Angiotensin Receptor-Neprilysin Inhibitor (ARNI) and Cardiac Arrhythmias

The renin-angiotensin-aldosterone system (RAAS) plays a major role in cardiovascular health and disease. Short-term RAAS activation controls water and salt retention and causes vasoconstriction, which are beneficial for maintaining cardiac output in low blood pressure and early stage heart failure. However, prolonged RAAS activation is detrimental, leading to structural remodeling and cardiac dysfunction. Natriuretic peptides (NPs) are activated to counterbalance the effect of RAAS and sympathetic nervous system by facilitating water and salt excretion and causing vasodilation. Neprilysin is a major NP-degrading enzyme that degrades multiple vaso-modulatory substances. Although the inhibition of neprilysin alone is not sufficient to counterbalance RAAS activation in cardiovascular diseases (e.g., hypertension and heart failure), a combination of angiotensin receptor blocker and neprilysin inhibitor (ARNI) was highly effective in several clinical trials and may modulate the risk of atrial and ventricular arrhythmias. This review summarizes the possible link between ARNI and cardiac arrhythmias and discusses potential underlying mechanisms, providing novel insights about the therapeutic role and safety profile of ARNI in the cardiovascular system.     

Effect of the annealing procedure and the molecular weight on the crystalline phase morphology and thermal properties of polylactide

The thermomechanical properties of poly(lactide) (PLA) are strongly related to its semicrystalline microstructure and morphology. Thermal annealing is a strategy to improve the crystallinity of PLA. However, the different techniques and specimen types needed for each kind of characterization could lead to misleading conclusions. In this work, annealed samples of three PLA grades with different molecular weights were studied by DSC, wide angle X‐ray scattering and polarized optical microscopy (POM) and the results are related to their thermomechanical and impact properties. Special focus is put on the POM results obtained by different approaches and the suitability of each of them to be related to the thermomechanical properties. By annealing medium molecular weight PLA specimens at 140 °C an important increase of the heat distortion temperature was obtained, which was not related to the spherulite size but to the combination of high crystallinity degree together with high α/α′ crystal type ratio. However, the impact properties of annealed PLA decreased with increase in the annealing temperature according to an increment in crystallinity and in the α/α′ crystal ratio. © 2019 Society of Chemical Industry     

Effect of Hanger Flexibility on Dynamic Response of Suspension Bridges

Linearized continuum models of a suspended span with unloaded backstays and of a symmetric three-span suspension bridge are used to study the effects of the flexibility of the hangers on the vertical vibrations of suspension bridges. The models include elastic parabolic cables, flexible distributed hangers with variable length, and a stiffening girder represented by an elastic beam. It is shown that the free vibrations of a suspended span with unloaded backstays are controlled by five dimensionless parameters, while six dimensionless parameters control the response of a symmetric three-span suspension bridge. The results indicate that the flexibility of the hangers has a significant effect on the natural frequencies of the higher modes only when the relative stiffness of the girder is very high. The effects of hanger flexibility on the response of a suspension bridge to localized impulsive loads are also found to be small. These findings confirm the traditional, albeit previously untested, assumption of inextensible hangers. Finally, the threshold amplitudes of free vibrations that would result in the incipient slackening of the hangers are determined.     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.     

Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.     

Generating operation specifications from UML class diagrams: A model transformation approach

One of the more tedious and complex tasks during the specification of conceptual schemas (CSs) is modeling the operations that define the system behavior. This paper aims to simplify this task by providing a method that automatically generates a set of basic operations that complement the static aspects of the CS and suffice to perform all typical life-cycle create/update/delete changes on the population of the elements of the CS. Our method guarantees that the generated operations are executable, i.e. their executions produce a consistent state wrt the most typical structural constraints that can be defined in CSs (e.g. multiplicity constraints). In particular, our method takes as input a CS expressed as a Unified Modeling Language (UML) class diagram (optionally defined using a profile to enrich the specification of associations) and generates an extended version of the CS that includes all necessary operations to start operating the system. If desired, these basic operations can be later used as building blocks for creating more complex ones. We show the formalization and implementation of our method by means of model-to-model transformations. Our approach is particularly relevant in the context of Model Driven Development approaches.     

Approximation of a Variable Density Cloud of Points by Shrinking a Discrete Membrane

This paper describes a method to obtain a closed surface that approximates a general 3D data point set with nonuniform density. Aside from the positions of the initial data points, no other information is used. Particularly, neither the topological relations between the points nor the normal to the surface at the data points are needed. The reconstructed surface does not exactly interpolate the initial data points, but approximates them with a bounded maximum distance. The method allows one to reconstruct closed surfaces with arbitrary genus and closed surfaces with disconnected shells.     

On the measurement of privacy as an attacker’s estimation error

A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy-enhancing technologies. Most of these metrics are specific to concrete systems and adversarial models and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist system designers in selecting the most appropriate metric for a given application. In this work, we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability, and Bayes decision.