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Abstract 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, 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. 

Abstract Background and aimsHakea prostrata(Proteaceae) is a highly phosphorus-use-efficient plant native to southwest Australia. It maintains a high photosynthetic rate at low leaf phosphorus (P) and exhibits delayed leaf greening, a convergent adaptation that increases nutrient-use efficiency. This study aimed to provide broad physiological and gene expression profiles across leaf development, uncovering pathways leading from young leaves as nutrient sinks to mature leaves as low-nutrient, energy-transducing sources. MethodsTo explore gene expression underlying delayed greening, we analysed a de novo transcriptome forH. prostrataacross five stages of leaf development. Photosynthesis and respiration rates, and foliar pigment, P and nitrogen (N) concentrations were determined, including the division of P into five biochemical fractions. Key resultsTranscripts encoding functions associated with leaf structure generally decreased in abundance across leaf development, concomitant with decreases in foliar concentrations of 85% for anthocyanins, 90% for P and 70% for N. The expression of genes associated with photosynthetic function increased during or after leaf expansion, in parallel with increases in photosynthetic pigments and activity, much later in leaf development than in species that do not have delayed greening. As leaves developed, transcript abundance for cytosolic and mitochondrial ribosomal proteins generally declined, whilst transcripts for chloroplast ribosomal proteins increased. ConclusionsThere was a much longer temporal separation of leaf cell growth from chloroplast development inH. prostratathan is found in species that lack delayed greening. Transcriptome-guided analysis of leaf development inH. prostrataprovided insight into delayed greening as a nutrient-saving strategy in severely phosphorus-impoverished landscapes. 

The standard of care treatment for neovascular age-related macular degeneration, delivered as ocular injection, is based on anti-vascular endothelial growth factor (anti-VEGF). The course of treatment may need to be modified quickly for certain patients based on their response. Models that track both the concentration and the response to an anti-VEGF treatment are presented. The specific focus is to assess the existence of analytical solutions for the different types of models. Both an ODE-based model and a map-based model illustrate the dependence of the solution on various biological parameters and allow the measurement of patient-specific parameters from experimental data. A PDE-based model incorporates diffusive effects. The results are consistent with observed values, and could provide a framework for practitioners to understand the effect of the therapy on the progression of the disease in both responsive and non-responsive patients. 

Abstract Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample‐dependent electrostatic and chemo‐electromechanical changes. Literature reports have often concentrated on the evaluation of theOff‐fieldpiezoresponse, compared toOn‐fieldpiezoresponse, based on the latter's increased sensitivity to non‐ferroelectric contributions. Using machine learning approaches incorporatingboth Off‐andOn‐fieldpiezoresponse response as well asOff‐fieldresonance frequency to maximize information, switching piezoresponse in a defect‐rich Pb(Zr,Ti)O₃thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena duringOn‐fieldmeasurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non‐ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo‐electromechanical response.