Navegando por Autor "Nascimento, Moysés"

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Abordagem bayesiana para avaliação da adaptabilidade e estabilidade de genótipos de Alfafa
(Pesquisa Agropecuária Brasileira, 2010-12-14) Nascimento, Moysés; Silva, Fabyano Fonseca e; Sáfadi, Thelma; Nascimento, Ana Carolina Campana; Ferreira, Reinaldo de Paula; Cruz, Cosme Damião
O objetivo deste trabalho foi propor uma abordagem bayesiana do método de Eberhart & Russell para avaliar a adaptabilidade e da estabilidade fenotípica de genótipos de alfafa (Medicago sativa), bem como avaliar a eficiência da utilização de distribuições a priori informativas e pouco informativas. Foram utilizados dados de um experimento em blocos ao acaso, no qual se avaliou a produção de massa de matéria seca de 92 genótipos. A metodologia bayesiana proposta foi implementada no programa livre R por meio da função MCMCregress do pacote MCMCpack. Para representar as distribuições a priori pouco informativas, utilizaram-se distribuições de probabilidade com grande variância; e, para representar distribuições a priori informativas, adotou-se o conceito de meta-análise, que se caracteriza pela utilização de informações provenientes de trabalhos anteriores. A comparação entre as distribuições a priori foi realizada por meio do fator de Bayes, com a função BayesFactor do pacote MCMCpack, que indicou a priori informativa como a mais adequada nas condições deste estudo.
Adaptability and stability assessment of bean cultivars of the carioca commercial group by a Bayesian approach
(Acta Scientiarum. Agronomy, 2018-07) Nascimento, Moysés; Euzebio, Milena Pierotti; Fonseca, Inês Cristina de Batista; Fonseca Júnior, Nelson da Silva; Giordani, Willian; Gonçalves, Leandro Simões Azeredo
To develop new bean commercial cultivars, a series of experiments called Value for Cultivation and Use (VCU) assays are necessary. Bayesian analysis using information on prior VCU trials is an alternative to obtain greater precision during genotype selection. The objective of the present work was to select, under a Bayesian perspective, genotypes of the carioca bean from the state of Paraná that combine high adaptability and phenotypic stability, using information from previous VCU assays. This study used data from six experiments conducted in a randomized block design, in which the grain yield of 18 genotypes was assayed. To represent weakly informative prior distributions, the study used probability distributions with high variance; to represent informative prior distributions, it adopted the meta-analysis concept used in prior VCU assays (2007/2008, 2008/2009, 2009/2010, 2010/2011, 2011/2012, 2012/2013, and 2013/2014). Bayesian inference provided greater precision in selecting carioca bean genotypes with high adaptability and phenotypical stability through the Eberhart and Russell method. The Bayes factor indicated that the use of a priori information gives more accurate results for genotype selection. According to the study, most genotypes are widely adaptable based on informative priors, except for the Bola Cheia cultivar, which has specific adaptability to favorable environments.
Aplicação da análise de agrupamento de dados de expressão gênica temporal a dados em painel
(Pesquisa Agropecuária Brasileira, 2011-10-02) Nascimento, Moysés; Sáfadi, Thelma; Silva, Fabyano Fonseca e
O objetivo deste trabalho foi determinar a melhor alternativa, entre os métodos de agrupamento hierárquico (Ward) e de otimização (Tocher), para a formação de grupos homogêneos de séries de expressão gênica, e realizar previsões quanto à expressão gênica dessas séries, a partir de pequeno número de observações temporais. Os dados utilizados referem-se à expressão de genes que atuam sobre o ciclo celular de Saccharomyces cerevisiae e corresponderam a 114 séries de expressão gênica, cada uma com dez valores de "fold-change" (medida da expressão gênica) ao longo do tempo (0, 15, 30, 45, 60, 75, 90, 105, 120 e 135 min). As estimativas dos parâmetros dos modelos autorregressivos AR(p) foram previamente ajustadas a séries individuais (de cada gene) de dados "microarray time series" e utilizadas, como variáveis, no processo de agrupamento. As previsões da expressão gênica foram feitas dentro de cada grupo formado, a partir dos ajustes no modelo AR(p) para dados em painel. O método de Ward foi o mais apropriado para a formação de grupos de genes com séries homogêneas. Uma vez obtidos esses grupos, é possível ajustar o modelo AR(2) para dados em painel e predizer a expressão gênica em um tempo futuro (135 min), a partir de um pequeno número de observações temporais (os outros nove valores de "fold-change").
Application of neural networks to predict volume in eucalyptus
(Crop Breeding and Applied Biotechnology, 2015-03-16) Bhering, Leonardo Lopes; Cruz, Cosme Damião; Peixoto, Leonardo de Azevedo; Rosado, Antônio Marcos; Nascimento, Moysés; Laviola, Bruno Galveas
The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models.
Artificial neural networks and linear discriminant analysis in early selection among sugarcane families
(Crop Breeding and Applied Biotechnology, 2017-10) Peternelli, Luiz Alexandre; Moreira, Édimo Fernando Alves; Nascimento, Moysés; Cruz, Cosme Damião
One of the major challenges in sugarcane breeding programs is an efficient selection of genotypes in the initial phase. The purpose of this study was to compare modelling by artificial neural networks (ANN) and linear discriminant analysis (LDA) as alternatives for the selection of promising sugarcane families based on the indirect traits number of sugarcane stalks (NS), stalk diameter (SD) and stalk height (SH). The analysis focused on two models, a full one with all predictors, and a reduced one, from which the variable SH was excluded. To compare and assess the applied methods, the apparent error rate (AER) and true positive rate (TPR) were used, derived from the confusion matrix. Modeling with ANN and LDA can be used successfully for selection among sugarcane families. The reduced model may be preferable, for having a low AER, high TPR and being easier to obtain in operational terms.
Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee
(Pesquisa Agropecuária Brasileira, 2017-03) 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
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, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.
Association between responses obtained using adaptability and stability methods in Alfalfa
(Semina: Ciências Agrárias, 2013-09-05) Nascimento, Moysés; Nascimento, Ana Carolina Campana; Cirillo, Marcelo Ângelo; Ferreira, Adésio; Peternelli, Luiz Alexandre; Paula, Reinaldo Ferreira de
This study aimed to evaluate the association between responses obtained using methods of adaptability and stability by using correspondence analysis. The forage yield of 92 genotypes of alfalfa (Medicago sativa L.) was investigated. The trial had a randomized block design, with 2 replicates, and the data were used to test the reliability of the different methods. Twenty cuttings were obtained from each genotype between November 2004 and June 2006. Each cutting was grown in a different environment. The estimates of adaptability and stability were obtained using the methods of Eberhart and Russell (1966), Cruz, Torres and Vencovsky (1989), Nascimento et al. (2009b), and Lin and Binns (1988). Following the association analysis, correspondence analysis was conducted for determining association and discriminating the responses obtained using the methods of adaptability and stability. The unfavorable (D) and unpredictable unfavorable (DI) responses obtained using the methods of Lin and Binns (1988) and Eberhart and Russell (1966), respectively, were discrepant in relation to other responses obtained using these methods. The greatest association between responses was confirmed using the methods of Eberhart and Russell (1966) and Cruz, Torres and Vencovsky (1989).
Avaliação do coeficiente de variação experimental para caracteres de frutos de pimenteiras
(Revista Ceres, 2011-01) Silva, Anderson Rodrigo da; Cecon, Paulo Roberto; Rêgo, Elizanilda Ramalho do; Nascimento, Moysés
O objetivo deste estudo foi formular uma classificação para coeficientes de variação, de experimentos com pimentas do gênero Capsicum, para utilização em seis variáveis morfológicas do fruto. Foram selecionados 38 experimentos com resultados publicados, em que se constaram dados de caracterizações morfológicas do fruto e coeficientes de variação (CV) das seguintes variáveis respostas: peso médio do fruto, comprimento do fruto, comprimento do pedúnculo, maior e menor diâmetro do fruto e espessura do pericarpo. As faixas de classificação dos CV foram baseadas na metodologia proposta por Garcia (1989), que considera a propriedade da distribuição normal do CV. Pelo teste de Lilliefors, a 5% de probabilidade, todas as variáveis satisfizeram a pressuposição de distribuição normal do CV. As faixas de classificação do CV, obtidas para essas seis variáveis, foram distintas daquelas da classificação geral, proposta por Gomes (2000). Ademais, constatou-se que as classificações dos coeficientes de variação de variáveis morfológicas de pimenteiras do gênero Capsicum dependem da variável resposta, sendo que as maiores faixas de classificação de CV ocorreram para menor diâmetro do fruto, espessura do pericarpo e peso médio do fruto.
