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The auxin-inducible degradation system has been widely adopted in the Caenorhabditis elegans research community for its ability to empirically control the spatiotemporal expression of target proteins. This system can efficiently degrade auxin-inducible degron (AID)-tagged proteins via the expression of a ligand-activatable AtTIR1 protein derived from A. thaliana that adapts target proteins to the endogenous C. elegans proteasome. While broad expression of AtTIR1 using strong, ubiquitous promoters can lead to rapid degradation of AID-tagged proteins, cell type-specific expression of AtTIR1 using spatially restricted promoters often results in less efficient target protein degradation. To circumvent this limitation, we have developed an FLP/FRT3-based system that functions to reanimate a dormant, high-powered promoter that can drive sufficient AtTIR1 expression in a cell type-specific manner. We benchmark the utility of this system by generating a number of tissue-specific FLP-ON::TIR1 drivers to reveal genetically separable cell type-specific phenotypes for several target proteins. We also demonstrate that the FLP-ON::TIR1 system is compatible with enhanced degron epitopes. Finally, we provide an expandable toolkit utilizing the basic FLP-ON::TIR1 system that can be adapted to drive optimized AtTIR1 expression in any tissue or cell type of interest.

 

Abstract The auxin-inducible degron (AID) system has emerged as a powerful tool to conditionally deplete proteins in a range of organisms and cell types. Here, we describe a toolkit to augment the use of the AID system in Caenorhabditis elegans. We have generated a set of single-copy, tissue-specific (germline, intestine, neuron, muscle, pharynx, hypodermis, seam cell, anchor cell) and pan-somatic TIR1-expressing strains carrying a co-expressed blue fluorescent reporter to enable use of both red and green channels in experiments. These transgenes are inserted into commonly used, well-characterized genetic loci. We confirmed that our TIR1-expressing strains produce the expected depletion phenotype for several nuclear and cytoplasmic AID-tagged endogenous substrates. We have also constructed a set of plasmids for constructing repair templates to generate fluorescent protein::AID fusions through CRISPR/Cas9-mediated genome editing. These plasmids are compatible with commonly used genome editing approaches in the C. elegans community (Gibson or SapTrap assembly of plasmid repair templates or PCR-derived linear repair templates). Together these reagents will complement existing TIR1 strains and facilitate rapid and high-throughput fluorescent protein::AID tagging of genes. This battery of new TIR1-expressing strains and modular, efficient cloning vectors serves as a platform for straightforward assembly of CRISPR/Cas9 repair templates for conditional protein depletion. 

Abstract The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/ c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation. 

Abstract The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype. 

Abstract Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.