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.

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.

Abstract The objective of this work was to evaluate the efficiency of direct and indirect selection in genome-wide selection (GWS) and to compare multivariate strategies applied to GWS via selection indices. Ten F2 populations with 800 individuals were simulated, considering four traits with different heritabilities. The simulated data were subjected to genomic selection analyses. The five following strategies of selection indices were developed and applied to GWS: weighting of marker effects by residual variance; coding and standardization of marker effects; application of the average to the marker effects; application of the Mulamba & Mock index to genomic genetic values; and coding and standardization of phenotypic values before the GWS analyses. The GWS methods were more efficient than phenotypic selection. The multivariate strategies provide a higher selection efficiency than the direct and indirect phenotypic selections and than the direct and indirect selections based on genomic genetic values and on the phenotypic selection index. genomewide genome wide (GWS F 80 heritabilities analyses variance 8

Resumo O objetivo deste trabalho foi avaliar a eficiência da seleção direta e indireta sobre a seleção genômica ampla (GWS) e comparar estratégias multivariadas aplicadas à GWS via índices de seleção. Dez populações F2 com 800 indivíduos foram simuladas, tendo-se considerando quatro características com herdabilidades diferentes. Os dados simulados foram submetidos a análises de seleção genômica. As cinco seguintes estratégias de índices de seleção foram desenvolvidas e aplicadas à GWS: ponderação dos efeitos dos marcadores pela variância residual; codificação e padronização dos efeitos dos marcadores; aplicação da média aos efeitos dos marcadores; aplicação do índice de Mulamba & Mock aos valores genéticos genômicos; e codificação e padronização dos valores fenotípicos antes das análises de GWS. Os métodos de GWS foram mais eficientes do que a seleção fenotípica. As estratégias multivariadas avaliadas proporcionam maior eficiência de seleção do que a seleção fenotípica direta e indireta e do que a seleção direta e indireta baseada em valores genéticos genômicos e no índice de seleção fenotípica. (GWS F 80 simuladas tendose tendo se diferentes residual 8

ABSTRACT The prediction of traits allows the breeder to guide strategies to select and accelerate the progress of genetic improvement. The objective of this work was to determine the best prediction approach and establish a network with better predictive power for white oat using methodologies based on artificial intelligence, and machine learning. Seventy-eight white oat genotypes were evaluated. The design was randomized blocks with three replications. The models were evaluated with and without fungicide, and prediction models were established using four sets of experiments. The grain yield was used as a response trait the others as explanatory traits. The coefficient of determination was considered to evaluate the proposed methodologies. The importance of the traits was assessed through the impact of destructuring or disturbing the information of a given input on the estimation of R2. For machine learning, decision trees, bagging, random forest, and boosting were used. The traits indicated to assist in decision-making are plant height, leaf rust severity, and lodging percentage. The R2 ranged from 30.14% - 96.45% and 10.57% - 94.61% for computational intelligence and machine learning, respectively. A high estimate of the coefficient of determination, which was larger than the other estimates, was obtained using the bagging technique. improvement learning Seventyeight Seventy eight replications fungicide experiments R trees forest decisionmaking making height severity percentage 3014 30 14 30.14 9645 96 45 96.45 1057 10 57 10.57 9461 94 61 94.61 respectively estimates technique 301 3 1 30.1 964 9 4 96.4 105 5 10.5 946 6 94.6 30. 96. 10. 94.

