Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields ...(G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017.
Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files 1 while 2016 and 2017 datasets are newly available to the public.
This open access book focuses on the public health crisis of youth suicide and provides a review of current research and prevention practices. It addresses important topics, including suicide ...epidemiology, suicide risk detection in school and medical settings, critical cultural considerations, and approaches to lethal means safety. This book offers cutting-edge research on emerging discoveries in the neurobiology of suicide, psychopharmacology, and machine learning. It focuses on upstream suicide prevention research methods and details how cost-effective approaches can mitigate youth suicide risk when implemented at a universal level. Chapters discuss critical areas for future research, including how to evaluate the effectiveness of suicide prevention and intervention efforts, increase access to mental health care, and overcome systemic barriers that undermine generalizability of prevention strategies. Finally, this book highlights what is currently working well in youth suicide prevention and, just as important, which areas require more attention and support. Key topics include: The neurobiology of suicide in at-risk children and adolescents. The role of machine learning in youth suicide prevention. Suicide prevention, intervention, and postvention in schools. Suicide risk screening and assessment in medical settings. Culturally informed risk assessment and suicide prevention efforts with minority youth. School mental health partnerships and telehealth models of care in rural communities. Suicide and self-harm prevention and interventions for LGBTQ+ youth. Risk factors associated with suicidal behavior in Black youth. Preventing suicide in youth with autism spectrum disorder (ASD) and intellectual disability (ID). Youth Suicide Prevention and Intervention is a must-have resource for policy makers and related professionals, graduate students, and researchers in child and school psychology, family studies, public health, social work, law/criminal justice, sociology, and all related disciplines.
Understanding allostery may serve to both elucidate mechanisms of protein regulation and provide a basis for engineering active mutants. Herein we describe directed evolution applied to the ...integrin$\alpha_{L}$inserted domain for studying allostery by using a yeast surface display system. Many hot spots for activation are identified, and some single mutants exhibit remarkable increases of 10,000-fold in affinity for a physiological ligand, intercellular adhesion molecule-1. The location of activating mutations traces out an allosteric interface in the interior of the inserted domain that connects the ligand binding site to the α7-helix, which communicates allostery to neighboring domains in intact integrins. The combination of two activating mutations (F265S/F292G) leads to an increase of 200,000-fold in affinity to intercellular adhesion molecule-1. The F265S/F292G mutant is potent in antagonizing lymphocyte function-associated antigen 1-dependent lymphocyte adhesion, aggregation, and transmigration.
To sustain plant growth, development, and crop yield, sucrose must be transported from leaves to distant parts of the plant, such as seeds and roots. To identify genes that regulate sucrose ...accumulation and transport in maize (Zea mays), we isolated carbohydrate partitioning defective33 (cpd33), a recessive mutant that accumulated excess starch and soluble sugars in mature leaves. The cpd33 mutants also exhibited chlorosis in the leaf blades, greatly diminished plant growth, and reduced fertility. Cpd33 encodes a protein containing multiple C2 domains and transmembrane regions. Subcellular localization experiments showed the CPD33 protein localized to plasmodesmata (PD), the plasma membrane, and the endoplasmic reticulum. We also found that a loss-of-function mutant of the CPD33 homolog in Arabidopsis, QUIRKY, had a similar carbohydrate hyperaccumulation phenotype. Radioactively labeled sucrose transport assays showed that sucrose export was significantly lower in cpd33 mutant leaves relative to wild-type leaves. However, PD transport in the adaxial-abaxial direction was unaffected in cpd33 mutant leaves. Intriguingly, transmission electron microscopy revealed fewer PD at the companion cell–sieve element interface in mutant phloem tissue, providing a possible explanation for the reduced sucrose export in mutant leaves. Collectively, our results suggest that CPD33 functions to promote symplastic transport into sieve elements.
