Disentangling potential genotypes for macro and micro nutrients and polymorphic markers in Chickpea

Abstract The present investigation was conducted to assess the nutritional diverseness and identify novel genetic resources to be utilized in chickpea breeding for macro and micro nutrients. The plants were grown in randomized block design. Nutritional and phytochemical properties of nine chickpea genotypes were estimated. The EST sequences from NCBI database were downloaded in FASTA format, clustered into contigs using CAP3, mined for novel SSRs using TROLL analysis and primer pairs were designed using Primer 3 software. Jaccard’s similarity coefficients were used to compare the nutritional and molecular indexes followed by dendrograms construction employing UPGMA approach. The genotypes PUSA-1103, K-850, PUSA-1108, PUSA-1053 and the EST-SSR markers including the 5 newly designed namely ICCeM0012, ICCeM0049, ICCeM0067, ICCeM0070, ICCeM0078, SVP55, SVP95, SVP96, SVP146, and SVP217 were found as potential donor/marker resources for the macro–micro nutrients. The genotypes differed (p < 0.05) for nutritional properties. Amongst newly designed primers, 6 were found polymorphic with median PIC (0.46). The alleles per primer ranged 1 to 8. Cluster analysis based on nutritional and molecular diversities partially matched to each other in principle. The identified novel genetic resources may be used to widen the germplasm base, prepare maintainable catalogue and identify systematic blueprints for future chickpea breeding strategies targeting macro–micro nutrients

DUNE Offline Computing Conceptual Design Report

International audienceThis document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe 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$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation