; Scenario planning. |
| Ano: 2003 |
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| Yue, Jun; Dong, Yue; Wu, Sangyun; Geng, Xiushan; Zhao, Changrong. |
| Based on a large number of actual data, the author believe that the modern global warming and sea level rise resulted from climate warming after the cold front of the Little Ice Age about 200 years ago and the developmnet of the sea level rise phase. In the past 30 years, the rate of sea level rise was increasing, which is under the background of the average temperature uplift 0.2F°(0.11 °C)every 10 years in succession from the 1980s to the past 10 years this century. On the basis of the absolute and relative sea-level rise rate that was calculated from the tidal data during the same period at home and abroad in the last 30 years, in accordance with the resolutions of the 2010 climate conference in Cancun, at the same time, considering the previous... |
| Tipo: Journal Contribution |
Palavras-chave: Global warming; Sea level variations; Prediction; Evaluation. |
| Ano: 2012 |
URL: http://hdl.handle.net/1834/5832 |
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| Tekin,Yücel; Tümsavas,Zeynal; Mouazen,Abdul Mounem. |
| Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples... |
| Tipo: Info:eu-repo/semantics/other |
Palavras-chave: Modeling; Prediction; Vis-NIR. |
| Ano: 2014 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832014000600014 |
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| Bocco,Mónica; Willington,Enrique; Arias,Mónica. |
| The incident solar radiation on soil is an important variable used in agricultural applications; it is also relevant in hydrology, meteorology and soil physics, among others. To estimate this variable, empirical models have been developed using several parameters and, recently, prognostic and prediction models based on artificial intelligence techniques such as neural networks. The aim of this work was to develop linear models and neural networks, multilayer perceptron, to estimate daily global solar radiation and compare their efficiency in its application to a region of the Province of Salta, Argentina. Relative sunshine duration, maximum and minimum temperature, rainfall, binary rainfall and extraterrestrial solar radiation data for the period... |
| Tipo: Journal article |
Palavras-chave: Modeling; Prediction; Linear regression; Multilayer perceptron. |
| Ano: 2010 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392010000300010 |
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| Michels,Marcus; Matte,Ursula; Fraga,Lucas Rosa; Mancuso,Aline Castello Branco; Ligabue-Braun,Rodrigo; Berneira,Elias Figueroa Rodrigues; Siebert,Marina; Sanseverino,Maria Teresa Vieira. |
| Abstract Pathogenic variants in the Cystic Fibrosis Transmembrane Conductance Regulator gene (CFTR) are responsible for cystic fibrosis (CF), the commonest monogenic autosomal recessive disease, and CFTR-related disorders in infants and youth. Diagnosis of such diseases relies on clinical, functional, and molecular studies. To date, over 2,000 variants have been described on CFTR (~40% missense). Since few of them have confirmed pathogenicity, in silico analysis could help molecular diagnosis and genetic counseling. Here, the pathogenicity of 779 CFTR missense variants was predicted by consensus predictor PredictSNP and compared to annotations on CFTR2 and ClinVar. Sensitivity and specificity analysis was divided into modeling and validation phases using... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: CFTR; Missense variant; Prediction; Bioinformatics; Cystic fibrosis. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572019000400560 |
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| Gallo,Sarita Bonagurio; Tedeschi,Luis Orlindo. |
| ABSTRACT: Intake is a multifactorial process that is influenced by animal type, environmental factors, and diet characteristics. Sheep, especially, have specific eating habits, with a greater selection of ingested feed compared to cattle. Thus, predictive equations for dry matter intake (DMI) must constantly be reviewed. The objective of this study was to combine different adjustment factors to develop one continuous adjustment factor for predicting the DMI of pregnant, dry, and lactating ewes. The equations evaluated for non-lactation ewes accounts for metabolic body weight and weight gain, and the equation for lactating ewes includes milk production and its fat content. The database used in this study was pooled from hair sheep ewes, two to four years... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Mathematical model; Nutrition model; Prediction; Requirement; Sheep. |
| Ano: 2021 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000200502 |
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| Bocco,Mónica; Ovando,Gustavo; Sayago,Silvina. |
| The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Modelling; Prediction; Backpropagation neural networks. |
| Ano: 2006 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2006000200001 |
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| Dickey-collas, Mark; Payne, Mark R.; Trenkel, Verena M.; Nash, Richard D. M.. |
| The use of modelling approaches in marine science, and in particular fisheries science, is explored. We highlight that the choice of model used for an analysis should account for the question being posed or the context of the management problem. We examine a model-classification scheme based on Richard Levins' 1966 work suggesting that models can only achieve two of three desirable model attributes: realism, precision, and generality. Model creation, therefore, requires trading-off of one of these attributes in favour of the other two: however, this is often in conflict with the desires of end-users (i.e. mangers or policy developers). The combination of attributes leads to models that are considered to have empirical, mechanistic, or analytical... |
| Tipo: Text |
Palavras-chave: Climate; Fisheries; GAM; Management; Prediction; Projection; Recruitment; Time-series analysis. |
| Ano: 2014 |
URL: http://archimer.ifremer.fr/doc/00222/33371/32179.pdf |
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| Teng,Feixiang; Sun,Jinxia; Yu,Lili; Li,Qisong; Cui,Yubao. |
| Dermatophagoides farinae (Der f), one of the main species of house dust mites, produces more than 30 allergens. A recently identified allergen belonging to the alpha-tubulin protein family, Der f 33, has not been characterized in detail. In this study, we used bioinformatics tools to construct the secondary and tertiary structures and predict the B and T cell epitopes of Der f 33. First, protein attribution, protein patterns, and physicochemical properties were predicted. Then, a reasonable tertiary structure was constructed by homology modeling. In addition, six B cell epitopes (amino acid positions 34–45, 63–67, 103–108, 224–230, 308–316, and 365–377) and four T cell epitopes (positions 178–186, 241–249, 335–343, and 402–410) were predicted. These... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Der f 33; Homology modeling; B-cell epitope; T-cell epitope; Prediction. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2018000500601 |
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| Registros recuperados: 57 | |
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