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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 initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including 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 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 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., Competing Interests: Conflicts of interest: The authors declare no conflicts of interest., (Published by Oxford University Press on behalf of The Genetics Society of America 2024.)

Our own experience of emotional events influences how we approach and react to others' emotions. Here we observe that mice exhibit divergent interindividual responses to others in stress (that is, preference or avoidance) only if they have previously experienced the same aversive event. These responses are estrus dependent in females and dominance dependent in males. Notably, silencing the expression of the corticotropin-releasing factor (CRF) within the medial prefrontal cortex (mPFC) attenuates the impact of stress self-experience on the reaction to others' stress. In vivo microendoscopic calcium imaging revealed that mPFC CRF neurons are activated more toward others' stress only following the same negative self-experience. Optogenetic manipulations confirmed that higher activation of mPFC CRF neurons is responsible for the switch from preference to avoidance of others in stress, but only following stress self-experience. These results provide a neurobiological substrate underlying how an individual's emotional experience influences their approach toward others in a negative emotional state., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: The pathogenesis of congenital diaphragmatic hernia (CDH) depends on multiple factors. Activation of the DNA-sensing cyclic-GMP-AMP-synthase (cGAS) and Stimulator-of-Interferon-Genes (STING) pathway by double-stranded DNA (dsDNA) links environmental stimuli and inflammation. We hypothesized that nitrofen exposure alters cGAS and STING in human bronchial epithelial cells and fetal rat lungs., Methods: We used the Quant-IT™-PicoGreen™ assay to assess dsDNA concentration in BEAS-2B cells after 24 h of nitrofen-exposure and performed immunofluorescence of cGAS/STING. We used nitrofen to induce CDH and harvested control and CDH lungs at embryonic day E15, E18 and E21 for cGAS/STING immunofluorescence, RT-qPCR and RNA-Scope™ in-situ-hybridization (E18, E21)., Results: We found a higher concentration of dsDNA following nitrofen treatment. Nitrofen-exposure to BEAS-2B cells increased cGAS and STING protein abundance. cGAS abundance was higher in nitrofen lungs at E15, E18 and E21. RNA-Scope in-situ-hybridization showed higher cGAS and STING expression in E18 and E21 lungs. RT-qPCR revealed higher mRNA expression levels of STING in E21 nitrofen-induced lungs., Conclusion: Our data suggest that nitrofen-exposure increases dsDNA content which leads to stimulation of the cGAS/STING pathway in human BEAS-2B cells and the nitrofen rat model of CDH. Consequently, DNA sensing and the cGAS-STING-pathway potentially contribute to abnormal lung development in CDH., Impact Statement: We found an alteration of DNA sensing targets cGAS and STING in human BEAS-2B cells and experimental congenital diaphragmatic hernia with higher protein abundance and mRNA expression in cells and lung sections of nitrofen-treated rat pups. This is the first study to investigate DNA sensing, a potential link between environmental stimuli and inflammation, in experimental CDH. Our study extends the knowledge on the pathogenesis of experimental CDH., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

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., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

Crop genetic diversity for climate adaptations is globally partitioned. We performed experimental evolution in maize to understand the response to selection and how plant germplasm can be moved across geographical zones. Initialized with a common population of tropical origin, artificial selection on flowering time was performed for two generations at eight field sites spanning 25° latitude, a 2800 km transect. We then jointly tested all selection lineages across the original sites of selection, for the target trait and 23 other traits. Modeling intergenerational shifts in a physiological reaction norm revealed separate components for flowering-time plasticity. Generalized and local modes of selection altered the plasticity of each lineage, leading to a latitudinal pattern in the responses to selection that were strongly driven by photoperiod. This transformation led to widespread changes in developmental, architectural, and yield traits, expressed collectively in an environment-dependent manner. Furthermore, selection for flowering time alone alleviated a maladaptive syndrome and improved yields for tropical maize in the temperate zone. Our findings show how phenotypic selection can rapidly shift the flowering phenology and plasticity of maize. They also demonstrate that selecting crops to local conditions can accelerate adaptation to climate change., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The chitin synthase Chs3 is a multipass membrane protein whose trafficking is tightly controlled. Accordingly, its exit from the endoplasmic reticulum (ER) depends on several complementary mechanisms that ensure its correct folding. Despite its potential failure on its exit, Chs3 is very stable in this compartment, which suggests its poor recognition by ER quality control mechanisms such as endoplasmic reticulum-associated degradation (ERAD). Here we show that proper N-glycosylation of its luminal domain is essential to prevent the aggregation of the protein and its subsequent recognition by the Hrd1-dependent ERAD-L machinery. In addition, the interaction of Chs3 with its chaperone Chs7 seems to mask additional cytosolic degrons, thereby avoiding their recognition by the ERAD-C pathway. On top of that, Chs3 molecules that are not degraded by conventional ERAD can move along the ER membrane to reach the inner nuclear membrane, where they are degraded by the inner nuclear membrane-associated degradation (INMAD) system, which contributes to the intracellular homeostasis of Chs3. These results indicate that Chs3 is an excellent model to study quality control mechanisms in the cell and reinforce its role as a paradigm in intracellular trafficking research.

