Correlations and the relativistic structure of the nucleon self-energy

A key point of Dirac Brueckner Hartree Fock calculations for nuclear matter is to decompose the self energy of the nucleons into Lorentz scalar and vector components. A new method is introduced for this decomposition. It is based on the dependence of the single-particle energy on the small component in the Dirac spinors used to calculate the matrix elements of the underlying NN interaction. The resulting Dirac components of the self-energy depend on the momentum of the nucleons. At densities around and below the nuclear matter saturation density this momentum dependence is dominated by the non-locality of the Brueckner G matrix. At higher densities these correlation effects are suppressed and the momentum dependence due to the Fock exchange terms is getting more important. Differences between symmetric nuclear matter and neutron matter are discussed. Various versions of the Bonn potential are considered.Comment: 18 pages LaTeX, including 6 figure

Scalar and vector decomposition of the nucleon self-energy in the relativistic Brueckner approach

We investigate the momentum dependence of the nucleon self-energy in nuclear matter. We apply the relativistic Brueckner-Hartree-Fock approach and adopt the Bonn A potential. A strong momentum dependence of the scalar and vector self-energy components can be observed when a commonly used pseudo-vector choice for the covariant representation of the T-matrix is applied. This momentum dependence is dominated by the pion exchange. We discuss the problems of this choice and its relations to on-shell ambiguities of the T-matrix representation. Starting from a complete pseudo-vector representation of the T-matrix, which reproduces correctly the pseudo-vector pion-exchange contributions at the Hartree-Fock level, we observe a much weaker momentum dependence of the self-energy. This fixes the range of the inherent uncertainty in the determination of the scalar and vector self-energy components. Comparing to other work, we find that extracting the self-energy components by a fit to the single particle potential leads to even more ambiguous results.Comment: 35 pages RevTex, 7 PS figures, replaced by a revised and extended versio

Coating of bioactive glasses with chitosan: The effects of the glass composition and coating method on the surface properties, including preliminary in vitro results

Two bioactive glasses were coated with chitosan: SCNB belongs to the SiO2-CaO-Na2O system, and SCNA has the addition of Al2O3 to enhance chemical stability. Different coating methods were compared: direct physical attachment, surface activation through tresyl chloride, and polydopamine as a linker. The samples were char-acterized through SEM-EDS, contact angle measurements, FTIR, zeta potential titrations, tape tests, in vitro precipitation of hydroxylapatite (bioactivity), and cell cultures (RAW 264.7 and UMR-106) on some selected samples. Direct physical attachment (in acetic acid) or use of polydopamine allowed complete surface coverage, while it dropped to one-quarter on both glasses by using tresyl chloride. The coating had a contact angle of about 80 degrees and it well showed typical functional groups (FTIR). The coatings on SCNA were chemically and mechan-ically stable (classified as 4-5B by the tape tests, partially maintained after soaking for 14 days), and showed an isoelectric point around 8. On SCNB, the coatings were unstable (classified as 0-3B, dissolved during soaking) but bioactivity was preserved. The coating affected the biological outcome of SCNA with M0/M1 polarization (1 day) and reduced viability of macrophages (3 days), while osteoblastic cells showed poor adhesion but maintained cell viability and osteogenic potential (3-7 days)

Influence of the in-medium pion dispersion relation in heavy ion collisions

We investigate the influence of medium corrections to the pion dispersion relation on the pion dynamics in intermediate energy heavy ion collisions. To do so a pion potential is extracted from the in-medium dispersion relation and used in QMD calculations and thus we take care of both, real and imaginary part of the pion optical potential. The potentials are determined from different sources, i.e. from the $\Delta$--hole model and from phenomenological approaches. Depending on the strength of the potential a reduction of the anti-correlation of pion and nucleon flow in non-central collisions is observed as well as an enhancement of the high energetic yield in transverse pion spectra. A comparison to experiments, in particular to $p_t$-spectra for the reaction Ca+Ca at 1 GeV/nucleon and the pion in-plane flow in Ne+Pb collisions at 800 MeV/nucleon, generally favours a weak potential.Comment: 25 pages, using REVTeX, 6 postscript figures; replaced by published versio

Relativistic Brueckner-Hartree-Fock calculations with explicit intermediate negative energy states

In a relativistic Brueckner-Hartree-Fock calculation we include explicit negative-energy states in the two-body propagator. This is achieved by using the Gross spectator-equation, modified by medium effects. Qualitatively our results compare well with other RBHF calculations. In some details significant differences occur, e.g, our equation of state is stiffer and the momentum dependence of the self-energy components is stronger than found in a reference calculation without intermediate negative energy states.Comment: 13 pages Revtex, 5 figures included seperatel

Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients

The time-consuming task of manual segmentation challenges routine systematic quantification of disease burden. Convolutional neural networks (CNNs) hold significant promise to reliably identify locations and boundaries of tumors from PET scans. We aimed to leverage the need for annotated data via semi-supervised approaches, with application to PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL). We analyzed 18F-FDG PET images of 292 patients with PMBCL (n=104) and DLBCL (n=188) (n=232 for training and validation, and n=60 for external testing). We employed FCM and MS losses for training a 3D U-Net with different levels of supervision: i) fully supervised methods with labeled FCM (LFCM) as well as Unified focal and Dice loss functions, ii) unsupervised methods with Robust FCM (RFCM) and Mumford-Shah (MS) loss functions, and iii) Semi-supervised methods based on FCM (RFCM+LFCM), as well as MS loss in combination with supervised Dice loss (MS+Dice). Unified loss function yielded higher Dice score (mean +/- standard deviation (SD)) (0.73 +/- 0.03; 95% CI, 0.67-0.8) compared to Dice loss (p-value<0.01). Semi-supervised (RFCM+alpha*LFCM) with alpha=0.3 showed the best performance, with a Dice score of 0.69 +/- 0.03 (95% CI, 0.45-0.77) outperforming (MS+alpha*Dice) for any supervision level (any alpha) (p<0.01). The best performer among (MS+alpha*Dice) semi-supervised approaches with alpha=0.2 showed a Dice score of 0.60 +/- 0.08 (95% CI, 0.44-0.76) compared to another supervision level in this semi-supervised approach (p<0.01). Semi-supervised learning via FCM loss (RFCM+alpha*LFCM) showed improved performance compared to supervised approaches. Considering the time-consuming nature of expert manual delineations and intra-observer variabilities, semi-supervised approaches have significant potential for automated segmentation workflows