ABSTRACT Species within the genus Corymbia are regarded as potential alternatives to Eucalyptus. In addition to having superior wood quality, Corymbia spp. are tolerant to most pests, diseases, and abiotic stresses that affecting Eucalyptus plantations, including physiological disorders, water deficit, and wind damage. However, environmental stresses stimulate kino production, which decreases the quality of pulp and sawn wood. This study aimed to develop a method for evaluating kinoand estimate genetic parameters in Corymbia. For this, 16 Corymbia (C. citriodora × C. torelliana) hybrid clones and 5 clones of Eucalyptus were used. Two evaluation methods (M1 and M2) were tested for kino evaluation; M1 consisted of drilling the bark with Pilodyn and M2 consisted of drilling the heartwood with Pilodyn. The following kino parameters were evaluated: exudation incidence, exudate length which flowed over the stem, and exudate weight. Genetic parameters were estimated by a mixed model method (REML/BLUP). The significance of random effects of the statistical model was tested by the likelihood ratio test. Significant clone effects were obtained for all kino parameters, except for exudate length as assessed by M2. Kino parameters determined by M1 exhibited higher heritability and accuracy. Therefore, M1 should be preferred for kino evaluation in Corymbia. spp pests diseases plantations disorders deficit damage However production this 1 C (C torelliana used M (M evaluated incidence stem weight REML/BLUP. REMLBLUP REML/BLUP . REML BLUP (REML/BLUP) test accuracy Therefore (REML/BLUP

RESUMO Espécies do gênero Corymbia são consideradas potenciais alternativas ao eucalipto. Além de possuir madeira de qualidade superior, Corymbia spp. são tolerantes à maioria das pragas, doenças e estresses abióticos que afetam as plantações de eucalipto, incluindo distúrbios fisiológicos, déficit hídrico e danos causados pelo vento. No entanto, as tensões ambientais estimulam a produção de kino, o que diminui a qualidade da celulose e da madeira serrada. Este estudo teve como objetivo estabelecer um método para avaliação de kino e estimativa de parâmetros genéticos em Corymbia. Para isso foram utilizados 16 clones híbridos de Corymbia (C. citriodora × C. torelliana) e 5 clones de Eucalyptus. Dois métodos (M1 e M2) foram testados para avaliação; M1 consistiu na perfuração da casca com Pilodyn e M2 consistiu na perfuração do cerne com Pilodyn. Foram avaliados os seguintes parâmetros: incidência de exsudação, comprimento do exsudato que fluiu sobre o caule e peso do exsudato. Os parâmetros genéticos foram estimados por um método de modelo misto (REML/BLUP). A significância dos efeitos aleatórios do modelo estatístico foi testada pelo teste da razão de verossimilhança. Efeitos clones significativos foram obtidos para todos os parâmetros, exceto para o comprimento do exsudato avaliado por M2. Os parâmetros determinados por M1 apresentaram maior herdabilidade e precisão. Portanto, M1 deve ser preferido para avaliação de kino em Corymbia. eucalipto superior spp pragas fisiológicos vento entanto serrada 1 C (C torelliana Eucalyptus M (M exsudação REML/BLUP. REMLBLUP REML/BLUP . REML BLUP (REML/BLUP) verossimilhança precisão Portanto (REML/BLUP

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 including informative priors in the estimation of variance components and genetic values in soybean breeding programs. The used phenotypic data refer to the evaluation of 80 soybean genotypes in ten environments over three years. For each evaluated crop year, informative and noninformative priors were used, and the parameters were estimated using the Gibbs sampler algorithm. Parameter estimates from the previous crop year were used as prior information for the next evaluated crop year. The goodness-of-fit was calculated using the deviance information criterion (DIC). Selective accuracy showed the highest values for the models chosen through DIC for both crop years. However, the intervals of the highest posterior density are narrower for all models that adopted informative priors. Adding information into Bayesian inference does not always result in a better model fitting. programs 8 years algorithm goodnessoffit goodness fit DIC. . (DIC) However fitting (DIC

Resumo O objetivo deste trabalho foi comparar o uso de a priori não informativas e informativas em modelos bayesianos, bem como avaliar a viabilidade da inclusão de a priori informativas na estimativa dos componentes de variância e dos valores genotípicos em programas de melhoramento de soja. Os dados fenotípicos utilizados referem-se à avaliação de 80 genótipos de soja, em dez ambientes, ao longo de três anos. Para cada safra avaliada, foram utilizadas a priori informativas e não informativas, e os parâmetros foram estimados com uso do algoritmo de amostragem de Gibbs. As estimativas dos parâmetros da safra anterior foram utilizadas como informação prévia para a próxima safra avaliada. A qualidade do ajuste foi calculada com uso do critério de informação de desvio (DIC). A acurácia seletiva apresentou maiores valores nos modelos escolhidos por meio do DIC, para ambas as safras. No entanto, os intervalos de maior densidade a posteriori são menores para todos os modelos que adotaram a priori informativas. Adicionar informações à inferência bayesiana nem sempre resulta em melhor ajuste ao modelo. bayesianos soja referemse referem se 8 ambientes anos avaliada Gibbs DIC. DIC . (DIC) safras entanto modelo (DIC

