Razionali emozioni

Articolo di introduzione ad altro articolo sul tema della Human Resources Managemen

Cool Core Clusters from Cosmological Simulations

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and AGN feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and on the iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of cool-core systems, to nearly flat core isentropic profiles, characteristic of non-cool-core systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and in observations. Furthermore, we also find that simulated cool-core clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic cool-core structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.Comment: 6 pages, 4 figures, accepted in ApJL, v2 contains some modifications on the text (results unchanged

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

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

Biodegradable magnesium coronary stents: Material, design and fabrication

Biodegradable cardiovascular stents in magnesium (Mg) alloys constitute a promising option for a less intrusive treatment, due to their high compatibility with the body tissue and intrinsic dissolution in body fluids. The design and fabrication aspects of this medical device require an integrated approach considering different aspects such as mechanical properties, corrosion behaviour and biocompatibility. This work gathers and summarises a multidisciplinary work carried out by three different research teams for the design and fabrication of Mg stents. In particular, the paper discusses the design of the novel stent mesh, the deformability study of the Mg alloys for tubular raw material and laser microcutting for the realisation of the stent mesh. Although, the results are not fully validated as the device has not been fully tested, they show the feasibility of the used approaches, as the first prototype stents in Mg alloy were produced successfully. © 2013 Copyright Taylor and Francis Group, LLC

Abnormal ECG Findings in Athletes: Clinical Evaluation and Considerations.

PURPOSE OF REVIEW: Pre-participation cardiovascular evaluation with electrocardiography is normal practice for most sporting bodies. Awareness about sudden cardiac death in athletes and recognizing how screening can help identify vulnerable athletes have empowered different sporting disciplines to invest in the wellbeing of their athletes. RECENT FINDINGS: Discerning physiological electrical alterations due to athletic training from those representing cardiac pathology may be challenging. The mode of investigation of affected athletes is dependent on the electrical anomaly and the disease(s) in question. This review will highlight specific pathological ECG patterns that warrant assessment and surveillance, together with an in-depth review of the recommended algorithm for evaluation

3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders

Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 )