Bayesian inference for the fitting of dry matter accumulation curves in garlic plants
(Pesquisa Agropecuária Brasileira, 2016-12-01) Macedo, Leandro Roberto de; Cecon, Paulo Roberto; Silva, Fabyano Fonseca e; Nascimento, Moysés; Puiatti, Guilherme Alves; Oliveira, Ana Carolina Ribeiro de; Puiatti, Mario
The objective of this work was to identify nonlinear regression models that best describe dry matter accumulation curves over time, in garlic (Allium sativum) accessions, using Bayesian and frequentist approaches. Multivariate cluster analyses were made to group similar accessions according to the estimates of the parameters with biological interpretation (β1 and β3). In order to verify if the obtained groups were equal, statistical tests were applied to assess the parameter equality of the representative curves of each group. Thirty garlic accessions were used, which are kept by the vegetable germplasm bank of Universidade Federal de Viçosa, Brazil. The logistic model was the one that fit best to data in both approaches. Parameter estimates of this model were subjected to the cluster analysis using Ward’s algorithm, and the generalized Mahalanobis distance was used as a measure of dissimilarity. The optimal number of groups, according to the Mojena method, was three and four, for the frequentist and Bayesian approaches, respectively. Hypothesis tests for the parameter equality from estimated curves, for each identified group, indicated that both approaches highlight the differences between the accessions identified in the cluster analysis. Therefore, both approaches are recommended for this kind of study.
Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding
(Journal of Applied Animal Research, 2018) Silva, Fabyano Fonseca; Jerez, Elcer Albenis Zamora; Resende, Marcos Deon Vilela de; Viana, José Marcelo Soriano; Azevedo, Camila Ferreira; Lopes, Paulo Sávio; Nascimento, Moysés; Lima, Rodrigo Oliveira de; Guimarães, Simone Eliza Facioni
We combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ‘LALDA’) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression – BRR; Bayes A – BA; Bayes B – BB; Bayes C – BC and Bayesian LASSO – BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets.
Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach
(Bioinformatics, 2012-06-04) Nascimento, Moysés; Sáfadi, Thelma; Silva, Fabyano Fonseca e; Nascimento, Ana Carolina C.
In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering. The proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model. The R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web http://www.det.ufv.br/∼moyses/links.php.
Characterization of the consumer market and motivations for the consumption of craft beer
(British Food Journal, 2018) Carvalho, Naiara Barbosa; Minim, Luis Antonio; Nascimento, Moysés; Ferreira, Gustavo Henrique de Castro; Minim, Valéria Paula Rodrigues
The purpose of this paper is to determine the demographic characteristics and habits of craft beer consumers, as well as to identify the motivational factors for consumption.Data were collected through questionnaires applied to 316 Brazilian craft beer consumers, and results were evaluated descriptively and by multivariate statistics.The results of the survey revealed that there is a growing market segment with different buying habits and behaviors compared to traditional beer consumers. Demographically, it was found that these consumers are an attractive part of the beer market in terms of age, schooling and, more importantly, in terms of income, factors that indicate the probability of continued growth in the sector.The research was limited to craft beer consumers in the metropolitan region of Belo Horizonte/MG, Brazil.The results obtained are important, as they can help new craft breweries, as well as help established industry managers to create strategies related to marketing four Ps in order to increase the consumption of its products, with competitive advantages to the market.This research presents the characteristics of the consumers of craft beer, a market segment in evident rise in Brazil, about which there are few studies. In addition, it provides valuable information to both the new beverage manufacturers as well as to the already established entrepreneurs in the market so that they can increase the consumption of their products in a strategic way.
Comparison between simulated annealing algorithms and rapid chain delineation in the construction of genetic maps
(Genetics and Molecular Biology, 2009-11-17) Nascimento, Moysés; Cruz, Cosme Damião; Peternelli, Luiz Alexandre; Campana, Ana Carolina Mota
The efficiency of simulated annealing algorithms and rapid chain delineation in establishing the best linkage order, when constructing genetic maps, was evaluated. Linkage refers to the phenomenon by which two or more genes, or even more molecular markers, can be present in the same chromosome or linkage group. In order to evaluate the capacity of algorithms, four F2 co-dominant populations, 50, 100, 200 and 1000 in size, were simulated. For each population, a genome with four linkage groups (100 cM) was generated. The linkage groups possessed 51, 21, 11 and 6 marks, respectively, and a corresponding distance of 2, 5, 10 and 20 cM between adjacent marks, thereby causing various degrees of saturation. For very saturated groups, with an adjacent distance between marks of 2 cM and in greater number, i.e., 51, the method based upon stochastic simulation by simulated annealing presented orders with distances equivalent to or lower than rapid chain delineation. Otherwise, the two methods were commensurate through presenting the same SARF distance.