Abstract The objective of this work was to evaluate models using the significant single nucleotide polymorphisms (SNPs) detected by marker-assisted selection and genome-wide association, as a fixed effect in the models commonly used in genome-wide selection for F2 population, in comparison with models using all SNPs. For all models, the Bayesian ridge regression method was used. Comparisons between the models were carried out to evaluate the phenotypic and genotypic prediction ability, phenotypic accuracy, selection gain, coincidence index, and processing time. Both methods failed to accurately identify true quantitative trait loci (QTL). The selection based only in the QTL identified by the studied methods elected individuals of low genetic value. The use of a genome-wide selection model – with the significant SNPs found by the genome-wide association as a fixed effect, and the remaining SNPs as a random effect – was the suitable strategy to select superior individuals with high accuracy. The introduction of QTL already described for a given trait into the genome-wide selection model allows of the selection of superior individuals with greater precision. (SNPs markerassisted marker assisted genomewide genome wide F population ability accuracy gain index time QTL. . (QTL) value precision (QTL

Resumo O objetivo deste trabalho foi avaliar modelos que utilizam os nucleotídeos de polimorfismo único significativos (SNPs), encontrados por seleção assistida por marcadores e associação genômica, como um efeito fixo em modelos comumente utilizados na seleção genômica ampla para a população F2, em comparação com o modelo que utiliza todos os SNPs. Utilizou-se para todos modelos o método bayesiano de regressão de crista. Para as comparações entre os modelos, avaliaram-se a capacidade de predição fenotípica e genotípica, a acurácia fenotípica, o ganho de seleção, o índice de coincidência e o tempo de processamento. Ambos os métodos não conseguiram identificar com precisão os verdadeiros loci de características quantitativas (QTL). A seleção baseada apenas nos QTL identificados pelos métodos avaliados elegeu indivíduos de baixo valor genético. O uso de um modelo de seleção genômica ampla -com os SNPs significativos encontrados pela associação genômica como um efeito fixo, e os SNPs restantes como um efeito aleatório – foi a estratégia adequada para selecionar indivíduos superiores com alta precisão. A introdução de QTL já descritos para uma dada característica no modelo de seleção genômica ampla permite a seleção de indivíduos superiores com maior precisão. SNPs, , (SNPs) F2 F Utilizouse Utilizou se crista avaliaramse avaliaram genotípica processamento QTL. . (QTL) genético (SNPs (QTL

ABSTRACT Open-pollinated onion cultivars predominate in the southern region of Brazil, due to their higher adaptability to local climatic conditions, unlike commercial hybrids, which have shown a lower adaptability. Cytoplasmic male sterility (CMS) systems are employed to develop hybrid onion cultivars. Two molecular markers, 5'cob and orfA501, were used to differentiate the cytoplasm type, and the AcSKP1 marker to identify the nuclear male fertility-restoring locus (Ms). A total of 1,126 plants from the most common onion cultivars grown in southern Brazil, including Bola Precoce®, Crioula®, Valessul®, Mega®, Joia® and Princesa do Sul®, were analyzed using all the three markers. An extremely rare occurrence of the S cytoplasm was observed among the cultivars, being detected in only 1.8 % of the samples, while the T cytoplasm was the most prevalent, accounting for 56.3 % of the samples. Among the 1,126 plants analyzed, only three exhibited the S cytoplasm and were recessive for the Ms-locus (Smsms). Additionally, 49 plants with the N-cytoplasm (as per the Engelke's classification) and recessive for the nuclear genotype (Nmsms) were identified, 45 of which were pollen producers. The male-fertility restoration occurred in 22.2 % of the crosses between Tmsms male-sterile plants and male-fertile N cytoplasmic-msms plants (as per the Engelke's classification). Openpollinated Open pollinated Brazil conditions hybrids CMS (CMS markers 5cob cob 5 orfA501 orfA type AcSKP fertilityrestoring fertility restoring Ms. Ms . (Ms) 1126 1 126 1,12 Precoce Precoce® Crioula Crioula® Valessul Valessul® Mega Mega® Joia Sul Sul® 18 8 1. samples prevalent 563 56 3 56. Mslocus Smsms. Smsms (Smsms) Additionally 4 Ncytoplasm as Engelkes Engelke s classification Nmsms (Nmsms identified producers malefertility 222 22 2 22. malesterile sterile malefertile fertile cytoplasmicmsms cytoplasmic msms classification. orfA50 (Ms 112 12 1,1 (Smsms orfA5 11 1,

