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1.
An Acad Bras Cienc ; 95(suppl 2): e20220760, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38126518

RESUMO

The selection of parents to originate promising base populations, as well as the knowledge of the gene effects controlling agronomic traits by means of diallel, are useful to drive genetic gains in Brazilian tropical wheat breeding programs. The goals of this study were to select tropical wheat parents with a high frequency of favorable alleles and segregating populations with high potential to originate superior progenies through partial diallel analysis. Thus, 14 parents were divided in two groups and crossed in a 7 × 7 partial diallel scheme to originate 49 F1 combinations. After obtaining F2 generation, the populations and the parents were evaluated in the field in the summer of 2021. Days for heading, plant height, rust and yellow spot resistance, and grain yield were evaluated. The data were subjected to partial diallel analysis. There were significant effects of general combining ability for all traits. The specific combining ability effect was significant for days for heading and plant height. The additive gene effects were predominant over the non-additive ones. The parents with the highest frequency of favorable alleles for the traits evaluated were selected in each group. Four populations with high genetic potential to originate superior progenies were selected.


Assuntos
Basidiomycota , Triticum , Triticum/genética , Melhoramento Vegetal , Genótipo , Fenótipo
2.
Plant Cell Environ ; 41(2): 327-341, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29044606

RESUMO

To identify genomic regions involved in the regulation of fundamental physiological processes such as photosynthesis and respiration, a population of Solanum pennellii introgression lines was analyzed. We determined phenotypes for physiological, metabolic, and growth related traits, including gas exchange and chlorophyll fluorescence parameters. Data analysis allowed the identification of 208 physiological and metabolic quantitative trait loci with 33 of these being associated to smaller intervals of the genomic regions, termed BINs. Eight BINs were identified that were associated with higher assimilation rates than the recurrent parent M82. Two and 10 genomic regions were related to shoot and root dry matter accumulation, respectively. Nine genomic regions were associated with starch levels, whereas 12 BINs were associated with the levels of other metabolites. Additionally, a comprehensive and detailed annotation of the genomic regions spanning these quantitative trait loci allowed us to identify 87 candidate genes that putatively control the investigated traits. We confirmed 8 of these at the level of variance in gene expression. Taken together, our results allowed the identification of candidate genes that most likely regulate photosynthesis, primary metabolism, and plant growth and as such provide new avenues for crop improvement.


Assuntos
Fotossíntese/genética , Solanum lycopersicum/genética , Clorofila/metabolismo , Genes de Plantas/genética , Genes de Plantas/fisiologia , Solanum lycopersicum/crescimento & desenvolvimento , Solanum lycopersicum/metabolismo , Solanum lycopersicum/fisiologia , Locos de Características Quantitativas/genética , Característica Quantitativa Herdável , Reação em Cadeia da Polimerase em Tempo Real
3.
An Acad Bras Cienc ; 90(4): 3925-3940, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30517226

RESUMO

Asian soybean rust (Phakopsora pachyrhizi - ASR) is one of the major diseases that occur in soybean and causes great damage to commercial crops. Therefore, the goal of this work was to investigate the relationship between biochemical and photosyntetic parameters in soybean with ASR. Two experiments were performed in a randomized complete block with three treatments (water, Tween 20, and methyl jasmonate). The evaluated traits were: severity, chlorophyll pools, concentration of phenolic compounds content, enzyme activity, and photosyntetic parameters. Based on Pearson correlation, correlation network and path analysis it was verified that the severity had high correlation with almost all traits evaluated meanwhile photosynthesis is weakly related with almost all traits. Therefore, the occurrence of ASR affects directly the traits related to enzymatic activity and phenolic compounds content in soybean plants inoculated with ASR. The management of ASR is important to keep in normal levels the rates of photosynthesis carried out by the plant, and thus not affect the yield. Besides that, understanding the biochemical mechanisms and ecophysiological responses that occur during the soybean-P. pachyrhizi interaction has a great importance for breeding programs, as it will help for obtaining resistant cultivars or efficient methods in fungus control.


