Enrichment of the hot intracluster medium: observations

Four decades ago, the firm detection of an Fe-K emission feature in the X-ray spectrum of the Perseus cluster revealed the presence of iron in its hot intracluster medium (ICM). With more advanced missions successfully launched over the last 20 years, this discovery has been extended to many other metals and to the hot atmospheres of many other galaxy clusters, groups, and giant elliptical galaxies, as evidence that the elemental bricks of life - synthesized by stars and supernovae - are also found at the largest scales of the Universe. Because the ICM, emitting in X-rays, is in collisional ionisation equilibrium, its elemental abundances can in principle be accurately measured. These abundance measurements, in turn, are valuable to constrain the physics and environmental conditions of the Type Ia and core-collapse supernovae that exploded and enriched the ICM over the entire cluster volume. On the other hand, the spatial distribution of metals across the ICM constitutes a remarkable signature of the chemical history and evolution of clusters, groups, and ellipticals. Here, we summarise the most significant achievements in measuring elemental abundances in the ICM, from the very first attempts up to the era of XMM-Newton, Chandra, and Suzaku and the unprecedented results obtained by Hitomi. We also discuss the current systematic limitations of these measurements and how the future missions XRISM and Athena will further improve our current knowledge of the ICM enrichment.Comment: 49 pages. Review paper. Accepted for publication on Space Science Reviews. This is the companion review of "Enrichment of the hot intracluster medium: numerical simulations

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Beta-blocker treatment guided by head-up tilt test in neurally mediated syncope

This study was an open-label, uncontrolled, dose-escalation trial of beta-blockers in patients with a history of syncope without warning or syncope resulting in trauma (malignant vasovagal syncope) who had positive head-up tilt test (HUT) responses, with or without isoproterenol infusion. Thirty patients (mean age, 37 +/- 21 years) with recurrent syncopal and near-syncopal episodes of unexplained origin in the previous year (6 +/- 14 syncopal episodes and 17 +/- 3 near-syncopes) underwent HUT for diagnostic purposes and for guiding prophylactic treatment. After patients were given a 10-minute rest in a recumbent position, rye performed an WT at 70 degrees for 25 minutes; if indicated, isoproterenol testing was performed at incremental dosages (dye steps at 10-minute intervals at 80 degrees), AU patients experienced syncope during HUT, 15 (50%) at baseline HUT and 15 (50%) during isoproterenol infusion (1 to 3 mu g/min; mean, 1.6 mu g/min). Sixteen syncopes were of vasodepressor type, 10 were mixed, and 4 were of cardioinhibitory type. After baseline HUT, betablocking drugs were prescribed to all patients as follows: 1 patient was given propranolol (160 mg daily), and 29 patients were given metoprolol (246 +/- 49 mg daily), with a dose titration period of 14 days. HUT was repeated after 3 weeks, and 24 patients (80%) had negative results (no syncope or anomalous responses). After further dosage adjustment of beta-blockers in nonresponders, a negative HUT was obtained in 28 patients (93%). Overall mean metoprolol daily dose was 262 +/- 60 mg (29 patients), and propranolol was administered at 160 mg daily in 1 patient. Thirteen patients (43%) reported side effects, none of which required drug withdrawal. At an average follow-up of 16 +/- 4 months, none of the patients experienced syncope, a statistically significant reduction. Moreover, a statistically significant reduction in the number of near-syncopal episodes was observed in comparison to the previous year. None of the patients discontinued treatment because of long-term side effects. Beta-blockers were well tolerated and achieved a high rate of efficacy, even in cardioinhibitory syncopes. In conclusion, in selected patients with malignant vasovagal syncope, treatment with metoprolol or propranolol at relatively high doses is feasible and, if guided by HUT results, is associated with a favorable outcome in terms of freedom from syncopal recurrences. Appropriate titration to achieve the full beta-blocking effect appears to be advisable

Phase Transformations in the CeO2-Sm2O3System : A Multiscale Powder Diffraction Investigation