Maize carbohydrate partitioning defective33 (cpd33) mutants exhibit carbohydrate hyperaccumulation in leaves, reduced sucrose export from leaves, and fewer plasmodesmata (PD) between companion cells–sieve elements in leaf minor veins. Cpd33 encodes an MCTP protein and localizes to the PD, the plasma membrane, and the endoplasmic reticulum. These data suggest Cpd33 functions to promote symplastic transport into sieve elements.
Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, ...dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available.
Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.
Living systems are fundamentally dynamic and adaptive, relying on a constant throughput of energy. They are also, by definition, self-sustaining over the full range of length and time scales (from ...sub-cellular structures to species considered as a whole). This characteristic combination of constant adaptive flux and emergent persistence requires that the properties of all living systems must, at some level, be cyclical. Consequently, oscillatory dynamics, in which system properties rise and fall in a regular rhythmic fashion, are a central feature of a wide range of biological processes. The scale of biological oscillations covers enormous ranges, from the sub-cellular to the population level, and from milliseconds to years. While the existence of a number of biological oscillations, such such as the regular beating of the human heart or the life-cycle of a unicellular organismis, is widely appreciated, there are many oscillatory phenomena that are much less obvious, albeit no less important. Since oscillations reflect periodic quantitative changes in system properties, their detection and characterisation relies on the quantitative measurement of a system over an extended period. Until recently, such measurements were difficult to obtain at cellular or sub-cellular resolution, and relatively few cellular oscillations had been described. However, recent methodological advances have revealed that oscillatory phenomena are as widespread in cells as they are at larger scales. The papers in this book provide an introduction to a range of both well known and less familiar cellular oscillations, and serve to illustrate the striking richness of cellular dynamics. The contributions focus particularly on elucidating the basic mechanisms that underlie these oscillations. The essentially quantitative nature of oscillations has long made them an attractive area of study for theoretical biologists and the application of complementary modelling and experimental approaches can yield insights into oscillatory dynamics that go beyond those that can be obtained by either in isolation. The benefits of this synergy are reflected in the contributions appearing in this book. That oscillations play central roles in phenomena at all levels of cellular organisation is illustrated by the range of examples detailed in this book. Chapters by Lloyd and by Aon and colleagues describe coherent oscillations in cellular metabolism, a process clearly common to all living cells. Similarly, the cell cycle, discussed by Csiksz-Nagy and colleagues, is a fundamentally cyclical process common to all cells. Rougemont and Naef describe models for circadian rhythms, which are critical in allowing organisms to entrain their cellular activities to imposed daily changes in their environment. The chapters by Lahav and by Momiji and Monk focus on recently-discovered oscillations in cellular response systems, in which the combined requirements of sensitive response and signal termination result in unexpected oscillatory instabilities. Oscillations contribute not only to temporal organisation within cells, but can also direct spatio-temporal organisation in multicellular tissues. Thul and colleagues review the central role played by oscillatory changes in calcium concentration in processes spanning these scales. A striking and well known example of oscillatory patterning at the multi-cellular level is the aggregation of developing cells of the slime mold Dictyostelium discoideum. Loomis discusses the critical role played by oscillatory cAMP signalling in this phenomenon. More recently discovered illustrations of the role of oscillations in spatial patterning are provided by the chapters of Lutkenhaus and Palmeirim and colleagues. Lutkenhaus describes the way in which many bacteria localise their cell division plane through oscillations of Min proteins. Palmeirim and colleagues review oscillatory mechanisms underlying the segmentation of vertebrate embryos. The current resurgence in interest in interdisciplinary approaches to cell and molecular biology (often referred to as Systems Biology) stems in part from the increasing availability of system-wide data on the state of the components of cellular regulatory networks. A limiting factor in these approaches is often the lack of suitable ways of characterising a network state in terms of summary quantitative features. Without such features, it is typically difficult to gain new qualitative insight into the operating logic of all but the simplest networks. In this regard, oscillatory phenomena provide ideal exemplars for systems approaches, since oscillations have clear summary features (such as period, amplitude and phase) that prove invaluable in combining mathematical models with experimental data.
Abstract
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate ...genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
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