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  1. Free, publicly-accessible full text available May 20, 2024
  2. Free, publicly-accessible full text available May 1, 2024
  3. Weather sensing and forecasting has become increasingly accurate in the last decade thanks to high-resolution radars, efficient computational algorithms, and high-performance computing facilities. Through a distributed and federated network of radars, scientists can make high-resolution observations of the weather conditions on a scale that benefits public safety, commerce, transportation, and other fields. While weather radars are critical infrastructure, they are often located in remote areas with poor network connectivity. Data retrieved from these radars are often delayed or lost, or even lack proper synchronization, resulting in sub-optimal weather prediction. This work applies Named Data Networking (NDN) to a federation of weather sensing radars for efficient content addressing and retrieval. We identify weather data based on a hierarchical naming scheme that allows us to explicitly access desired files. We demonstrate that compared to the window-based mechanism in TCP/IP, an NDN based mechanism improves data quality, reduces uncertainty, and enhances weather prediction. 
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  4. As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area). 
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    Free, publicly-accessible full text available March 6, 2024
  5. Long non-coding RNAs (lncRNAs) are an increasingly studied group of non-protein coding transcripts with a wide variety of molecular functions gaining attention for their roles in numerous biological processes. Nearly 6,000 lncRNAs have been identified in Arabidopsis thaliana but many have yet to be studied. Here, we examine a class of previously uncharacterized lncRNAs termed CONSERVED IN BRASSICA RAPA ( lncCOBRA ) transcripts that were previously identified for their high level of sequence conservation in the related crop species Brassica rapa , their nuclear-localization and protein-bound nature. In particular, we focus on lncCOBRA1 and demonstrate that its abundance is highly tissue and developmental specific, with particularly high levels early in germination. lncCOBRA1 contains two snoRNAs domains within it, making it the first sno-lincRNA example in a non-mammalian system. However, we find that it is processed differently than its mammalian counterparts. We further show that plants lacking lncCOBRA1 display patterns of delayed germination and are overall smaller than wild-type plants. Lastly, we identify the proteins that interact with lncCOBRA1 and propose a novel mechanism of lincRNA action in which it may act as a scaffold with the RACK1A protein to regulate germination and development, possibly through a role in ribosome biogenesis. 
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  6. Morrell, P (Ed.)
    Abstract By modeling the homoeologous gene losses that occurred in 50 genomes deriving from ten distinct polyploidy events, we show that the evolutionary forces acting on polyploids are remarkably similar, regardless of whether they occur in flowering plants, ciliates, fishes, or yeasts. We show that many of the events show a relative rate of duplicate gene loss before the first postpolyploidy speciation that is significantly higher than in later phases of their evolution. The relatively weak selective constraint experienced by the single-copy genes these losses produced leads us to suggest that most of the purely selectively neutral duplicate gene losses occur in the immediate postpolyploid period. Nearly all of the events show strong evidence of biases in the duplicate losses, consistent with them being allopolyploidies, with 2 distinct progenitors contributing to the modern species. We also find ongoing and extensive reciprocal gene losses (alternative losses of duplicated ancestral genes) between these genomes. With the exception of a handful of closely related taxa, all of these polyploid organisms are separated from each other by tens to thousands of reciprocal gene losses. As a result, it is very unlikely that viable diploid hybrid species could form between these taxa, since matings between such hybrids would tend to produce offspring lacking essential genes. It is, therefore, possible that the relatively high frequency of recurrent polyploidies in some lineages may be due to the ability of new polyploidies to bypass reciprocal gene loss barriers. 
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  7. In this paper, we describe how we extended the Pegasus Workflow Management System to support edge-to-cloud workflows in an automated fashion. We discuss how Pegasus and HTCondor (its job scheduler) work together to enable this automation. We use HTCondor to form heterogeneous pools of compute resources and Pegasus to plan the workflow onto these resources and manage containers and data movement for executing workflows in hybrid edge-cloud environments. We then show how Pegasus can be used to evaluate the execution of workflows running on edge only, cloud only, and edge-cloud hybrid environments. Using the Chameleon Cloud testbed to set up and configure an edge-cloud environment, we use Pegasus to benchmark the executions of one synthetic workflow and two production workflows: CASA-Wind and the Ocean Observatories Initiative Orcasound workflow, all of which derive their data from edge devices. We present the performance impact on workflow runs of job and data placement strategies employed by Pegasus when configured to run in the above three execution environments. Results show that the synthetic workflow performs best in an edge only environment, while the CASA - Wind and Orcasound workflows see significant improvements in overall makespan when run in a cloud only environment. The results demonstrate that Pegasus can be used to automate edge-to-cloud science workflows and the workflow provenance data collection capabilities of the Pegasus monitoring daemon enable computer scientists to conduct edge-to-cloud research. 
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  8. Abstract Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts that can have critical biological functions. The wealth of RNA-sequencing (RNA-seq) data available for plants provides the opportunity to implement a harmonized identification and annotation effort for lincRNAs that enables cross-species functional and genomic comparisons as well as prioritization of functional candidates. In this study, we processed >24 Tera base pairs of RNA-seq data from >16,000 experiments to identify ∼130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify conserved motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our data set with sequence or transcriptional conservation. Finally, we used guilt-by-association analyses to assign putative functions to lincRNAs within our data set. We tested this approach on a subset of lincRNAs associated with germination and seed development, observing germination defects for Arabidopsis lines harboring T-DNA insertions at these loci. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, small open reading frames, or abiotic-stress modulated expression are a few of the annotations that will guide functional analyses into this cryptic portion of the transcriptome. 
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