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.     

Adenosine Signaling in Mast Cells and Allergic Diseases

Adenosine is a nucleoside involved in the pathogenesis of allergic diseases. Its effects are mediated through its binding to G protein-coupled receptors: A1, A2a, A2b and A3. The receptors differ in the type of G protein they recruit, in the effect on adenylyl cyclase (AC) activity and the downstream signaling pathway triggered. Adenosine can produce both an enhancement and an inhibition of mast cell degranulation, indicating that adenosine effects on these receptors is controversial and remains to be clarified. Depending on the study model, A1, A2b, and A3 receptors have shown anti- or pro-inflammatory activity. However, most studies reported an anti-inflammatory activity of A2a receptor. The precise knowledge of the adenosine mechanism of action may allow to develop more efficient therapies for allergic diseases by using selective agonist and antagonist against specific receptor subtypes.     

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     

An ensemble-based method for linear feature extraction for two-class problems

In this paper we propose three variants of a linear feature extraction technique based on Adaboost for two-class classification problems. Unlike other feature extraction techniques, we do not make any assumptions about the distribution of the data. At each boosting step we select from a pool of linear projections the one that minimizes the weighted error. We propose three different variants of the feature extraction algorithm, depending on the way the pool of individual projections is constructed. Using nine real and two artificial data sets of different original dimensionality and sample size we compare the performance of the three proposed techniques with three classical techniques for linear feature extraction: Fisher linear discriminant analysis (FLD), Nonparametric discriminant analysis (NDA) and a recently proposed feature extraction method for heteroscedastic data based on the Chernoff criterion. Our results show that for data sets of relatively low-original dimensionality FLD appears to be both the most accurate and the most economical feature extraction method (giving just one-dimension in the case of two classes). The techniques based on Adaboost fare better than the classical techniques for data sets of large original dimensionality.

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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.     

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.     

Evolutionary Multiobjective Optimization algorithm for multimedia delivery in critical applications through Content-Aware Networks

Critical applications which need to deliver multimedia through the Internet, may achieve the required quality of service thanks to the Content-Aware Networks (CAN). The key element of CAN is an efficient decision algorithm responsible for the selection of the best content source and routing paths for content delivery. This paper proposes a two-phase decision algorithm, exploiting the Evolutionary Multiobjective Optimization (EMO) approach. It allows to consider valid information in different time scales, adapting decision-maker to the evolving network and server conditions as well as to get the optimal solution in different shapes of Pareto front. The simulation experiments performed in a large-scale network model, confirm the effectiveness of the proposed two-phase EMO algorithm, comparing to other multi-criteria decision algorithms used in CAN.     

Pattern codification strategies in structured light systems

Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and definitive classification of patterns for structured light sensors. We have implemented a set of representative techniques in this field and present some comparative results. The advantages and constraints of the different patterns are also discussed.