1,139 research outputs found
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 .
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 , 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 . 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 are 10 per cent lower
with respect to 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 , where we find that the slope, the scatter, and
the covariance matrix of the relations are stable. The scaling between mass and
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
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