12 research outputs found
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
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 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 data and simulation
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
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
Okara residue as source of antioxidants against lipid oxidation in milk enriched with omega-3 and bioavailability of bioactive compounds after in vitro gastrointestinal digestion
LRG1 promotes proliferation and inhibits apoptosis in colorectal cancer cells via RUNX1 activation
Physicochemical and Bioactive Properties of Soluble Dietary Fibers from Blasting Extrusion Processing (BEP)-Extruded Carrot Residues
High hydrostatic pressure processing enhances pectin solubilisation on apple by-product improving techno-functional properties
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Stem cell-like transcriptional reprogramming mediates metastatic resistance to mTOR inhibition.
Inhibitors of the mechanistic target of rapamycin (mTOR) are currently used to treat advanced metastatic breast cancer. However, whether an aggressive phenotype is sustained through adaptation or resistance to mTOR inhibition remains unknown. Here, complementary studies in human tumors, cancer models and cell lines reveal transcriptional reprogramming that supports metastasis in response to mTOR inhibition. This cancer feature is driven by EVI1 and SOX9. EVI1 functionally cooperates with and positively regulates SOX9, and promotes the transcriptional upregulation of key mTOR pathway components (REHB and RAPTOR) and of lung metastasis mediators (FSCN1 and SPARC). The expression of EVI1 and SOX9 is associated with stem cell-like and metastasis signatures, and their depletion impairs the metastatic potential of breast cancer cells. These results establish the mechanistic link between resistance to mTOR inhibition and cancer metastatic potential, thus enhancing our understanding of mTOR targeting failure
Profiles of Amino Acids and Acylcarnitines Related with Insecticide Exposure in Culex quinquefasciatus (Say)
Stem cell-like transcriptional reprogramming mediates metastatic resistance to mTOR inhibition
Inhibitors of the mechanistic target of rapamycin (mTOR) are currently used to treat advanced metastatic breast cancer. However,
whether an aggressive phenotype is sustained through adaptation or resistance to mTOR inhibition remains unknown. Here,
complementary studies in human tumors, cancer models and cell lines reveal transcriptional reprogramming that supports metastasis
in response to mTOR inhibition. This cancer feature is driven by EVI1 and SOX9. EVI1 functionally cooperates with and positively
regulates SOX9, and promotes the transcriptional upregulation of key mTOR pathway components (REHB and RAPTOR) and of lung
metastasis mediators (FSCN1 and SPARC). The expression of EVI1 and SOX9 is associated with stem cell-like and metastasis signatures,
and their depletion impairs the metastatic potential of breast cancer cells. These results establish the mechanistic link between
resistance to mTOR inhibition and cancer metastatic potential, thus enhancing our understanding of mTOR targeting failure