Source and sink interactions play a critical but mechanistically poorly understood role in the regulation of senescence. To disentangle the genetic and molecular mechanisms underlying source-sink-regulated senescence (SSRS), we performed a phenotypic, transcriptomic, and systems genetics analysis of senescence induced by the lack of a strong sink in maize (Zea mays). Comparative analysis of genotypes with contrasting SSRS phenotypes revealed that feedback inhibition of photosynthesis, a surge in reactive oxygen species, and the resulting endoplasmic reticulum (ER) stress were the earliest outcomes of weakened sink demand. Multienvironmental evaluation of a biparental population and a diversity panel identified 12 quantitative trait loci and 24 candidate genes, respectively, underlying SSRS. Combining the natural diversity and coexpression networks analyses identified 7 high-confidence candidate genes involved in proteolysis, photosynthesis, stress response, and protein folding. The role of a cathepsin B like protease 4 (ccp4), a candidate gene supported by systems genetic analysis, was validated by analysis of natural alleles in maize and heterologous analyses in Arabidopsis (Arabidopsis thaliana). Analysis of natural alleles suggested that a 700-bp polymorphic promoter region harboring multiple ABA-responsive elements is responsible for differential transcriptional regulation of ccp4 by ABA and the resulting variation in SSRS phenotype. We propose a model for SSRS wherein feedback inhibition of photosynthesis, ABA signaling, and oxidative stress converge to induce ER stress manifested as programed cell death and senescence. These findings provide a deeper understanding of signals emerging from loss of sink strength and offer opportunities to modify these signals to alter senescence program and enhance crop productivity., Competing Interests: Conflict of interest statement. The authors declare no conflict of interest., (© American Society of Plant Biologists 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© 2023. The Author(s).)

The stiff-stalk heterotic group in Maize (Zea mays L.) is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and, to a lesser extent, B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four expired Plant Variety Protection (ex-PVP) lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40, which is a Pioneer Hi-Bred International (PHI) early stiff-stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13-2.18 Gbp with N50 scaffold lengths >200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas, whereas transposable element (TE) annotation was conducted using de novo and homology-directed methodologies. Compared with the reference inbred B73, synteny analyses revealed extensive collinearity across the five stiff-stalk genomes, although unique components of the maize pangenome were detected. Comparison of this set of stiff-stalk inbreds with the original Iowa Stiff Stalk Synthetic breeding population revealed that these inbreds represent only a proportion of variation in the original stiff-stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original stiff-stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent stiff-stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pangenome., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (-0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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., (© 2023. The Author(s).)

Variation for maize (Zea mays L.) kernel vitreousness is of interest in the improvement of maize for human and ruminant nutrition based on its connection with starch degradability in the rumen as well as the concentration of lysine and other essential amino acids. In this trial, 199 recombinant inbred lines (RILs) from the intermated B73 and Mo17 (IBM) population and 133 testcrosses of the IBM RIL with inbred W604S were grown in two replications in two Wisconsin locations in 2007. Ears were harvested at physiological maturity and ground kernel samples were scanned using near-infrared spectroscopy (NIRS). Samples representing the NIRS spectral range were scored for kernel vitreousness using a horizontal light box and for kernel hardness using the Stenvert hardness test. The RIL phenotypes for hardness and vitreousness followed a normal distribution, and transgressive segregation was seen for both traits. Correlations between inbreds and respective testcrosses for hardness and vitreousness were significant and positive. The correlation between hardness and vitreousness was also positive. Quantitative trait loci (QTL) analysis of this population found 33 QTL for hardness and vitreousness with several QTL overlapping across traits. The IBM population represents a good model for the study of variation of kernel vitreousness in U.S. Corn Belt Dent germplasm and the genomic regions identified could be useful for the genetic improvement of maize varieties with enhanced nutritional composition. doi: 10.2135/cropsci2009.12.0726

Wisconsin Quality Synthetic (WQS) is a maize population that has undergone four cycles of [S.sub.2]-topcross selection for increased silage yield and feed quality. This study evaluated forage yield and quality for each cycle of WQS per se as well as testcrosses to two Stiff Stalk-type testers. Linear improvement was seen in whole-plant yield, stover yield, and whole-plant quality both in the population per se and in testcrosses. Stover quality has not improved through selection. Starch content has increased while crude protein has decreased. Milk yields on a Mg dry matter and hectare basis have increased with selection. Changes in silage dry matter yield have been greater on a percent basis than changes in silage quality, suggesting that the current selection protocol of selecting [S.sub.2]-top-crosses first for yield then for quality may be more efficient at improving yield than quality. Selection directly focused on stover quality may be necessary if more rapid improvement in stover composition is desired. doi: 10.2135/cropsci20009.12.0725

Maize hybrids with improved stover yield and quality are needed to support the emerging cellulosic biofuels industry. Knowledge of the genetic variation, covariation, and genetic architecture of agronomic and cell wall traits will help maize breeders improve relevant traits through selective breeding and genetic engineering. We utilized the maize intermated B73 x Mo17 (IBM) recombinant inbred line (RIL) population to map quantitative trait loci (QTL) and to evaluate the genetic relationships among traits relevant for cellulosic biofuel production, including grain yield, stover yield, neutral detergent fiber digestibility (NDFD), and glucan, xylan, anal lignin concentration. Intermated B73 x Mo17 RIL were evaluated per se and as testcrosses to W604S to assess how well performance and QTL effects correspond between inbreds and hybrids. There were no unfavorable correlations between agronomic and cell wall traits within the testcross progeny. Polysaccharide content was either positively correlated to, or not correlated to, NDFD, indicating that increasing both carbohydrate content and convertibility is feasible. Quantitative trait loci were identified for all traits evaluated, but few QTL were in common between inbred per se and testcross evaluations despite moderate to high genotypic correlations between progeny types. This study provides information that will support the development of dual-purpose maize hybrids that are high in grain yield and also high in yield of digestible cellulosic feedstock. doi: 10.2135/cropsci2009.04.0168

The use of maize (Zea mays L.) stover as a feedstock for cellulosic biofuels production will create demand for maize hybrids with greater stover yield. It is expected that grain yield will remain the most critical trait and continue to drive hybrid sales, requiring that any increases in stover yield be made without sacrificing grain yield potential. The objective of this review was to determine the relationship between grain yield, harvest index, and stover yield to illuminate the potential for increasing both grain and stover yield through breeding. In contrast to what has been observed in other crops, gains in maize grain yield over time in the U.S. Corn Belt have been accompanied by increases in stover yield. Where recurrent selection on grain yield has been most successful, stover yield also increased while harvest index has been relatively stable. The opposite situation has been observed in tropical germplasm, where gains in grain yield have been associated with increasing harvest index and relatively constant biomass yield. We expect that stover yield of Corn Belt hybrids will continue to increase along with grain yield, resulting in future hybrids capable of producing both more food and biofeedstock for energy production. If maize breeders pursue selection for increased stover yield, we found no evidence to suggest that breeding for stover yield will necessarily reduce rate of gain in grain yield. doi: 10.2135/cropsci2009.02.0086

Key Message: Significant introgression-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome. Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to introgression-by-environment interactions. To study potential patterns of introgression-by-environment interactions, fifteen near-isogenic lines (NILs) with > 90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess introgression-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the introgression-by-environment interaction varies across traits. Some introgressed segments were more prone to introgression-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of introgression-by-environment interaction in NILs is useful in consideration of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.

Maize (Zea mays L.) stover has been identified as an important feedstock for the production of cellulosic ethanol. Our objectives were to measure hybrid effect and combining ability patterns of traits related to cellulosic ethanol production, determine if germplasm and mutations used for silage production would also be beneficial for feedstock production, and examine relationships between traits that are relevant to selective breeding. We evaluated grain hybrids, germplasm bred for silage production, brown-midrib hybrids, and a leafy hybrid. Yield and composition traits were measured in four environments. There was a 53% difference in stover yield between commercial grain hybrids that were equivalent for other production-related traits. Silage germplasm may be useful for increasing stover yield and reducing lignin concentration. We found much more variation among hybrids than either in vitro ruminal fermentability or polysaccharide concentration. Correlations between traits were mostly favorable or nonexistent. Our results suggest that utilizing standing genetic variation of maize in breeding programs could substantially increase the amount of biofuels produced from stover per unit area of land.