RESUMO O uso de cultivares de polinização livre de cebola predomina na região Sul do Brasil, devido à maior adaptabilidade às condições climáticas locais, enquanto híbridos comerciais têm demonstrado baixa adaptabilidade. A macho-esterilidade citoplasmática (CMS) é utilizada para o desenvolvimento de cultivares híbridas de cebola. Foram utilizados dois marcadores moleculares, 5'cob e orfA501, para diferenciar o tipo de citoplasma, e o marcador AcSKP1 para identificar o gene nuclear Ms responsável pela manutenção ou restauração da macho-fertilidade em cebola. Um total de 1.126 plantas de cultivares de cebola mais comuns no Sul do Brasil, incluindo Bola Precoce®, Crioula®, Valessul®, Mega®, Joia® e Princesa do Sul®, foram analisadas utilizando-se os três marcadores. A ocorrência do citoplasma S mostrou-se extremamente rara nas cultivares analisadas, sendo detectado em somente 1,8 % das amostras, enquanto o citoplasma T foi o mais frequente, sendo detectado em 56,3 % das amostras. Das 1.126 plantas analisadas, apenas três possuíam o citoplasma tipo S e eram recessivas para o locus Ms (Smsms). Também foram identificadas 49 plantas com citoplasma N (de acordo com a classificação de Engelke) e recessivas para o gene nuclear (Nmsms), das quais 45 eram produtoras de pólen. A restauração da macho-fertilidade ocorreu em 22,2 % dos cruzamentos entre plantas macho-estéreis Tmsms e plantas macho-férteis Nmsms (de acordo com a classificação de Engelke). Brasil locais machoesterilidade macho esterilidade CMS (CMS moleculares 5cob cob 5 orfA501 orfA AcSKP machofertilidade fertilidade 1126 1 126 1.12 Precoce Precoce® Crioula Crioula® Valessul Valessul® Mega Mega® Joia Sul® utilizandose utilizando se mostrouse mostrou 18 8 1, amostras frequente 563 56 3 56, Smsms. Smsms . (Smsms) 4 Engelke Nmsms, , (Nmsms) pólen 222 22 2 22, machoestéreis estéreis machoférteis férteis Engelke. orfA50 112 12 1.1 (Smsms (Nmsms orfA5 11 1.

Abstract The objective of this work was to evaluate how heritability and the number of quantitative trait loci (QTL) controlling the trait can influence the prediction of genetic value by genomic selection methods. A prediction equation was established to estimate genetic correlation based on phenotypic correlation, using an F2 population with 1,000 individuals, simulated in different scenarios. Heritability (5, 20, 40, 60, 80, and 99%) and QTL number (60, 120, 180, and 240) varied in each scenario. The following four genomic selection methods were used in the analyses: ridge-regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), Bayesian estimation method B (Bayes B), and reproducing kernel Hilbert spaces regression (RKHS). The phenotypic and genotypic predictive abilities were calculated for each method, and Tukey’s test was used to compare means. The effect of heritability and of the number of QTL controlling the trait was evaluated by the regression analysis. Tukey’s test revealed differences between the methods, with Bayes B and RR-BLUP being superior to the others in almost all scenarios. Heritability presents a positive linear relationship with phenotypic predictive ability and a positive quadratic relationship with genotypic predictive ability. The number of QTL controlling the trait has no relationship with the phenotypic and genotypic predictive abilities. (QTL F 1000 1 000 1,00 individuals scenarios 5, 5 (5 20 40 60 80 99% 99 (60 120 180 240 scenario analyses ridgeregression ridge RRBLUP, RRBLUP RR , (RR-BLUP) GBLUP, GBLUP (GBLUP) B, B) RKHS. RKHS . (RKHS) Tukeys Tukey s means analysis 100 00 1,0 ( 2 4 6 8 9 (6 12 18 24 (RR-BLUP (GBLUP (RKHS 10 0 1,