Assuntos
Glycine max/microbiologia , Phakopsora pachyrhizi/fisiologia , Fotossíntese/fisiologia , Doenças das Plantas/microbiologia , Fenótipo , Fotossíntese/genética , Locos de Características Quantitativas/genética , Glycine max/química , Glycine max/fisiologia
4.
Planta ; 242(3): 677-91, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-26007687

RESUMO

MAIN CONCLUSION: Collectively, the results presented improve upon the utility of an important genetic resource and attest to a complex genetic basis for differences in both leaf metabolism and fruit morphology between natural populations. Diversity of accessions within the same species provides an alternative method to identify physiological and metabolic traits that have large effects on growth regulation, biomass and fruit production. Here, we investigated physiological and metabolic traits as well as parameters related to plant growth and fruit production of 49 phenotypically diverse pepper accessions of Capsicum chinense grown ex situ under controlled conditions. Although single-trait analysis identified up to seven distinct groups of accessions, working with the whole data set by multivariate analyses allowed the separation of the 49 accessions in three clusters. Using all 23 measured parameters and data from the geographic origin for these accessions, positive correlations between the combined phenotypes and geographic origin were observed, supporting a robust pattern of isolation-by-distance. In addition, we found that fruit set was positively correlated with photosynthesis-related parameters, which, however, do not explain alone the differences in accession susceptibility to fruit abortion. Our results demonstrated that, although the accessions belong to the same species, they exhibit considerable natural intraspecific variation with respect to physiological and metabolic parameters, presenting diverse adaptation mechanisms and being a highly interesting source of information for plant breeders. This study also represents the first study combining photosynthetic, primary metabolism and growth parameters for Capsicum to date.


Assuntos
Capsicum/metabolismo , Capsicum/fisiologia , Fotossíntese/fisiologia , Fotossíntese/genética , Folhas de Planta/metabolismo , Folhas de Planta/fisiologia
5.
Rev Bras Enferm ; 77(1): e20230201, 2024.
Artigo em Inglês, Português | MEDLINE | ID: mdl-38422311

RESUMO

OBJECTIVES: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. METHODS: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. RESULTS: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. CONCLUSIONS: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.


Assuntos
Inteligência Artificial , Estado Terminal , Humanos , Redes Neurais de Computação , Algoritmos , Unidades de Terapia Intensiva
6.
Front Plant Sci ; 15: 1293307, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38726298

RESUMO

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

7.
PLoS One ; 19(3): e0299290, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38442106

RESUMO

Probabilistic models enhance breeding, especially for the Tahiti acid lime, a fruit essential to fresh markets and industry. These models identify superior and persistent individuals using probability theory, providing a measure of uncertainty that can aid the recommendation. The objective of our study was to evaluate the use of a Bayesian probabilistic model for the recommendation of superior and persistent genotypes of Tahiti acid lime evaluated in 12 harvests. Leveraging the Monte Carlo Hamiltonian sampling algorithm, we calculated the probability of superior performance (superior genotypic value), and the probability of superior stability (reduced variance of the genotype-by-harvests interaction) of each genotype. The probability of superior stability was compared to a measure of persistence estimated from genotypic values predicted using a frequentist model. Our results demonstrated the applicability and advantages of the Bayesian probabilistic model, yielding similar parameters to those of the frequentist model, while providing further information about the probabilities associated with genotype performance and stability. Genotypes G15, G4, G18, and G11 emerged as the most superior in performance, whereas G24, G7, G13, and G3 were identified as the most stable. This study highlights the usefulness of Bayesian probabilistic models in the fruit trees cultivars recommendation.


Assuntos
Compostos de Cálcio , Óxidos , Melhoramento Vegetal , Humanos , Teorema de Bayes , Probabilidade , Polinésia
8.
Sci Rep ; 14(1): 18429, 2024 08 08.
Artigo em Inglês | MEDLINE | ID: mdl-39117704

RESUMO

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.


Assuntos
Eucalyptus , Melhoramento Vegetal , Eucalyptus/genética , Melhoramento Vegetal/métodos , Brasil , Interação Gene-Ambiente , Tomada de Decisões , Genótipo , Meio Ambiente , Reprodutibilidade dos Testes
9.
G3 (Bethesda) ; 14(8)2024 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-38869242

RESUMO

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.


Assuntos
Simulação por Computador , Haploidia , Modelos Genéticos , Melhoramento Vegetal , Seleção Genética , Zea mays , Zea mays/genética , Melhoramento Vegetal/métodos , Genômica/métodos , Fenótipo , Genoma de Planta , Genótipo
10.
Genet Mol Biol ; 36(3): 371-81, 2013 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-24130445

RESUMO

The genetic variability of the Brazilian physic nut (Jatropha curcas) germplasm bank (117 accessions) was assessed using a combination of phenotypic and molecular data. The joint dissimilarity matrix showed moderate correlation with the original matrices of phenotypic and molecular data. However, the correlation between the phenotypic dissimilarity matrix and the genotypic dissimilarity matrix was low. This finding indicated that molecular markers (RAPD and SSR) did not adequately sample the genomic regions that were relevant for phenotypic differentiation of the accessions. The dissimilarity values of the joint dissimilarity matrix were used to measure phenotypic + molecular diversity. This diversity varied from 0 to 1.29 among the 117 accessions, with an average dissimilarity among genotypes of 0.51. Joint analysis of phenotypic and molecular diversity indicated that the genetic diversity of the physic nut germplasm was 156% and 64% higher than the diversity estimated from phenotypic and molecular data, respectively. These results show that Jatropha genetic variability in Brazil is not as limited as previously thought.

