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  1. ; ; ; ( , PLOS Computational Biology)
    Coelho, Luis Pedro (Ed.)
    Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0more »retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.« less
    Free, publicly-accessible full text available January 18, 2023
  2. ; ; ; ; ; ; ( , Journal of Biological Chemistry)
    Free, publicly-accessible full text available November 1, 2022
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Applied Microbiology and Biotechnology)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ; ; ; ; ( , Virus Research)
    Free, publicly-accessible full text available September 1, 2022
  5. ; ; ( , Structure)
    Full Text Available 
  6. ; ; ; ; ; ; ( , Journal of Virology)
    López, Susana (Ed.)
    ABSTRACT The rotavirus polymerase VP1 mediates all stages of viral RNA synthesis within the confines of subviral particles and while associated with the core shell protein VP2. Transcription (positive-strand RNA [+RNA] synthesis) by VP1 occurs within double-layered particles (DLPs), while genome replication (double-stranded RNA [dsRNA] synthesis) by VP1 occurs within assembly intermediates. VP2 is critical for VP1 enzymatic activity; yet, the mechanism by which the core shell protein triggers polymerase function remains poorly understood. Structural analyses of transcriptionally competent DLPs show that VP1 is located beneath the VP2 core shell and sits slightly off-center from each of the icosahedral 5-foldmore »axes. In this position, the polymerase is contacted by the core shell at 5 distinct surface-exposed sites, comprising VP1 residues 264 to 267, 547 to 550, 614 to 620, 968 to 980, and 1022 to 1025. Here, we sought to test the functional significance of these VP2 contact sites on VP1 with regard to polymerase activity. We engineered 19 recombinant VP1 (rVP1) proteins that contained single- or multipoint alanine mutations within each individual contact site and assayed them for the capacity to synthesize dsRNA in vitro in the presence of rVP2. Three rVP1 mutants (E265A/L267A, R614A, and D971A/S978A/I980A) exhibited diminished in vitro dsRNA synthesis. Despite their loss-of-function phenotypes, the mutants did not show major structural changes in silico, and they maintained their overall capacity to bind rVP2 in vitro via their nonmutated contact sites. These results move us toward a mechanistic understanding of rotavirus replication and identify precise VP2-binding sites on the polymerase surface that are critical for its enzymatic activation. IMPORTANCE Rotaviruses are important pathogens that cause severe gastroenteritis in the young of many animals. The viral polymerase VP1 mediates all stages of viral RNA synthesis, and it requires the core shell protein VP2 for its enzymatic activity. Yet, there are several gaps in knowledge about how VP2 engages and activates VP1. Here, we probed the functional significance of 5 distinct VP2 contact sites on VP1 that were revealed through previous structural studies. Specifically, we engineered alanine amino acid substitutions within each of the 5 VP1 regions and assayed the mutant polymerases for the capacity to synthesize RNA in the presence of VP2 in a test tube. Our results identified residues within 3 of the VP2 contact sites that are critical for robust polymerase activity. These results are important because they enhance the understanding of a key step of the rotavirus replication cycle.« less
    Full Text Available 
  7. ; ; ; ; ; ; ; ; ; ; et al ( , Computing and Software for Big Science)
    Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hardmore »scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.« less
    Free, publicly-accessible full text available December 1, 2023
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Computing and Software for Big Science)
    Abstract The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed tomore »meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.« less
    Free, publicly-accessible full text available December 1, 2023
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
    Free, publicly-accessible full text available May 1, 2023
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
    Free, publicly-accessible full text available May 1, 2023