Direct, indirect and simultaneous selection as strategies for alfalfa breeding on forage yield and nutritive value
(Pesquisa Agropecuária Tropical, 2018-04) Santos, Iara Gonçalves dos; Cruz, Cosme Damião; Nascimento, Moysés; Rosado, Renato Domiciano Silva; Ferreira, Reinaldo de Paula
Alfalfa breeding aimed at trait improvement for livestock feed takes longer periods of time, if compared to many other crops. Therefore, better selection methods are necessary for the success of alfalfa breeding programs. Although knowing about selection methods is quite important, there is a notable lack of information, as regards successful solutions. This study aimed to use direct, indirect and simultaneous selection methods for selecting alfalfa cultivars, based on yield traits and nutritive value. The evaluated traits were subdivided into two groups: forage yield and nutritive value. Selection gains were estimated by direct, indirect and simultaneous selection for each group, considering the selection of the 25 % best cultivars. Direct and indirect selections among genotype averages are not efficient to provide the desirable responses to the whole set of traits. The results for simultaneous selection, using the Tai index, provided a more balanced gain distribution to the set of traits in all cuts. The simultaneous selection allowed the identification of the 5681 and Verdor cultivars in the first cut, as well as ProINTA Patricia in the second cut, as superior in the two groups of evaluated traits.
The Eberhart and Russel’s bayesian method used as an instrument to select maize hybrids
(Euphytica, 2018-03-08) Nascimento, Moysés; Oliveira, Tâmara Rebecca Albuquerque de; Carvalho, Hélio Wilson Lemos de; Costa, Emiliano Fernandes Nassau; Amaral Junior, Antonio Teixeira do; Gravina, Geraldo de Amaral; Carvalho Filho, José Luiz Sandes de
Adaptability and stability analysis methods that use a priori information allow identifying and selecting potentially productive genotypes with greater accuracy. The aim of the current study is to use the Eberhart and Russel’ Bayesian method as an instrument to analyze the adaptability and stability of hybrid maize cultivars and to assess the efficiency of using the distribution of informative and non-informative priors to select cultivars. Twenty-five (25) hybrid maize cultivars were assessed in 11 environments located in the Brazilian Northeastern region, during 2012 and 2013, according to a complete randomized block design, with two repetitions. The Eberhart and Russel’s methodology was performed in the GENES software, whereas the Bayesian procedure was implemented in the free software R, by using the MCMCregress function of the MCMCpack package. The adaptability and stability parameters values and the credibility intervals have shown that the Eberhart and Russel’s method via Bayesian technique has shown greater stability-estimation accuracy and greater efficiency in recommending cultivars adapted to favorable and unfavorable environments. The Bayesian methods using priories informative (M1) and few informative (M2) distributions have presented the same genotype classifications in the comparison between a priori distributions; however, according to the Bayes Factor, the M1 was the most adequate distribution to help finding more reliable estimates.
Eficiência técnica da atividade leiteira em Minas Gerais: uma aplicação de regressão quantílica
(Revista Brasileira de Zootecnia, 2011-09-21) Nascimento, Ana Carolina Campana; Lima, João Eustáquio de; Braga, Marcelo José; Nascimento, Moysés; Gomes, Adriano Provezano
O objetivo principal neste estudo foi analisar a influência de variáveis técnicas e econômicas sobre os índices de eficiência técnica de produtores de leite de Minas Gerais ao longo de pontos distintos da distribuição dos índices de eficiência utilizando-se a técnica de regressão quantílica. Os índices de eficiência técnica foram estimados com base em um modelo de fronteira estocástica utilizando-se dados de 875 produtores de leite do estado de Minas Gerais coletados no ano de 2005. Os principais resultados revelaram, na fronteira de produção, que possivelmente está havendo utilização extensiva do fator terra. De modo geral, a variável percentual de vacas em lactação foi a mais relevante na explicação da eficiência técnica em todos os quantis estudados, enquanto o percentual de mão-de-obra familiar utilizado foi importante para explicar apenas os menores níveis de eficiência. Além disso, foi encontrada diferença significativa entre os coeficientes estimados dos quantis em estudo, o que mostra que as variáveis explicativas não têm o mesmo impacto no aumento da eficiência em todos os pontos da distribuição.
Fuzzy control systems for decision-making in cultivars recommendation
(Acta Scientiarum. Agronomy, 2018) Carneiro, Vinícius Quintão; Prado, Adalgisa Leles do; Cruz, Cosme Damião; Carneiro, Pedro Crescêncio Souza; Nascimento, Moysés; Carneiro, José Eustáquio de Souza
The objective of the present study was to propose fuzzy control systems to support the recommendation of cultivars of different agronomic crops. Grain yield data from 23 lines and 2 cultivars of red bean were used to evaluate the applicability of these controllers. Genotypes were evaluated in nine environments in the Zona da Mata region, Minas Gerais State, Brazil. Using the parameters of Eberhart and Russell analysis, fuzzy controllers were developed with the Mamdani and Sugeno inference systems. Analyses of adaptability and stability were carried out by the method of Eberhart and Russell. The parameters obtained for each genotype were submitted to the respective controllers. There were significant genotypes x environments interaction, which justified the necessity of performing an adaptability and stability analysis. For both controllers (Mamdani and Sugeno), seven lines presented general adaptability, while only one presented adaptability to unfavorable environments. It was also found that both inference systems were useful for developing controllers that had the aim of recommending cultivars. Thus, it was noted that fuzzy control systems have the potential to identify the behavior of bean genotypes.
Genomic sequence of the yeast Kluyveromyces marxianus CCT 7735 (UFV-3), a highly lactose-fermenting yeast isolated from the Brazilian dairy industry
(Genome Announcements, 2014-11-06) Silveira, Wendel B.; Diniz, Raphael H. S.; Cerdán, M. Esperanza; González-Siso, María I.; Souza, Robson de A; Vidigal, Pedro M. P.; Brustolini, Otávio J. B.; Prata, Emille R. B. de Almeida; Medeiros, Alexsandra C.; Paiva, Lílian C.; Nascimento, Moysés; Ferreira, Éder G.; Santos, Valdilene C. dos; Bragança, Caio R. S.; Fernandes, Tatiana A. R.; Colombo, Lívia T.; Passos, Flávia M. L.
Here, we present the draft genome sequence of Kluyveromyces marxianus CCT 7735 (UFV-3), including the eight chromosomes and the mitochondrial genomic sequences.
GenomicLand: Software for genome-wide association studies and genomic prediction
(Acta Scientiarum. Agronomy, 2019) Nascimento, Moysés; Fontes, Vitor Cunha; Silva, Fabyano Fonseca e; Resende, Marcos Deon Vilela de; Cruz, Cosme Damião
GenomicLand is free software intended for prediction and genomic association studies based on the R software. This computational tool has an intuitive interface and supports large genomic databases, without requiring the user to use the command line. GenomicLand is available in English, can be downloaded from the Internet (https://licaeufv.wordpress.com/), and requires the Windows or Linux operating system. The software includes statistical procedures based on mixed models, Bayesian inference, dimensionality reduction and artificial intelligence. Examples of data files that can be processed by GenomicLand are available. The examples are useful to learn about the operation of the modules and statistical procedures.
Impact of energy restriction during late gestation on the muscle and blood transcriptome of beef calves after preconditioning
(BMC Genomics, 2018) Nascimento, Moysés; Sanglard, Leticia P.; Moriel, Philipe; Sommer, Jeffrey; Ashwell, Melissa; Poore, Matthew H.; Duarte, Márcio de S.; Serão, Nick V. L.
Maternal nutrition has been highlighted as one of the main factors affecting intra-uterine environment. The increase in nutritional requirements by beef cows during late gestation can cause nutritional deficiency in the fetus and impact the fetal regulation of genes associated with myogenesis and immune response.Forty days before the expected calving date, cows were assigned to one of two diets: 100% (control) or 70% (restricted group) of the daily energy requirement. Muscle samples were collected from 12 heifers and 12 steers, and blood samples were collected from 12 steers. The objective of this work was to identify and to assess the biological relevance of differentially expressed genes (DEG) in the skeletal muscle and blood of beef calves born from cows that experienced [or not] a 30% energy restriction during the last 40 days of gestation.A total of 160, 164, and 346 DEG (q-value< 0.05) were identified in the skeletal muscle for the effects of diet, sex, and diet-by-sex interaction, respectively. For blood, 452, 1392, and 155 DEG were identified for the effects of diet, time, and diet-by-time interaction, respectively. For skeletal muscle, results based on diet identified genes involved in muscle metabolism. In muscle, from the 10 most DEG down-regulated in the energy-restricted group (REST), we identified 5 genes associated with muscle metabolism and development: SLCO3A1, ATP6V0D1, SLC2A1, GPC4, and RASD2. In blood, among the 10 most DEG, we found genes related to response to stress up-regulated in the REST after weaning, such as SOD3 and INO80D, and to immune response down-regulated in the REST after vaccination, such as OASL, KLRF1, and LOC104968634.In conclusion, maternal energy restriction during late gestation may limit the expression of genes in the muscle and increase expression in the blood of calves. In addition, enrichment analysis showed that a short-term maternal energy restriction during pregnancy affects the expression of genes related to energy metabolism and muscle contraction, and immunity and stress response in the blood. Therefore, alterations in the intra-uterine environment can modify prenatal development with lasting consequences to adult life.