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Key Message: A novel locus was discovered on chromosome 7 associated with a lesion mimic in maize; this lesion mimic had a quantitative and heritable phenotype and was predicted better via subset genomic markers than whole genome markers across diverse environments. Lesion mimics are a phenotype of leaf micro-spotting in maize (Zea mays L.), which can be early signs of biotic or abiotic stresses. Dissecting its inheritance is helpful to understand how these loci behave across different genetic backgrounds. Here, 538 maize recombinant inbred lines (RILs) segregating for a novel lesion mimic were quantitatively phenotyped in Georgia, Texas, and Wisconsin. These RILs were derived from three bi-parental crosses using a tropical pollinator (Tx773) as the common parent crossed with three inbreds (LH195, LH82, and PB80). While this lesion mimic was heritable across three environments based on phenotypic ([Formula: see text] = 0.68) and genomic ([Formula: see text] = 0.91) data, transgressive segregation was observed. A genome-wide association study identified a single novel locus on chromosome 7 (at 70.6 Mb) also covered by a quantitative trait locus interval (69.3-71.0 Mb), explaining 11-15% of the variation, depending on the environment. One candidate gene identified in this region, Zm00001eb308070, is related to the abscisic acid pathway involving in cell death. Genomic predictions were applied to genome-wide markers (39,611 markers) contrasted with a marker subset (51 markers). Population structure explained more variation than environment in genomic prediction, but other substantial genetic background effects were additionally detected. Subset markers explained substantially less genetic variation (24.9%) for the lesion mimic than whole genome markers (55.4%) in the model, yet predicted the lesion mimic better (0.56-0.66 vs. 0.26-0.29). These results indicate this lesion mimic phenotype was less affected by environment than by epistasis and genetic background effects, which explain its transgressive segregation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2023. The Author(s).)

Background: The initial management of acute aortic dissection centers around rapid control of blood pressure and heart rate, commonly requiring initiation of continuous intravenous (IV) antihypertensive agents and intensive care unit (ICU) admission. However, there is limited guidance for when and how to transition off IV infusions to enteral agents, potentially extending ICU length of stay (LOS) in stable patients who are otherwise ready for floor transfer. The objective of this study is to compare the impact of rapid vs. slow transition from IV to enteral vasoactive medications on ICU LOS., Methods: In this retrospective cohort study of 56 adult patients admitted with aortic dissection requiring IV vasoactive infusions for >6 hours, patients were grouped by time required to fully transition from IV to enteral vasoactive agents. Patients who transitioned in ≤72 hours were considered the "rapid" group, and the "slow" group required >72 hours to fully convert. The primary endpoint was ICU LOS., Results: For the primary endpoint, the median ICU LOS was 3.6 days for the "rapid" group, compared to 7.7 days in the "slow" group (P<0.001). The "slow" group required a significantly longer duration of IV vasoactive infusions (115.7 vs. 36.0 hours, P<0.001) and also trended towards longer median hospital LOS. The two cohorts had similar incidences of hypotension., Conclusions: In this study, rapid transition to enteral antihypertensives within 72 hours was associated with a shorter ICU LOS without an increase in hypotension., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-22-1274/coif). The authors have no conflicts of interest to declare., (2023 Journal of Thoracic Disease. All rights reserved.)

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. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors., Competing Interests: Conflicts of interest None declared., (Published by Oxford University Press on behalf of the Genetics Society of America 2023.)

The open-pollinated maize (Zea mays subsp. mays) population Golden Glow [GG(MP)] provides excellent material for the study of morphological and genetic changes associated with selection for prolificacy. Late generations of the GG(MP), when planted at low densities, resemble the architecture of teosinte (Z. mays snbsp, parviglumis), the ancestor of maize. Our objectives were (i) to identify molecular markers linked to chromosomal regions that influenced prolificacy and related morphological traits, (ii) to determine whether genes previously shown to influence branching patterns in maize were associated with quantitative trait loci (QTLs) influencing these traits, and (iii) to determine whether epistatic interactions among putative QTLs influenced prolificacy and correlated traits. A mapping population was developed from the cross of inbred A679 and a highly prolific S- plant from Cycle 23 of GG(MP). Simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers were combined for the linkage map construction. Thirty-three QTLs were found for 14 of the 16 traits analyzed. A region located on Linkage Group 1 (LG 1) shared similarities with the pattern of development suggested for the teosinte branched1 (tb1) mutant. This suggests that genetic control of prolificacy and associated traits in GG(MP) may resemble the genetic changes underlying branching morphology that occurred during maize domestication. No evidence for additive-by-additive epistatic interactions was found. Abbreviations: AFLP, amplified fragment length polymorphism; CIM, composite interval mapping; cM, centimorgan; LG, linkage group; PCR. polymerase chain reaction; QTL, quantitative trait locus; SSR, simple sequence repeat; tb1, teosinte branched1., THE GENETIC BASIS underlying morphological change is the focus on much current research and it is essential to understanding evolutionary biology. It has been suggested that plant populations can undergo [...]