11.
Sci Rep ; 13(1): 17909, 2023 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-37864089

RESUMO

Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean.


Assuntos
Glycine max , Melhoramento Vegetal , Glycine max/genética , Fenótipo , Genótipo , Agricultura , Grão Comestível/genética
12.
Sci Rep ; 11(1): 13583, 2021 06 30.
Artigo em Inglês | MEDLINE | ID: mdl-34193953

RESUMO

Genome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with call rate = 95% and minor allele frequency (MAF) > 4%. GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.


Assuntos
Produção Agrícola , Produtos Agrícolas , Jatropha , Melhoramento Vegetal , Polimorfismo de Nucleotídeo Único , Seleção Genética , Alelos , Produtos Agrícolas/genética , Produtos Agrícolas/crescimento & desenvolvimento , Frequência do Gene , Genoma de Planta , Estudo de Associação Genômica Ampla , Jatropha/genética , Jatropha/crescimento & desenvolvimento , Fenótipo
13.
PLoS One ; 16(10): e0258473, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34673808

RESUMO

Spatial trends represent an obstacle to genetic evaluation in maize breeding. Spatial analyses can correct spatial trends, which allow for an increase in selective accuracy. The objective of this study was to compare the spatial (SPA) and non-spatial (NSPA) models in diallel multi-environment trial analyses in maize breeding. The trials consisted of 78 inter-populational maize hybrids, tested in four environments (E1, E2, E3, and E4), with three replications, under a randomized complete block design. The SPA models accounted for autocorrelation among rows and columns by the inclusion of first-order autoregressive matrices (AR1 ⊗ AR1). Then, the rows and columns factors were included in the fixed and random parts of the model. Based on the Bayesian information criteria, the SPA models were used to analyze trials E3 and E4, while the NSPA model was used for analyzing trials E1 and E2. In the joint analysis, the compound symmetry structure for the genotypic effects presented the best fit. The likelihood ratio test showed that some effects changed regarding significance when the SPA and NSPA models were used. In addition, the heritability, selective accuracy, and selection gain were higher when the SPA models were used. This indicates the power of the SPA model in dealing with spatial trends. The SPA model exhibits higher reliability values and is recommended to be incorporated in the standard procedure of genetic evaluation in maize breeding. The analyses bring the parents 2, 10 and 12, as potential parents in this microregion.


Assuntos
Zea mays , Melhoramento Vegetal
14.
PLoS One ; 16(1): e0243666, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33400704

RESUMO

This study assessed the efficiency of Genomic selection (GS) or genome-wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.


Assuntos
Simulação por Computador , Genoma de Planta , Modelos Genéticos , Plantas/genética , Seleção Genética , Marcadores Genéticos , Genótipo , Melhoramento Vegetal , Característica Quantitativa Herdável
15.
PLoS One ; 16(3): e0247775, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33661980

RESUMO

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.


Assuntos
Biocombustíveis/provisão & distribuição , Jatropha/crescimento & desenvolvimento , Melhoramento Vegetal/métodos , Algoritmos , Teorema de Bayes , Genótipo , Jatropha/genética , Cadeias de Markov , Modelos Genéticos , Modelos Teóricos , Método de Monte Carlo , Fenótipo
16.
PLoS One ; 15(11): e0242705, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33216796

RESUMO

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.


Assuntos
Interação Gene-Ambiente , Modelos Genéticos , Herança Multifatorial , Melhoramento Vegetal , Locos de Características Quantitativas , Zea mays/genética , Seleção Genética
17.
PLoS One ; 15(12): e0244021, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33362265

RESUMO

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.


Assuntos
Jatropha/genética , Modelos Genéticos , Melhoramento Vegetal , Variação Biológica da População , Variação Genética
18.
Rev. bras. enferm ; 77(1): e20230201, 2024. tab
Artigo em Inglês | LILACS-Express | LILACS, BDENF - enfermagem (Brasil) | ID: biblio-1535565

RESUMO

ABSTRACT Objectives: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. Methods: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. Results: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. Conclusions: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.


