Search Results - "da Silva, Felipe Lopes"
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Photocatalytic Degradation of Perfluorooctanoic Acid (PFOA) From Wastewaters by TiO2, In2O3 and Ga2O3 Catalysts
Published in Topics in catalysis (01-11-2017)“…The aim of the work was to prepare nanosized In 2 O 3 and Ga 2 O 3 photocatalysts for degradation of perfluorooctanoic acid (PFOA) in water. Their commercial…”
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2
Multi-trait multi-environment models in the genetic selection of segregating soybean progeny
Published in PloS one (18-04-2019)“…At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is…”
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3
Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits
Published in PloS one (29-12-2021)“…Breeding programs of the species Coffea canephora rely heavily on the significant genetic variability between and within its two varietal groups (conilon and…”
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4
Overcoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content
Published in PloS one (22-05-2020)“…Path analysis allows understanding the direct and indirect effects among traits. Multicollinearity in correlation matrices may cause a bias in path analysis…”
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5
Upgrade of the SPECIES beamline at the MAX IV Laboratory
Published in Journal of synchrotron radiation (01-03-2021)“…The SPECIES beamline has been transferred to the new 1.5 GeV storage ring at the MAX IV Laboratory. Several improvements have been made to the beamline and its…”
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6
Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora
Published in PloS one (26-01-2022)“…Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of…”
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Incorporating prior knowledge into Bayesian models for genetic evaluation in soybean breeding
Published in Pesquisa agropecuaria brasileira (01-01-2024)“…Abstract The objective of this work was to compare the use of noninformative and informative priors in Bayesian models, as well as to evaluate the viability of…”
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8
Machine learning applied to the prediction of root architecture of soybean cultivars under two water availability conditions
Published in Semina. Ciências agrárias : revista cultural e científica da Universidade Estadual de Londrina (01-05-2022)“…The objective of this study was to evaluate the performance of four machine learning models, as well as multitask learning, to predict soybean root variables…”
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Soybean crop yield estimation using artificial intelligence techniques
Published in Acta scientiarum. Agronomy (01-01-2024)“…It is common to observe conventional methods for estimating soybean crop yields, making the process slow and susceptible to human error. Therefore, the…”
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10
Near infrared spectroscopy for the classification of vigor level of soybean seed
Published in Ciência agronômica (01-01-2024)“…ABSTRACT This work aimed at investigating the viability of near infrared spectrometry (NIR), associated with chemometric methods, in order to identify…”
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11
Artificial aging for predicting the storability of soybean seeds via GGE biplot
Published in Journal of seed science (2024)“…Creation, adjustments and adoption of tests and tools that help in the prediction of seed storability have been highly demanded. Therefore, this work aimed to…”
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12
Repeatability and the optimal number of measurements for screening of soybean cultivars under water deficit
Published in Ciência agronômica (01-01-2022)“…ABSTRACT This study aimed at determining the optimal number of replications and measurements necessary for the screening of soybean cultivars subjected to…”
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13
Weathering deterioration in pre-harvest of soybean seeds: physiological, physical, and morpho-anatomical changes
Published in Scientia agricola (01-01-2021)“…Successive cycles of water absorption and loss favor weathering deterioration, one of the main factors that affect the quality of soybean seeds. This study…”
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14
Complementarity between parents for earliness and grain yield in soybean
Published in Pesquisa agropecuaria brasileira (01-01-2022)“…Abstract The objective of this work was to determine the general and specific combining ability (GCA and SCA, respectively) of six soybean (Glycine max)…”
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15
Combined BLUP selection indexes with parents and F2 populations in soybean (Glycine max) breeding
Published in Crop breeding and applied biotechnology (2023)“…This study aimed to apply selection indexes in situations in which F2 populations and their parents are evaluated simultaneously in field trials to predict…”
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Soybean productivity, stability, and adaptability through mixed model methodology
Published in Ciência rural (01-01-2021)“…The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the…”
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17
SNP markers associated with soybean partial resistance to Phytophthora sojae
Published in Crop breeding and applied biotechnology (01-01-2019)“…Phytophthora root and stem rot is one of the most aggressive diseases in soybean crop. The use of resistant cultivars is the main strategy to reduce losses…”
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18
Causal variant loci and protein-coding genes for soybean bacterial pustule resistance in the flowering stage
Published in Crop breeding and applied biotechnology (2022)“…Bacterial pustule is an important soybean disease caused by Xanthomonas citri pv. glycines, but information about genetic resistance to this pathogen is…”
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19
Selection of inbred soybean progeny (Glycine max): an approach with population effect
Published in Plant breeding (01-12-2018)“…To meet the growing demand of the soybean consumer market, cultivars increasingly early, productive and resistant to biotic and abiotic stress are sought…”
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20
Environmental stratification and genotype recommendation toward the soybean ideotype: a Bayesian approach
Published in Crop breeding and applied biotechnology (2021)“…The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genotypes recommendation. Thus,…”
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