Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Smallholder maize yield estimation using satellite data and machine learning in Ethiopia

Zhe Guo Jordan Chamberlin Liangzhi You (2023, [Artículo])

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Sentinel-2 Smallholder Agriculture Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING

Establishment of heterotic groups for hybrid wheat breeding

Yunbi Xu (2022, [Artículo])

Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE CROPS FORECASTING PLANTS COMBINING ABILITY HETEROSIS HETEROTIC GROUPS MALE INFERTILITY PLANT HEIGHT WHEAT

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Xu Wang Sandesh Kumar Shrestha Philomin Juliana Suchismita Mondal Francisco Pinto Govindan Velu Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Ravi Singh Jesse Poland (2023, [Artículo])

New Crop Varieties Plant Breeding Programs Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEARNING GRAIN YIELDS WHEAT BREEDING FOOD SECURITY

Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])

Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES

Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat

Jose Crossa Osval Antonio Montesinos-Lopez Morten Lillemo (2024, [Artículo])

Multispectral Imaging Grain Yield Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN YIELDS HIGH-THROUGHPUT PHENOTYPING SPRING WHEAT

A novel method for genomic-enabled prediction of cultivars in new environments

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023, [Artículo])

Genomic Best Linear Unbiased Prediction Gains in Accuracy Genomic Prediction Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOTYPE ENVIRONMENT INTERACTION METHODS ENVIRONMENT

Multimodal deep learning methods enhance genomic prediction of wheat breeding

Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])

Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION

Enviromic prediction enables the characterization and mapping of Eucalyptus globulus Labill breeding zones

Germano Costa Neto Jose Crossa (2024, [Artículo])

Forest Tree Breeding Genomic Relationship Matrix Genomic Selection Best Linear Unbiased Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOREST TREES BREEDING MARKER-ASSISTED SELECTION MYRTACEAE EUCALYPTUS GLOBULUS

Morphological Alterations in Human Skeletal Muscle Cells Exposed to di-(2-Ethylhexyl) Phthalate (dehp) in Primary Cell Culture Conditions

Alteraciones Morfológicas en Células Musculares Esqueléticas Humanas Expuestas a Ftalato de di-(2-etilhexilo) (dehp) en Condiciones de Cultivo Celular Primario

Pablo Hernández Almaraz ORLANDO LUGO LUGO Ramón Gaxiola Robles OSCAR KURT BITZER QUINTERO Luis Javier Ramírez Jirano Elizabeth Brassea Pérez TANIA ZENTENO SAVIN (2022, [Artículo])

"Di-(2-ethylhexyl) phthalate (DEHP) is among the most common plasticizer additives that humans are in contact with daily. DEHP can be released from plastic and enter the human body, whereby it is metabolized and transformed into oxidative hydrophilic molecules. Clinical follow-ups in patients exposed to this phthalate and investigations in cultures of several cell types have provided information on its effects. For example, it is associated with inhibition of diploid human cell development and morphological changes in cultured germ cells. Although skeletal muscle represents around 50 % of the human body mass, knowledge about the effects of DEHP on this tissue is poor. Cultured skeletal muscle cells were exposed to DEHP (1 mM) for 13 days with the aim of exploring and evaluating some of the potential morphological effects. Three culture development parameters and nine cell characteristics were monitored during the bioassay. At 13 days, growth area, cell viability, and concentration of total proteins were lower in DEHP exposed than in control cells. Cell width and area, as well as the diameter of the nucleus and nucleolus, were greater in exposed cells than in control cells. These are interpreted as signs of cytotoxicity and suggest potential adverse effects on the development of skeletal muscle cells from DEHP exposure, as reported for other cell types."

Emerging pollutants, Morphometry, Phthalates, Plasticizers, Toxicity MEDICINA Y CIENCIAS DE LA SALUD CIENCIAS MÉDICAS BIOLOGÍA HUMANA FISIOLOGÍA DEL MÚSCULO FISIOLOGÍA DEL MÚSCULO