Resumo O objetivo deste trabalho foi avaliar como a herdabilidade e o número de locos de características quantitativas (QTL) que controla a característica podem influenciar na predição do valor genético por meio de métodos de seleção genômica. Uma equação de predição foi estabelecida para estimar a correlação genética baseada na correlação fenotípica, tendo-se utilizado uma população F2 com 1.000 indivíduos, simulados em diferentes cenários. A herdabilidade (5, 20, 40, 60, 80 e 99%) e o número de QTL (60, 120, 180 e 240) variaram em cada cenário. Os quatro seguintes métodos de seleção genômica foram utilizados nas análises: ridge-regression best linear unbiased prediction (RR-BLUP), BLUP genômico (GBLUP), método bayesiano de estimação B (Bayes B) e reproducing kernel Hilbert spaces regression (RKHS). As habilidades preditivas fenotípicas e genotípicas foram calculadas para cada método, e o teste de Tukey foi utilizado para comparação de médias. O efeito da herdabilidade e do número de QTL que controla a característica foi avaliado por análise de regressão. O teste de Tukey revelou diferenças entre os métodos, sendo que Bayes B e RR-BLUP foram superiores aos demais em quase todos os cenários. A herdabilidade apresenta relação linear positiva com a capacidade preditiva fenotípica e relação quadrática positiva com a capacidade preditiva genotípica. O número de QTL controlando a característica não tem relação com a capacidade preditiva fenotípica e genotípica. (QTL tendose tendo se F 1000 1 000 1.00 indivíduos cenários 5, 5 (5 20 40 60 8 99% 99 (60 120 18 240 cenário análises ridgeregression ridge RRBLUP, RRBLUP RR , (RR-BLUP) GBLUP, GBLUP (GBLUP) RKHS. RKHS . (RKHS) médias regressão genotípica 100 00 1.0 ( 2 4 6 9 (6 12 24 (RR-BLUP (GBLUP (RKHS 10 0 1.

Abstract Species of the genus Cynodon are among the most cultivated forage crops in the world due to their high yield and nutritional quality, and its use in cattle feeding has been associated with gains in animal weight and increased milk production. The objective of this study was to model covariance structures in Cynodon spp. clones and study the changes in ranking of the selected genotypes, since it is believed that affects the ranking of genotypes. A total of 202 genotypes were evaluated in an experiment conducted in an augmented block design with four replications and four harvests. The genotypes were assessed for plant height, green weight, percentage of dry matter, and plant vigor. Nineteen repeated measures models with different covariance structures were tested. The best-fitted model adopts the CORH covariance structure for the genetic effects. Correct modeling of the covariance structure affected the ranking of genotypes in all variables evaluated. quality production spp 20 harvests height matter vigor tested bestfitted best fitted effects 2