RESUMEN Objetivos: evaluar el rendimiento predictivo de diferentes algoritmos de inteligencia artificial para estimar el tiempo de ejecución del baño en cama en pacientes críticos. Métodos: estudio metodológico, que utilizó algoritmos de inteligencia artificial para predecir el tiempo de baño en cama en pacientes críticos. Se analizaron los resultados de modelos de regresión múltiple, redes neuronales perceptrón multicapa y función de base radial, árbol de decisión y random forest. Resultados: entre los modelos evaluados, el modelo de red neuronal con función de base radial, que contiene 13 neuronas en la capa oculta, presentó el mejor desempeño predictivo para estimar el tiempo de ejecución del baño en cama. En la validación de datos, la correlación al cuadrado entre los valores predichos y los valores originales fue del 62,3%. Conclusiones: el modelo de red neuronal con función de base radial mostró mejor rendimiento predictivo para estimar el tiempo de ejecución del baño en cama en pacientes críticos.


RESUMO Objetivos: avaliar a performance preditiva de diferentes algoritmos de inteligência artificial para estimar o tempo de execução do banho no leito em pacientes críticos. Métodos: estudo metodológico, que utilizou algoritmos de inteligência artificial para predizer o tempo de banho no leito em pacientes críticos. Foram analisados os resultados dos modelos de regressão múltipla, redes neurais perceptron multicamadas e função de base radial, árvore de decisão e random forest. Resultados: entre os modelos avaliados, o modelo de rede neural com função de base radial, contendo 13 neurônios na camada oculta, apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito. Na validação dos dados, o quadrado da correlação entre os valores preditos e os valores originais foi de 62,3%. Conclusões: o modelo de rede neural com função de base radial apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito em pacientes críticos.

19.
PLoS One ; 14(12): e0226523, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31846491

RESUMO

Photosynthetic efficiency has become the target of several breeding programs since the positive correlation between photosynthetic rate and yield in soybean suggests that the improvement of photosynthetic efficiency may be a promising target for new yield gains. However, studies on combining ability of soybean genotypes for physiological traits are still scarce in the literature. The objective of this study was to estimate the combining ability of soybean genotypes based on F2 generation aiming to identify superior parents and segregating populations for physiological traits. Twenty-eight F2 populations resulting from partial diallel crossings between eleven lines were evaluated in two crop seasons for the physiological traits: photosynthesis, stomatal conductance, internal CO2 concentration, and transpiration. General combining ability (GCA) of the parents and specific combining ability (SCA) of the F2 populations were estimated. Our findings reveal the predominance of additive effects in controlling the traits. The genotype TMG 7062 IPRO is the most promising parent for programs aiming at photosynthetic efficiency. We have also identified other promising parents and proposed cross-breeding with higher potential for obtaining superior lines for photosynthetic efficiency.


Assuntos
Glycine max/genética , Hibridização Genética , Alelos , Variação Genética , Genótipo
20.
PLoS One ; 13(7): e0199880, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30001344

RESUMO

Jatropha (Jatropha curcas) has become one of the most important species for producing biofuels. Currently, Genotype x Environment (GxE) interaction is the biggest challenge that breeders should solve to increase the section accuracy in the plant breeding. Therefore, the objectives in this study were to estimate the parameters in the 180 half-sib families in Jatropha evaluated for five production years, to verify the significance of the GxE interaction variance, to evaluate the adaptability and stability for each family based on three prediction methods, to select superior half-sib families based on the adaptability and stability analyses, and to predict the accuracy for the sixth production year. Jatropha half-sib families were classified and selected using the follow adaptability and stability methods: linear regression, bi-segmented linear regression and mixed models concepts called harmonic mean of the relative performance of genetic values (HMRPGV). The prediction accuracy was estimated by the Pearson correlation between the predicted genetic values by adaptability and stability methods and the phenotypic value in the sixth production year. In result, most half-sib families were classified as general adaptability and general stability for the evaluated traits. The selection gain obtained via HMRPGV was higher than other methods. The prediction accuracy for the sixth production year was 0.45. Therefore, HMRPGV is efficient to maximize the genetic gain, and it can be a useful strategy to select genotype with high adaptability and stability in Jatropha breeding as well as other species that should be evaluated for many years to take a suitable selection accuracy.


Assuntos
Instabilidade Genômica , Jatropha/genética , Modelos Genéticos , Melhoramento Vegetal/métodos , Seleção Genética , Adaptação Fisiológica , Interação Gene-Ambiente
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