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  • 1
    Online Resource
    Online Resource
    Protoplast fusion in Bacillus species produces frequent, unbiased, genome-wide homologous recombination
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. 11 ( 2022-06-24), p. 6211-6223
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. 11 ( 2022-06-24), p. 6211-6223
    Abstract: In eukaryotes, fine-scale maps of meiotic recombination events have greatly advanced our understanding of the factors that affect genomic variation patterns and evolution of traits. However, in bacteria that lack natural systems for sexual reproduction, unbiased characterization of recombination landscapes has remained challenging due to variable rates of genetic exchange and influence of natural selection. Here, to overcome these limitations and to gain a genome-wide view on recombination, we crossed Bacillus strains with different genetic distances using protoplast fusion. The offspring displayed complex inheritance patterns with one of the parents consistently contributing the major part of the chromosome backbone and multiple unselected fragments originating from the second parent. Our results demonstrate that this bias was in part due to the action of restriction–modification systems, whereas genome features like GC content and local nucleotide identity did not affect distribution of recombination events around the chromosome. Furthermore, we found that recombination occurred uniformly across the genome without concentration into hotspots. Notably, our results show that species-level genetic distance did not affect genome-wide recombination. This study provides a new insight into the dynamics of recombination in bacteria and a platform for studying recombination patterns in diverse bacterial species.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac025
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 2
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    Extending the Genotype in Brachypodium by Including DNA Methylation Reveals a Joint Contribution with Genetics on Adaptive Traits
    Oxford University Press (OUP) ; 2020
    In:  G3 Genes|Genomes|Genetics Vol. 10, No. 5 ( 2020-05-01), p. 1629-1637
    Details
    In: G3 Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 10, No. 5 ( 2020-05-01), p. 1629-1637
    Abstract: Epigenomic changes have been considered a potential missing link underlying phenotypic variation in quantitative traits but is potentially confounded with the underlying DNA sequence variation. Although the concept of epigenetic inheritance has been discussed in depth, there have been few studies attempting to directly dissect the amount of epigenomic variation within inbred natural populations while also accounting for genetic diversity. By using known genetic relationships between Brachypodium lines, multiple sets of nearly identical accession families were selected for phenotypic studies and DNA methylome profiling to investigate the dual role of (epi)genetics under simulated natural seasonal climate conditions. Despite reduced genetic diversity, appreciable phenotypic variation was still observable in the measured traits (height, leaf width and length, tiller count, flowering time, ear count) between as well as within the inbred accessions. However, with reduced genetic diversity there was diminished variation in DNA methylation within families. Mixed-effects linear modeling revealed large genetic differences between families and a minor contribution of DNA methylation variation on phenotypic variation in select traits. Taken together, this analysis suggests a limited but significant contribution of DNA methylation toward heritable phenotypic variation relative to genetic differences.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    URL: Article
    DOI: 10.1534/g3.120.401189
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2629978-1
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  • 3
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    Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Plant Phenomics Vol. 5 ( 2023-01)
    Details
    In: Plant Phenomics, American Association for the Advancement of Science (AAAS), Vol. 5 ( 2023-01)
    Abstract: Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.
    Type of Medium: Online Resource
    ISSN: 2643-6515
    URL: Article
    DOI: 10.34133/plantphenomics.0072
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
    detail.hit.zdb_id: 2968615-5
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  • 4
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    Global Diversity of the Brachypodium Species Complex as a Resource for Genome-Wide Association Studies Demonstrated for Agronomic Traits in Response to Climate
    Oxford University Press (OUP) ; 2019
    In:  Genetics Vol. 211, No. 1 ( 2019-01-01), p. 317-331
    Details
    In: Genetics, Oxford University Press (OUP), Vol. 211, No. 1 ( 2019-01-01), p. 317-331
    Abstract: The development of model systems requires a detailed assessment of standing genetic variation across natural populations. The Brachypodium species complex has been promoted as a plant model for grass genomics with translation to small grain and biomass crops. To capture the genetic diversity within this species complex, thousands of Brachypodium accessions from around the globe were collected and genotyped by sequencing. Overall, 1897 samples were classified into two diploid or allopolyploid species, and then further grouped into distinct inbred genotypes. A core set of diverse B. distachyon diploid lines was selected for whole genome sequencing and high resolution phenotyping. Genome-wide association studies across simulated seasonal environments was used to identify candidate genes and pathways tied to key life history and agronomic traits under current and future climatic conditions. A total of 8, 22, and 47 QTL were identified for flowering time, early vigor, and energy traits, respectively. The results highlight the genomic structure of the Brachypodium species complex, and the diploid lines provided a resource that allows complex trait dissection within this grass model species.
