Satellite-enabled enviromics to enhance crop improvement

Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

Optimising validity and translational potential in rodent models of brain disorders

Neurological and psychiatric disorders collectively constitute the greatest burden of disease. However, the human brain is the most complex of biological systems and therefore accurately modeling brain disorders presents enormous challenges. A large range of therapeutic approaches across a diverse collection of brain disorders have been found to show great promise in preclinical testing and then failed during clinical trials. There are a variety of potential reasons for such failures, on both the preclinical and clinical sides of the equation. In this article, I wi l l focus on the key issues of validity in animal models. I wi l l discuss two forms of construct validity,‘genetic construct validity’ and ‘environmental construct val idity’,which model specific aspects of the genome and ‘envirome’ relevant to the disorder in question. The generation of new gene-edited animal models has been facilitated by new technologies, the most notable of which are CRISPR-Cas systems. These and other technologies can be used to enhance contruct validity. Finally, I wi l l discuss how face validity can be optimized, via more sophisticated cognitive, affective and motor behavioural tests, translational tools and the integration of molecular, cellular and systems data. Predictive validity cannot yet be assessed for the many preclinical models where we currently lack effective clinical interventions, however this wi l l change as the translational pipeline is honed to deliver therapies for a range of devastating disorders.

GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield

The expression of quantitative traits of a line of a crop depends on its genetics,the environment where it is sown and the interaction between the genetic information and the environment known as GxE.Thus to maximize food production,new varieties are developed by selecting superior lines of seeds suitable for a specific environment.Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop.A large number of statistical and machine learning models are employed for single environment trials,where it is assumed that the environment does not have any effect on the quantitative traits.However,it is essential to consider both genomic and environmental data to develop a new variety,as these strong assumptions may lead to failing to select top lines for an environment.Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment.In the process,we also developed a new technique for identifying environmentspecific markers that can be useful in many applications of environment-specific genomic selection.The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario.Furthermore,the feature importance analysis shows that environmental information,followed by genomic information,is the driving factor in predicting environment-specific yield for a line.We also demonstrate a way to extend our framework for new data types,such as text or soil data.The extended model also shows the potential to be useful in genomic selection.