ABSTRACT present study aimed to determine the effects of different light restriction levels (shading levels) on soybean genetic parameters using a Bayesian multi-trait model (MTM) and select high-yielding soybean cultivars. Eighteen commercial soybean cultivars bred in a soybean breeding program were evaluated over two agricultural seasons. Three shading levels were used over two agricultural crop seasons, giving six treatments (light restriction × crop season). The experiments were arranged in a randomized complete block design with six treatments replicated thrice. The genetic values and parameters were estimated using a Monte Carlo Markov Chain algorithm. Broad-sense heritability range from 0.2093 to 0.7153. The lowest genotypic variance estimate was observed at the 45 % photosynthetically active radiation level in the 2019/2020 crop season year compared with that of other shading levels. Furthermore, a 40 % selection intensity had the highest soybean yield under different shading levels. The Bayesian MTM combined with the factor analysis and genotype-ideotype distance method can be used to evaluate and select soybean genotypes considering different shading levels. The soybean cultivars 8579RSF, NS8338, NS7901, NS7667, RK8115, and 8473RSF had higher genetic potential than other cultivars under different shading levels. multitrait multi trait (MTM highyielding high yielding seasons season. . season) thrice algorithm Broadsense Broad sense 02093 0 2093 0.209 07153 7153 0.7153 4 20192020 2019 2020 2019/202 Furthermore genotypeideotype genotype ideotype 8579RSF RSF NS8338 NS NS7901 NS7667 RK8115 RK 0209 209 0.20 0715 715 0.715 2019202 201 202 2019/20 NS833 NS790 NS766 RK811 020 20 0.2 071 71 0.71 201920 2019/2 NS83 NS79 NS76 RK81 02 2 0. 07 7 0.7 20192 2019/ NS8 NS7 RK8

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. Objectives patients Methods study analyzed Results assessed 1 layer validation 623 62 3 62.3% Conclusions 6 62.3 62.

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. Objetivos críticos Métodos metodológico múltiple forest Resultados evaluados 1 oculta datos 623 62 3 62,3% Conclusiones 6 62,3 62,

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. Objetivos críticos Métodos metodológico múltipla forest Resultados avaliados 1 oculta dados 623 62 3 62,3% Conclusões 6 62,3 62,

ABSTRACT. The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice. ABSTRACT Thus floodirrigated flood irrigated regression inputs flowering 1

Abstract 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.

Abstract The objective of this work was to estimate the genetic parameters and to evaluate the genotypic diversity of 12 'Tahiti' acid lime (Citrus latifolia) genotypes grafted onto two rootstocks. The experiment was carried out from July 2017 to January 2019, in the municipality of São Mateus, in the state of Espírito Santo, Brazil. Vegetative (stem diameter, canopy projection diameter, and plant height), productive (yield and fruit number), and fruit quality (fruit diameter, soluble solids content, and juice yield) traits were determined. A mixed model was used to estimate heritability and repeatability coefficients, as well as to predict clonal values. Scion diversity was determined through the standardized mean difference of Euclidean distances, and genotypes were clustered by modified Tocher. Clustering quality and trait importance were evaluated using the principal component analysis (PCA). Genotypic variance was observed, which is indicative of the possibility of selection of superior genotypes. The Tocher method showed the presence of three clusters, which is in alignment with the PCA results. The multivariate analyses allows of the selection and the recommendation of superior 'Tahiti' acid lime genotypes.

Resumo O objetivo deste trabalho foi estimar os parâmetros genéticos e avaliar a diversidade genética de 12 genótipos de lima ácida 'Tahiti' (Citrus latifolia) enxertados em dois porta-enxertos. O experimento foi conduzido de julho de 2017 a janeiro de 2019, no município de São Mateus, no estado de Espírito Santo, Brasil. Foram mensuradas características vegetativas (diâmetro de caule, diâmetro de projeção de copa e altura de planta), produtivas (rendimento e número de frutos) e de qualidade de frutos (diâmetros de fruto, teor de sólidos solúveis e rendimento de suco). Utilizou-se modelo misto para estimar os coeficientes de herdabilidade e repetibilidade, bem como predizer os valores genéticos. A diversidade entre os enxertos foi obtida por meio da diferença da distância euclidiana média padronizada, e os genótipos foram agrupados pelo método de Tocher modificado. A qualidade do agrupamento e a importância dos caracteres foi avaliada pela análise de componentes principais (ACP). Observou-se variabilidade genética, o que indica a possibilidade de seleção de genótipos superiores. O método de Tocher mostrou a presença de três grupos de genótipos, o que foi congruente com os resultados da ACP. O uso de técnicas multivariadas permite a seleção e a recomendação de genótipos superiores de lima ácida 'Tahiti'.

ABSTRACT. Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones.