Single Proton Knock-Out Reactions from 24,25,26F

The cross sections of the single proton knock-out reactions from 24F, 25F, and 26F on a 12C target were measured at energies of about 50 MeV/nucleon. Ground state populations of 6.6+-.9 mb, 3.8+-0.6 mb for the reactions 12C(24F,23O) and 12C(25F,24O) were extracted, respectively. The data were compared to calculations based on the many-body shell model and the eikonal theory. In the reaction 12C(26F,25O) the particle instability of 25O was confirmed

Nuclear vorticity and the low-energy nuclear response - Towards the neutron drip line

The transition density and current provide valuable insight into the nature of nuclear vibrations. Nuclear vorticity is a quantity related to the transverse transition current. In this work, we study the evolution of the strength distribution, related to density fluctuations, and the vorticity strength distribution, as the neutron drip line is approached. Our results on the isoscalar, natural-parity multipole response of Ni isotopes, obtained by using a self-consistent Skyrme-Hartree-Fock + Continuum RPA model, indicate that, close to the drip line, the low-energy response is dominated by L>1 vortical transitions.Comment: 8 pages, incl. 4 figures; to appear in Phys.Lett.

Soft Dipole Modes in Neutron-rich Ni-isotopes in QRRPA

The soft dipole modes in neutron rich even-even Ni-isotopes are investigated in the quasiparticle relativistic random phase approximation. We study the evolution of strengths distribution, centroid energies of dipole excitation in low-lying and normal GDR regions with the increase of the neutron excess. It is found in the present study that the centroid energies of the soft dipole strengths strongly depend on the thickness of neutron skin along with the neutron rich even-even Ni-isotopes.Comment: 14 pages, 7 figure

Crossing the Dripline to 11N Using Elastic Resonance Scattering

The level structure of the unbound nucleus 11N has been studied by 10C+p elastic resonance scattering in inverse geometry with the LISE3 spectrometer at GANIL, using a 10C beam with an energy of 9.0 MeV/u. An additional measurement was done at the A1200 spectrometer at MSU. The excitation function above the 10C+p threshold has been determined up to 5 MeV. A potential-model analysis revealed three resonance states at energies 1.27 (+0.18-0.05) MeV (Gamma=1.44 +-0.2 MeV), 2.01(+0.15-0.05) MeV, (Gamma=0.84 +-$0.2 MeV) and 3.75(+-0.05) MeV, (Gamma=0.60 +-0.05 MeV) with the spin-parity assignments I(pi) =1/2+, 1/2- and 5/2+, respectively. Hence, 11N is shown to have a ground state parity inversion completely analogous to its mirror partner, 11Be. A narrow resonance in the excitation function at 4.33 (+-0.05) MeV was also observed and assigned spin-parity 3/2-.Comment: 14 pages, 9 figures, twocolumn Accepted for publication in PR

De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development

Bosma arhinia microphthalmia syndrome (BAMS) is an extremely rare and striking condition characterized by complete absence of the nose with or without ocular defects. We report here that missense mutations in the epigenetic regulator SMCHD1 mapping to the extended ATPase domain of the encoded protein cause BAMS in all 14 cases studied. All mutations were de novo where parental DNA was available. Biochemical tests and in vivo assays in Xenopus laevis embryos suggest that these mutations may behave as gain-of-function alleles. This finding is in contrast to the loss-of-function mutations in SMCHD1 that have been associated with facioscapulohumeral muscular dystrophy (FSHD) type 2. Our results establish SMCHD1 as a key player in nasal development and provide biochemical insight into its enzymatic function that may be exploited for development of therapeutics for FSHD

Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

IntroductionOrgan-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based autosegmentation model to reduce contouring time without compromising contour accuracy is assessed through a blinded randomized trial of radiation oncologists (ROs) using retrospective, de-identified patient data.MethodsTwo head and neck expert ROs used dedicated time to create gold standard (GS) contours on computed tomography (CT) images. 445 CTs were used to train a custom 3D U-Net DL model covering 42 organs-at-risk, with an additional 20 CTs were held out for the randomized trial. For each held-out patient dataset, one of the eight participant ROs was randomly allocated to review and revise the contours produced by the DL model, while another reviewed contours produced by a medical dosimetry assistant (MDA), both blinded to their origin. Time required for MDAs and ROs to contour was recorded, and the unrevised DL contours, as well as the RO-revised contours by the MDAs and DL model were compared to the GS for that patient.ResultsMean time for initial MDA contouring was 2.3 hours (range 1.6-3.8 hours) and RO-revision took 1.1 hours (range, 0.4-4.4 hours), compared to 0.7 hours (range 0.1-2.0 hours) for the RO-revisions to DL contours. Total time reduced by 76% (95%-Confidence Interval: 65%-88%) and RO-revision time reduced by 35% (95%-CI,-39%-91%). All geometric and dosimetric metrics computed, agreement with GS was equivalent or significantly greater (p<0.05) for RO-revised DL contours compared to the RO-revised MDA contours, including volumetric Dice similarity coefficient (VDSC), surface DSC, added path length, and the 95%-Hausdorff distance. 32 OARs (76%) had mean VDSC greater than 0.8 for the RO-revised DL contours, compared to 20 (48%) for RO-revised MDA contours, and 34 (81%) for the unrevised DL OARs.ConclusionDL autosegmentation demonstrated significant time-savings for organ-at-risk contouring while improving agreement with the institutional GS, indicating comparable accuracy of DL model. Integration into the clinical practice with a prospective evaluation is currently underway