On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population’s intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome

Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes

Soil-app: a tool for soil analysis interpretation

New apps have changed the traditional way of learning and teaching; they are also applied as a quickly executed and effective method in agriculture. Soil-app is a web application with a friendly click-point interface built through packages lodged in R software. The app is an advanced model of an open-source platform to support teaching and learning activities in soil analyses and fertilizer recommendations. Soil-app includes soil test interpretation, soil amendment calculations (lime and gypsum), the fertilizer rate for the most important crops in Brazil, an NPK blend calculator, and NPK blend evaluation. It also includes experimental statistical analysis as applied to soil science. Soil-app is a user-friendly and high-performance tool, garnering fast adoption by both students and professionals. It is available for network use through the following link: http://www.genetica.esalq.usp.br/alogamas/R.htm

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions

Predição de sementes haploides de milho usando aprendizado profundo e utilização de genomas mock para a predição genômica de híbridos

Prediction is a key concept for animal and plant breeding. Accurate estimates of phenotypic and genetic values are crucial for the selection of the best genotypes. For this reason, several tools have been used to improve the accuracy of these estimates, from molecular markers, used to access genetic information, to high-throughput phenotyping, used to increase sample size and phenotypic precision. Here, we present two studies involving the use of different approaches and tools in the prediction process. First, we describe a study using deep learning and images for seed phenotyping. We built a convolutional neural network (CNN) model to classify images from putative and true haploid maize seeds based on the R1-nj phenotype. Our results reveal that the CNN model could classify putative haploid maize seeds with high accuracy (97%). However, the CNN model was unable to recognize true haploid seeds. Finally, we provide a highly accurate and trained CNN model to the scientific community to classify haploid maize seeds via R1-nj. In the latter, we studied using mock genomes to discover markers and their effect on estimates of genetic diversity and genomic prediction of hybrids. Moreover, we compared them with SNP markers from SNP-array and genotyping-by-sequencing (GBS) scored in the reference genome B73. Our results show that using mock genomes delivers estimates comparable to standard platforms when considering simple traits and additive effects. However, for complex traits and dominance effects, the estimates were slightly worse. We believe that these studies provide relevant knowledge for the phenotypic and genomic prediction applied to plant breeding.A predição é um conceito chave para o melhoramento animal e de plantas. Estimativas acuradas dos valores fenotípicos e genéticos são fundamentais para a seleção dos melhores genótipos. Por isso, diversas ferramentas vêm sendo empregadas com o objetivo de melhorar a precisão dessas estimativas, desde marcadores moleculares, usados para acessar a informação genética, até a fenotipagem de alto rendimento, usada para aumentar o tamanho da amostra e a precisão fenotípica. Nesse trabalho, nós apresentamos dois estudos envolvendo o uso de diferentes abordagens e ferramentas no processo de predição. No primeiro capítulo, nós apresentamos um estudo envolvendo o uso de deep learning e imagens para a fenotipagem de sementes. Nele, nós construímos um modelo de rede neural convolucional (CNN) para classificar imagens de sementes haploides putativas e verdadeiras de milho baseadas no fenótipo R1-nj. Nossos resultados mostram que o modelo CNN foi capaz de classificar as sementes putativas com elevada acurácia (97%). No entanto, o modelo não conseguiu detectar as sementes haploides verdadeiras. Por fim, nós disponibilizamos à comunidade científica um modelo CNN treinado e com alta acurácia para classificar sementes haploides de milho. No último capítulo, nós estudamos a utilização de genomas mock para a descoberta de marcadores e o seu efeito sobre estimativas de diversidade genética e predição genômica de híbridos. Além disso, nós os comparamos com marcadores SNP oriundos de um SNP-array e genotyping-by-sequencing (GBS) ancorado no genoma de referência B73. Nossos resultados mostram que a utilização de genomas mock entrega estimativas comparáveis às plataformas padrão, quando consideramos caracteres simples e efeitos aditivos. No entanto, para caracteres complexos e para os efeitos de dominância as estimativas foram um pouco piores. Nós acreditamos que esses trabalhos adicionam conhecimento relevante para a predição fenotípica e genômica aplicado ao melhoramento vegetal

Agrometeorological model evaluation for sugarcane (Saccharum spp.) genotype tolerance to water stress

O aumento da demanda por energias renováveis proporcionou um intenso crescimento do setor sucroenergético. Essa expansão fez com que a cana-deaçúcar passasse a ocupar novas regiões agrícolas, em especial o Cerrado brasileiro. No entanto, essas novas regiões enfrentam grandes desafios para a produção da cultura, no qual o déficit hídrico é o de maior importância. Sabendo-se disso, o conhecimento da tolerância diferencial de genótipos de cana-de-açúcar ao déficit hídrico, se torna importante ferramenta para o manejo do sistema de produção. O Método da Zona Agroecológica (MZA), também conhecido como Modelo da FAO, estima a produtividade potencial e atingível das culturas agrícolas, mediante a entrada de variáveis meteorológicas. O Modelo da FAO determina que a depleção da produtividade ocorre em função do déficit hídrico relativo (1- ETr/ETC), através de um coeficiente de resposta ao déficit hídrico, denominado Ky. Nesse sentido, o presente trabalho teve como objetivo verificar a tolerância de genótipos de cana-deaçúcar ao déficit hídrico, mediante o uso desse modelo. Para isso, foram necessários os dados de produtividade real de 18 genótipos em 79 ensaios diferentes do Programa de Melhoramento Genético de Cana-de-açúcar do Instituto Agronômico de Campinas (Programa Cana/IAC), entre os anos de 2009 a 2011. Os dados meteorológicos utilizados pelo modelo, para cada experimento e período, foram obtidos das estações meteorológicas mais próximas para cada local. Visto os dados dos coeficientes de resposta ao déficit hídrico (Ky), existentes na literatura, não condizerem com estudos sobre os estádios fenológicos de maior sensibilidade ao estresse hídrico, o modelo foi calibrado e os resultados foram condizentes com diversos autores. O período de maior sensibilidade à deficiência hídrica, encontrado pelo modelo, foi o estádio de desenvolvimento máximo da cultura, ou seja, o período de elongação dos colmos, onde o valor de Ky é 0,72. O Método da Zona Agroecológica estimou a produtividade atingível com um índice de concordância de 0,75, o que é considerado bom para os modelos de estimativa. As estimativas, para cada genótipo, mostraram valores de erro médio que variaram de -1,7 a 5,4 t.ha-1. Considerando a verificação da tolerância diferencial dos genótipos de cana-deaçúcar ao déficit hídrico, o Modelo da FAO identificou os genótipos IACSP974039, IACSP994010 e RB867515 como os de maior tolerância.The increased demand for renewable energy lead to an intense growth of the sugarcane industry. This expansion made the sugarcane pass to occupy other restrictive environments, especially regions with high water deficit. Knowing this, the knowledge of the differential tolerance of sugarcane genotypes to water stress becomes an important tool for the management of the production system. The Agroecological Zone Method (AZE), also known as FAO Model, estimates the potential and actual productivities of crops from meteorological variables. This model determines the depletion of productivity as a function of the relative water deficit (1 - ETr/ETc) and a water deficit sensitivity index, named Ky. Accordingly, this study aimed to determine the tolerance of sugarcane genotypes to water stress, through this model to estimate the potential and actual productivities. For this, real productivity data of 18 sugarcane genotypes grown in 79 different trials were taken from the Sugarcane Breeding Program from the Agronomic Institute of Campinas (Programa Cana/IAC) between the years 2009-2011. The meteorological data used by the model, for each experiment and period, was obtained from the closest meteorological stations. Since the original data water deficit sensitivity index (Ky) and duality found in the literature, in other studies, due to periods of greatest sensitivity to water stress, the model was calibrated and consistent results were obtained in relation to several authors. It was found that the period of greatest sensitivity of the sugarcane occurs at the stage of stem elongation, when the value of Ky is 0.72. The Agroecological Zone Method estimated the actual productivity with a performance index of 0.75, which is considered good. Estimation showed values of mean bias error, estimated for each genotype, ranged from -1.7 to 5.4 t.ha-1. Regarding the verification of the tolerance of the different genotypes to water stress, the FAO Model identified the genotypes IACSP974039, IACSP994010 and RB867515 as those of greatest tolerance to water stress

Design of Pacifiers

BackgroundA pacifier is an object designed for use by children aged two weeks to around five years old. It. Although a convenient and an efficient alternative to thumb sucking, its use is highly questionable. With the advancement of medicine and technology, harms related to its use were discovered and were related to poor development of the teeth. Furthermore, the use of some industrial raw materials may cause damage to the child’s health during the growing process. Method  This study evaluates different models of pacifiers available on the market, taking into consideration the design, materials and attendance to Brazilian Standards.ResultsThe Fourier Transform Infrared Spectroscopy (FTIR) analysis of the five different models of pacifiers indicates the use of different materials. For models A, B and E, the nipple is basically composed of silicone, while the guard is made of polycarbonate. For model C and D, the nipple is basically composed of natural rubber, while the guard and the ring are made of polycarbonate. For model D and E, however, the presence of Bisphenol A (BPA) was also used in composition. For the tensile strength tests, only model C was disapproved.ConclusionSilicon and natural rubber satisfy the requirements for technical performance. However, this does not take into account hygiene and toxicity as parameters for the selection, which are also important when considering child health

DataSheet_1_Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets.pdf

Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.</p

Processo de controle seletivo de populações de artrópodes hematófagos usando inibidores das enzimas do metabolismo da tirosina

Universidade Federal do Rio de JaneiroUniversidade Federal do Rio Grande do SulVeterináriaDepositad

Processo de controle seletivo de populações de artrópodes hematófagos usando inibidores das enzimas do metabolismo da tirosina

Universidade Federal do Rio de JaneiroUniversidade Federal do Rio Grande do SulVeterináriaDepositad