    Type of Medium: Online Resource
    ISSN: 1943-2631
    URL: Article
    DOI: 10.1534/genetics.118.301589
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1477228-0
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  • 5
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    Quinoa Phenotyping Methodologies: An International Consensus
    MDPI AG ; 2021
    In:  Plants Vol. 10, No. 9 ( 2021-08-24), p. 1759-
    Details
    In: Plants, MDPI AG, Vol. 10, No. 9 ( 2021-08-24), p. 1759-
    Abstract: Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
    Type of Medium: Online Resource
    ISSN: 2223-7747
    URL: Article
    DOI: 10.3390/plants10091759
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704341-1
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  • 6
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    Climatic Clustering and Longitudinal Analysis with Impacts on Food, Bioenergy, and Pandemics
    Scientific Societies ; 2023
    In:  Phytobiomes Journal Vol. 7, No. 1 ( 2023-06), p. 65-77
    Details
    In: Phytobiomes Journal, Scientific Societies, Vol. 7, No. 1 ( 2023-06), p. 65-77
    Abstract: Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on Earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel correlation of correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37 × 10 18 operations/s) and one of the largest 168.7 × 10 21 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.
    Type of Medium: Online Resource
    ISSN: 2471-2906
    URL: Article
    DOI: 10.1094/PBIOMES-02-22-0007-R
    Language: English
    Publisher: Scientific Societies
    Publication Date: 2023
    detail.hit.zdb_id: 2897163-2
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  • 7
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    Environmental resource deficit may drive the evolution of intraspecific trait variation in invasive plant populations
    Wiley ; 2019
    In:  Oikos Vol. 128, No. 2 ( 2019-01), p. 171-184
    Details
    In: Oikos, Wiley, Vol. 128, No. 2 ( 2019-01), p. 171-184
    Abstract: Intraspecific trait variation within natural populations (i.e. intra‐population trait variation, IPTV) is the basic source for selection and can have significant ecological consequences. Higher IPTV may increase a population's niche breath and benefit interspecies competition under a resource‐limited environment, thus affecting the ability of a species to move into novel habitats. However, the reciprocal influences of variation in environmental conditions and phenotypic trait expression in spreading plant populations are not clearly defined. We propose that during invasion, IPTV and its relative change in response to key resource enrichment may increase with the resource deficit of invaded sites, and that this relationship may facilitate plant invasions into resource‐limited environments. We analyzed the invasion trend, IPTV and its response to water enrichment, and moisture variability among populations of an annual grass Brachypodium hybridum in California, United States. We incorporated a genotyping‐by‐sequencing approach, a common garden experiment that had two water level treatments, and public plant and climate databases. Our hypothesis was supported by the observation that for populations that invaded sites with higher spring moisture deficit, both their seed biomass IPTV (for the water‐enriched treatment only) and relative change of the IPTV across water treatments were larger when examined in the common garden experiment. A generally north to south spreading direction was found in these B. hybridum populations, towards a drier and warmer climate exhibiting higher moisture deficit for plant growth. Our results suggest a role for interactions between IPTV (rather than trait means) and environmental resource availability in promoting plant invasions, providing new insights into the significance of IPTV in shaping plant geographic distributions.
    Type of Medium: Online Resource
    ISSN: 0030-1299 , 1600-0706
    URL: Issue
    URL: Article
    DOI: 10.1111/oik.2019.v128.i2
    DOI: 10.1111/oik.05548
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2025658-9
    detail.hit.zdb_id: 207359-6
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  • 8
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    Data_Sheet_2.xlsx
    Frontiers Media SA ; 2023
    In:  Frontiers in Psychiatry Vol. 14 ( 2023-8-1)
    Details
    In: Frontiers in Psychiatry, Frontiers Media SA, Vol. 14 ( 2023-8-1)
    Abstract: Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. Methods We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Results Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Discussion Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.
    Type of Medium: Online Resource
    ISSN: 1664-0640
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fpsyt.2023.1178633
    DOI: 10.3389/fpsyt.2023.1178633.s001
    DOI: 10.3389/fpsyt.2023.1178633.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2564218-2
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  • 9
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    Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?
    Elsevier BV ; 2020
    In:  Current Opinion in Biotechnology Vol. 61 ( 2020-02), p. 217-225
    Details
    In: Current Opinion in Biotechnology, Elsevier BV, Vol. 61 ( 2020-02), p. 217-225
    Type of Medium: Online Resource
    ISSN: 0958-1669
    URL: Article
    DOI: 10.1016/j.copbio.2020.01.010
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2019676-3
    SSG: 12
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  • 10
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    Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models
    Springer Science and Business Media LLC ; 2020
    In:  Genome Biology Vol. 21, No. 1 ( 2020-12)
    Details
    In: Genome Biology, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2020-12)
    Abstract: A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.
    Type of Medium: Online Resource
    ISSN: 1474-760X
    URL: Article
    DOI: 10.1186/s13059-020-02191-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2040529-7
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