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.

RootBot: High-throughput root stress phenotyping robot.

Premise: Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.

Yield prediction through integration of genetic, environment, and management data through deep learning.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models.

Timing-Dependent Potentiation and Depression of Electrical Synapses Contribute to Network Stability in the Crustacean Cardiac Ganglion.

Central pattern generators produce many rhythms necessary for survival (e.g., chewing, breathing, locomotion), and doing so often requires coordination of neurons through electrical synapses.

Molecular profiling of single neurons of known identity in two ganglia from the crab .

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling-RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia.

Studying gap junctions with PARIS.

A new genetically encoded system manipulates the pH inside cells to detect whether they are coupled to each other.

Dopamine maintains network synchrony via direct modulation of gap junctions in the crustacean cardiac ganglion.

The Large Cell (LC) motor neurons of the crab cardiac ganglion have variable membrane conductance magnitudes even within the same individual, yet produce identical synchronized activity in the intact network. In a previous study we blocked a subset of K conductances across LCs, resulting in loss of synchronous activity (Lane et al., 2016).

Variability in neural networks.

Experiments on neurons in the heart system of the leech reveal why rhythmic behaviors differ between individuals.

The Hillary Climber trumps manual testing: an automatic system for studying Drosophila climbing.

Climbing or negative geotaxis is an innate behavior of the fruit fly Drosophila melanogaster. There has been considerable interest in using this simple behavior to gain insights into the changes in brain function associated with aging, influence of drugs, mutated genes, and human neurological disorders. At present, most climbing tests are conducted manually and there is a lack of a simple and automatic device for repeatable and quantitative analysis of fly climbing behavior.