Prolificacy (the potential to produce ear shoots at multiple nodes on the main stalk) has been under selection since the early domestication of maize (Zea mays subsp, mays) from teosinte (Z. mays subsp. parviglumis). Until the early part of the 20th century, before mechanization, human selection may have favored single-eared maize because it facilitated hand-harvesting. Prolificacy, however, has the potential to increase stress tolerance under intensive management. Our objectives were (i) to assess variation for prolificacy and 15 related morphological traits in a population of 194 [F.sub.3] families derived from inbred line A679 and a highly prolific [S.sub.1] plant from the cultivar Golden Glow after 23 cycles of mass selection for prolificacy; and (ii) to determine relationships among traits and to infer which ones appear to be controlled by similar genetic factors. Traits were evaluated with two replications in three field environments. The population varied significantly for all traits and most traits had relatively high heritabilities (>0.80). Some traits were highly correlated, and two main groups were identified. These groups involved traits mostly associated with either the activity of axillary meristems, or intercalary meristems. In general, the correlations of trails across these two groups were insignificant or of lesser magnitude than within groups, suggesting that common genetic factors might be influencing some of these morphological traits. Abbreviations: BLUP, best linear unbiased prediction; REML, restricted maximum likelihood., MAIZE PLANTS initiate axillary meristems on most nodes of the main stalk, but many potential ear shoots abort during plant development, resulting in one or two grain-filled ears in most [...]

Maize (Zea mays L.) has the potential to produce an ear primordium at several nodes, and selection for increased prolificacy often increases grain yield. We have previously demonstrated the effectiveness of mass selection for increasing the number of active ear shoots in the `Golden Glow' maize population. The objective of our research was to evaluate associated changes in ear and plant morphology after 24 cycles of mass selection for prolificacy in Golden Glow. Cycles 0, 6, 12, 18, 23, and 24 were grown in 1997 at two locations (Madison and Arlington, WI) at two planting densities (15 070 and 73 197 plants [ha.sup.-1]). At the lower planting density, the mean number of ears per plant increased from 1.61 at C0 to 4.93 at C24, and the response rate increased over the more recent cycles, mainly from C18 to C24. At C24, tiller number has increased, and lateral branches (terminating in ears) were located at approximately one node higher and two nodes lower on the main stalk compared with the original population. Total node number on the main stalk increased, and the internode length decreased over the 24 cycles, but the opposite occurred for lateral branches. At the lower planting density, ear diameter, ear length, and kernel size decreased by 0.03, 0.10, and 0.01 cm [cycle.sup.-1] respectively, but total number of kernels per plant and dry kernel weight per plant increased significantly at a rate of 36.69 kernels [cycle.sup.-1] and 5.60 g [cycle.sup.-1] over the 24 cycles. Similar trends, but of lesser magnitude, were observed at the higher planting density. The changes in plant morphology observed across cycles of selection for prolificacy represent a general derepression of axillary meristematic growth in both main stalks and lateral branches., MAIZE HAS THE POTENTIAL to produce an ear primordium at several nodes, even though most modern hybrids have only one or two ears, at least under the conditions in which [...]

Prenatal and postnatal treatment modalities for congenital diaphragmatic hernia (CDH) continue to improve, however patients still face high rates of morbidity and mortality caused by severe underlying persistent pulmonary hypertension and pulmonary hypoplasia. Though the majority of CDH cases are idiopathic, it is believed that CDH is a polygenic developmental defect caused by interactions between candidate genes, as well as environmental and epigenetic factors. However, the origin and pathogenesis of these developmental insults are poorly understood. Further, connections between disrupted lung development and the failure of diaphragmatic closure during embryogenesis have not been fully elucidated. Though several animal models have been useful in identifying candidate genes and disrupted signalling pathways, more studies are required to understand the pathogenesis and to develop effective preventative care. In this article, we summarize the most recent litterature on disrupted embryological lung and diaphragmatic development associated with CDH., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)