Effectiveness of organised versus opportunistic mammography screening

Background: Detailed comparison of effectiveness between organised and opportunistic mammography screening operating in the same country has seldom been carried out. Patients and methods: Prognostic indicators, as defined in the European Guidelines, were used to evaluate screening effectiveness in Switzerland. Matching of screening programmes' records with population-based cancer registries enabled to compare indicators of effectiveness by screening and detection modality (organised versus opportunistic screening, unscreened, interval cancers). Comparisons of prognostic profile were also drawn with two Swiss regions uncovered by service screening of low and high prevalence of opportunistic screening, respectively. Results: Opportunistic and organised screening yielded overall little difference in prognostic profile. Both screening types led to substantial stage shifting. Breast cancer prognostic indicators were systematically more favourable in Swiss regions covered by a programme. In regions without a screening programme, the higher the prevalence of opportunistic screening, the better was the prognostic profile. Conclusions: Organised screening appeared as effective as opportunistic screening. Mammography screening has strongly influenced the stage distribution of breast cancer in Switzerland, and a favourable impact on mortality is anticipated. Extension of organised mammography screening to the whole of Switzerland can be expected to further improve breast cancer prognosis in a cost-effective wa

Non-archimedean integrals as limits of complex integrals

We explain how non-archimedean integrals considered by Chambert-Loir and Ducros naturally arise in asymptotics of families of complex integrals. To perform this analysis we work over a non-standard model of the field of complex numbers, which is endowed at the same time with an archimedean and a non-archimedean norm. Our main result states the existence of a natural morphism between bicomplexes of archimedean and non-archimedean forms which is compatible with integration.Comment: 55 page

Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10 5 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Material Decomposition in Spectral CT using deep learning: A Sim2Real transfer approach

The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomography is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specific materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. We compared projection- and image-based decomposition approaches where the network is trained to decompose the materials either in the projection or in the image domain. The proposed Sim2Real transfer strategies are compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data

Particles dispersion in supersonic shear layers by direct numerical simulation

In experimental measurements like Laser Doppler Velocimetry, small solid or liquid particles are used to tag the flow in order to measure fluid velocity. In this case, particles are supposed to have the same behaviour as fluid particles in order to give reliability to the experimental measure. However it has been shown that noticeable errors can appear in the rms velovity measurement of supersonic jet or shear layer, even if care has been taken concerning particle seeding of the flow. The aim of this paper is to use direct numerical simulation of particle-gas flow to investigate this phenomenon

Boundary value problems for second order linear difference equations: application to the computation of the inverse of generalized Jacobi matrices

We have named generalized Jacobi matrices to those that are practically tridiagonal, except for the two final entries and the two first entries of its first andits last row respectively. This class of matrices encompasses both standard Jacobiand periodic Jacobi matrices that appear in many contexts in pure and appliedmathematics. Therefore, the study of the inverse of these matrices becomes ofspecific interest. However, explicit formulas for inverses are known only in a fewcases, in particular when the coefficients of the diagonal entries are subjected tosome restrictions.We will show that the inverse of generalized Jacobi matrices can be raisedin terms of the resolution of a boundary value problem associated with a secondorder linear difference equation. In fact, recent advances in the study of lineardifference equations, allow us to compute the solution of this kind of boundaryvalue problems. So, the conditions that ensure the uniqueness of the solution ofthe boundary value problem leads to the invertibility conditions for the matrix,whereas that solutions for suitable problems provide explicitly the entries of theinverse matrix.Peer ReviewedPostprint (author's final draft

Energy Transfer and Spectra in Simulations of Two-dimensional Compressible Turbulence

We present results of high-resolution numerical simulations of compressible 2D turbulence forced at intermediate spatial scales with a solenoidal white-in-time external acceleration. A case with an isothermal equation of state, low energy injection rate, and turbulent Mach number $M\approx0.34$ without energy condensate is studied in detail. Analysis of energy spectra and fluxes shows that the classical dual-cascade picture familiar from the incompressible case is substantially modified by compressibility effects. While the small-scale direct enstrophy cascade remains largely intact, a large-scale energy flux loop forms with the direct acoustic energy cascade compensating for the inverse transfer of solenoidal kinetic energy. At small scales, the direct enstrophy and acoustic energy cascades are fully decoupled at small Mach numbers and hence the corresponding spectral energy slopes comply with theoretical predictions, as expected. At large scales, dispersion of acoustic waves on vortices softens the dilatational velocity spectrum, while the pseudo-sound component of the potential energy associated with coherent vortices steepens the potential energy spectrum.Comment: 10 pages, 6 figures. To appear in: Turbulence in Complex Conditions, Proc. Euromech/Ercoftac Colloquium 589, ed. M. Gorokhovski, Springer, 201

Limits on WWgamma and WWZ Couplings from W Boson Pair Production

The results of a search for W boson pair production in pbar-p collisions at sqrt{s}=1.8 TeV with subsequent decay to emu, ee, and mumu channels are presented. Five candidate events are observed with an expected background of 3.1+-0.4 events for an integrated luminosity of approximately 97 pb^{-1}. Limits on the anomalous couplings are obtained from a maximum likelihood fit of the E_T spectra of the leptons in the candidate events. Assuming identical WWgamma and WWZ couplings, the 95 % C.L. limits are -0.62<Delta_kappa<0.77 (lambda = 0) and -0.53<lambda<0.56 (Delta_kappa = 0) for a form factor scale Lambda = 1.5 TeV.Comment: 10 pages, 1 figure, submitted to Physical Review

Spin Correlation in tt-bar Production from pp-bar Collisions at sqrt(s)=1.8 TeV

The D0 collaboration has performed a study of spin correlation in tt-bar production for the process tt-bar to bb-bar W^+W^-, where the W bosons decay to e-nu or mu-nu. A sample of six events was collected during an exposure of the D0 detector to an integrated luminosity of approximately 125 pb^-1 of sqrt{s}=1.8 TeV pp-bar collisions. The standard model (SM) predicts that the short lifetime of the top quark ensures the transmission of any spin information at production to the tt-bar decay products. The degree of spin correlation is characterized by a correlation coefficient k. We find that k>-0.25 at the 68% confidence level, in agreement with the SM prediction of k=0.88.Comment: Submitted to PRL, Added references, minor changes to tex