0000000000236316
AUTHOR
L. Wood
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino…
RANDOMIZED PHASE II STUDY OF FIRST-LINE EVEROLIMUS (EVE) + BEVACIZUMAB (BEV) VERSUS INTERFERON ALFA-2A (IFN) + BEV IN PATIENTS (PTS) WITH METASTATIC RENAL CELL CARCINOMA (MRCC): RECORD-2
ABSTRACT Background Study results demonstrated that IFN augments BEV activity and improves median PFS in pts with mRCC. Thus, combination BEV + IFN is a standard first-line treatment option for mRCC. Combining BEV with the mTOR inhibitor EVE may be an efficacious and well-tolerated treatment option. The open-label, phase II RECORD-2 trial compared first-line EVE + BEV and IFN + BEV in mRCC. Patients and methods: Therapy-naive pts with clear cell mRCC and prior nephrectomy were randomized 1:1 to BEV 10 mg/kg IV every 2 weeks with either EVE 10 mg oral daily or IFN (9 MIU SC 3 times/week, if tolerated). Tumour assessments were every 12 weeks. Primary objective was treatment effect on progress…