Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.

Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

Fatores que acarretam insatisfação no trabalho do enfermeiro no contexto hospitalar privado / Factors causing dissatisfaction among nurses in the private hospital context / Factores que llevan a la insatisfacción en el trabajo de enfermería en el contexto hospitalario particular

OBJETIVO: verificar o grau de satisfação do enfermeiro e identificar os fatores causadores de insatisfação no trabalho do enfermeiro dentro do serviço hospitalar. MÉTODOS: estudo exploratório de métodos mistos, realizado em uma instituição hospitalar privada de uma capital do Sul do Brasil. A coleta de dados ocorreu entre os meses de agosto e setembro de 2017 com enfermeiros assistenciais. No primeiro momento foi aplicada uma escala de satisfação no trabalho que está dividida em cinco dimensões: satisfação com os colegas; com o salário; com a chefia; com a natureza do trabalho e com as promoções. No segundo momento, aqueles enfermeiros cujo resultado da escala foi "insatisfeito" foram convidados a responderem uma entrevista semiestruturada. Os dados qualitativos das entrevistas foram analisados segundo a análise de conteúdo temático-categorial, com apoio do software MAXQDA© para organização dos dados. RESULTADOS: participaram da pesquisa 40 enfermeiros, dos quais 10 se manifestaram insatisfeitos com a remuneração inadequada; com a falta de promoção e crescimento profissional; com o regime de trabalho; com a falta de reconhecimento e valorização profissional; com o desvio de função; com a competitividade e desunião entre os profissionais e a equipe multiprofissional; e com a falta de liderança. No entanto, ficou evidenciado que trabalhar com o que gosta e as amizades que se constroem com colegas no trabalho foram fatores de satisfação para os enfermeiros insatisfeitos. CONCLUSÃO: o reconhecimento dos fatores de insatisfação dos enfermeiros pode subsidiar a implantação de um plano institucional para mudar a situação identificada. Trabalhadores satisfeitos são mais motivados e produzem melhor.(AU)

Objective: to verify the nurse's level of satisfaction and to identify the factors causing dissatisfaction in the nurse's work within the hospital service. Methods: an exploratory study of mixed methods, conducted in a private hospital in a capital of southern Brazil. Data collection took place between August and September 2017 with care nurses. At first, a job satisfaction scale was applied, which is divided into five dimensions: satisfaction with co-workers; with pay; with leadership; with the nature of the work; and with promotions. Later, those nurses whose scale result was "dissatisfied" were invited to answer a semi-structured interview. The qualitative data of the interviews were analyzed according to thematic-categorical content analysis, using the MAXQDA© software for data organization. Results: forty nurses participated in the research, of which 10 expressed dissatisfaction with inadequate remuneration; with the lack of promotions and professional growth; with the working arrangements; with the lack of professional recognition and appreciation; with working outside the scope of employment; with the competitiveness and disunity between professionals and the multidisciplinary team; and lack of leadership. However, it was evidenced that working with what they like and the friendships that are built with co-workers at work were satisfaction factors for dissatisfied nurses. Conclusion: recognition of the nurses' dissatisfaction factors...(AU)

Objetivo: verificar el grado de satisfacción de los enfermeros e identificar los factores que causan insatisfacción laboral dentro del servicio hospitalario. Métodos: estudio exploratorio de métodos mixtos, realizado en un hospital particular de una capital del sur de Brasil. La recogida de datos tuvo lugar entre agosto y septiembre de 2017 con enfermeros asistenciales. Primero se aplicó una escala de satisfacción laboral dividida en cinco dimensiones: satisfacción con los colegas, con el sueldo, con el jefe, con el tipo de trabajo y con las promociones. Después, se invitaron a aquellos enfermeros cuyo resultado en la escala había dado "insatisfecho" a que respondieran una entrevista semiestructurada. Los datos cualitativos de las entrevistas se analizaron de acuerdo con el análisis de contenido temáticocategórico, respaldado por el software MAXQDA© para la organización de datos. Resultados: en la investigación participaron 40 enfermeros, 10 de ellos expresaron insatisfacción con la remuneración inadecuada; con la falta de promoción y crecimiento profesional; con el régimen laboral; con la falta de reconocimiento y valoración profesional; con la desviación de la función; con la competitividad y la desunión entre los profesionales y el equipo multiprofesional y con la falta de liderazgo. Sin embargo, trabajar en lo que les gusta y las amistades que se crean con los colegas en el trabajo eran factores de satisfacción para los enfermeros insatisfechos. Conclusión: el reconocimiento de los factores de insatisfacción de los enfermeros puede respaldar la implementación de un plan institucional para cambiar la situación identificada. Los trabajadores satisfechos están más motivados y producen mejor.(AU)