The structure evolution in the CeO2-Sm2O3system is revisited by combining high resolution synchrotron powder diffraction with pair distribution function (PDF) to inquire about local, mesoscopic, and average structure. The CeO2fluorite structure undergoes two phase transformations by Sm doping, first to a cubic (C-type) and then to a monoclinic (B-type) phase. Whereas the C to B-phase separation occurs completely and on a long-range scale, no miscibility gap is detected between fluorite and C-type phases. The transformation rather occurs by growth of C-type nanodomains embedded in the fluorite matrix, without any long-range phase separation. A side effect of this mechanism is the ordering of the oxygen vacancies, which is detrimental for the application of doped ceria as an electrolyte in fuel cells. The results are discussed in the framework of other Y and Gd dopants, and the relationship between nanostructuring and the above equilibria is also investigated

Financial applications based on Gram-Charlier expansions

The reliability of risk measures of financial portfolios crucially rests on the availability of sound representations of the involved random variables. The trade-off between adherence to reality and specification parsimony can find a fitting balance in a technique that \u201dadjust\u201d the moments of a density function by making use of its associated orthogonal polynomials. This approach essentially rests on the Gram-Charlier expansion of a Gaussian law which, allowing for leptokurtosis to an appreciable extent, makes the resulting random variable a tail-sensitive density function. In this paper we determine the density of sums of leptokurtic normal variables duly adjusted for excess kurtosis via their Gram-Charlier expansions based on Hermite polynomials. The aforesaid density can be properly used to compute some risk measures such as the Value at Risk and the expected short fall. An application to a portfolio of financial returns provides evidence of the effectiveness of the proposed approach

Cosmological hydrodynamical simulations of galaxy clusters: X-ray scaling relations and their evolution

We analyse cosmological hydrodynamical simulations of galaxy clusters to study the X-ray scaling relations between total masses and observable quantities such as X-ray luminosity, gas mass, X-ray temperature, and $Y_{X}$. Three sets of simulations are performed with an improved version of the smoothed particle hydrodynamics GADGET-3 code. These consider the following: non-radiative gas, star formation and stellar feedback, and the addition of feedback by active galactic nuclei (AGN). We select clusters with $M_{500} > 10^{14} M_{\odot} E(z)^{-1}$, mimicking the typical selection of Sunyaev-Zeldovich samples. This permits to have a mass range large enough to enable robust fitting of the relations even at $z \sim 2$. The results of the analysis show a general agreement with observations. The values of the slope of the mass-gas mass and mass-temperature relations at $z=2$ are 10 per cent lower with respect to $z=0$ due to the applied mass selection, in the former case, and to the effect of early merger in the latter. We investigate the impact of the slope variation on the study of the evolution of the normalization. We conclude that cosmological studies through scaling relations should be limited to the redshift range $z=0-1$, where we find that the slope, the scatter, and the covariance matrix of the relations are stable. The scaling between mass and $Y_X$ is confirmed to be the most robust relation, being almost independent of the gas physics. At higher redshifts, the scaling relations are sensitive to the inclusion of AGNs which influences low-mass systems. The detailed study of these objects will be crucial to evaluate the AGN effect on the ICM.Comment: 24 pages, 11 figures, 5 tables, replaced to match accepted versio

In vivo biodistribution and lifetime analysis of cy5.5-conjugated rituximab in mice bearing lymphoid tumor xenograft using time-domain near-infrared optical imaging

Rituximab is a chimeric monoclonal antibody directed against human CD20 antigen, which is expressed on B-cell lymphocytes and on the majority of B-cell lymphoid malignancies. Herein we report the conjugate of rituximab with the near-infrared (NIR) fluorophore Cy5.5 (RI-Cy5.5) as a tool for in vitro, in vivo, and ex vivo NIR time-domain (TD) optical imaging. In vitro, RI-Cy5.5 retained biologic activity and led to elevated cell-associated fluorescence on tumor cells. In vivo, TD optical imaging analysis of RI-Cy5.5 injected into lymphoma-bearing mice revealed a slow tumor uptake and a specific long-lasting persistence of the probe within the tumor. Biodistribution studies after intraperitoneal and endovenous administration were undertaken to evaluate differences in the tumor uptake. RI-Cy5.5 concentration in the organs after intraperitoneal injection was not as high as after endovenous injection. Ex vivo analysis of biologic tissues and organs by both TD optical imaging and immunohistochemistry confirmed the probe distribution, as demonstrated by imaging experiment in vivo, showing that RI-Cy5.5 selectively accumulated in the tumor tissue and major excretion organs. In summary, the study indicates that NIR TD optical imaging is a powerful tool for rituximab-targeting investigation, furthering understanding of its administration outcome in lymphoma treatment

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival