Incidence of prosthesis-patient mismatch in patients receiving mitral Biocor® porcine prosthetic valves

Background: The aim was to assess the incidence of prosthesis-patient mismatch (PPM) after mitral valve replacement (MVR) in patients receiving Biocor® porcine or mechanical valves, and to evaluate the effect of PPM on long-term survival. Methods: All patients undergoing MVR between 2009 and 2013 received either mechanical or bioprosthetic valves (Biocor® porcine). PPM was defined as severe when the indexed effective ori­fice area was < 0.9 cm2/m2, moderate between 0.9 cm2/m2 and 1.2 cm2/m2 or absent > 1.2 cm2/m2. The primary endpoint was all-cause long-term mortality. Results: Among a total of 136 MVR, PPM was severe in 27%, moderate in 44% and absent in 29% of patients. Implanted valves were 57% mechanical and 43% bioprosthetic. Only 3% of patients with mechanical valves had severe PPM vs. 59% with bioprostheses (p < 0.0001). Sixty-month survival with severe mismatch was 0.559 (SE 0.149) and with no mismatch 0.895 (SE 0.058) (p = 0.043). Survival of patients suffering from severe mismatch, or moderate mismatch with pulmonary hypertension (PH) was 0.749 (SE 0.101); while for patients with no mismatch or with moderate mismatch without PH, survival was 0.951 (SE 0.028) (p = 0.016). Conclusions: About one-fourth of patients had severe PPM and almost all of them had received a bioprosthesis. Sixty-month survival was significantly lower in patients with severe mismatch, or moderate mismatch with PH. Specifically, when a bioprothesis is chosen and while further evidence on the impact of PPM on clinical outcomes appears, surgeons are recommended to follow a preoperative strategy to implant a mitral prosthesis of adequate size in order to prevent PP

The history of a quiet-Sun magnetic element revealed by IMaX/SUNRISE

Isolated flux tubes are considered to be fundamental magnetic building blocks of the solar photosphere. Their formation is usually attributed to the concentration of magnetic field to kG strengths by the convective collapse mechanism. However, the small size of the magnetic elements in quiet-Sun areas has prevented this scenario from being studied in fully resolved structures. Here we report on the formation and subsequent evolution of one such photospheric magnetic flux tube, observed in the quiet Sun with unprecedented spatial resolution (0\farcs 15 - 0\farcs 18) and high temporal cadence (33 s). The observations were acquired by the Imaging Magnetograph Experiment (IMaX) aboard the \textsc{Sunrise} balloon-borne solar observatory. The equipartition field strength magnetic element is the result of the merging of several same polarity magnetic flux patches, including a footpoint of a previously emerged loop. The magnetic structure is then further intensified to kG field strengths by convective collapse. The fine structure found within the flux concentration reveals that the scenario is more complex than can be described by a thin flux tube model with bright points and downflow plumes being established near the edges of the kG magnetic feature. We also observe a daisy-like alignment of surrounding granules and a long-lived inflow towards the magnetic feature. After a subsequent weakening process, the field is again intensified to kG strengths. The area of the magnetic feature is seen to change in anti-phase with the field strength, while the brightness of the bright points and the speed of the downflows varies in phase. We also find a relation between the brightness of the bright point and the presence of upflows within it.Comment: 13 pages. Accepted in ApJ. Animation 1 can be viewed and downloaded from: http://spg.iaa.es/downloads.as

Promoción turística sostenible: i-Naturhouse

La eficiencia energética y el aprovechamiento de las fuentes de energía naturales se han convertido en uno de los principales temas de investigación de Universidades y Gobiernos. Por ello, no es nada nuevo la presentación de un proyecto basado en una construcción que aprovecha los recursos naturales para la generación y posterior autoconsumo o venta de electricidad a la red. Nuestro proyecto es mucho más ambicioso y creemos que puede tener un fuerte impacto en un mercado global al utilizar todas las nuevas tecnologías existentes en campos como el bioclimatismo, energía solar, energía eólica, microcogeneración, telecomunicaciones, automatización, freecooling, iluminación y eficiencia energética. Deja de ser por tanto una solución parcial que aprovecha únicamente el conocimiento en algunos ámbitos de la ingeniería, para dar paso a una solución que se desarrolla en los campos de la Ingeniería Industrial en general (Mecánica, Energética, Eléctrica…etc.) las Telecomunicaciones, así como en el área de Economía y Finanzas. Nuestro proyecto aunque lo localizamos en la ciudad de Lleida, es un proyecto de alcance e impacto global, y en él analizamos en detalle cada uno de los puntos principales que intervendrían en este nuevo modelo de sensibilización de la sociedad hacia el consumo energético sostenible.El proyecto i‐Naturhouse es un trabajo de combinación de diferentes tecnologías con un fin común y en el cual éstas se combinan de forma equilibrada ofreciendo a la sociedad una herramienta que con un gran potencial, si tiene el alcance que se le proyecta. El proyecto ha sido dividido en 9 apartados. El primero es de introducción y contextualización para pasar a analizar posteriormente un caso concreto que ubicaríamos en la población de Lleida

SoK: Security of Programmable Logic Controllers

Billions of people rely on essential utility and manufacturing infrastructures such as water treatment plants, energy management, and food production. Our dependence on reliable infrastructures makes them valuable targets for cyberattacks. One of the prime targets for adversaries attacking physical infrastructures are Programmable Logic Controllers (PLCs) because they connect the cyber and physical worlds. In this study, we conduct the first comprehensive systematization of knowledge that explores the security of PLCs: We present an in-depth analysis of PLC attacks and defenses and discover trends in the security of PLCs from the last 17 years of research. We introduce a novel threat taxonomy for PLCs and Industrial Control Systems (ICS). Finally, we identify and point out research gaps that, if left ignored, could lead to new catastrophic attacks against critical infrastructures.Comment: 25 pages, 13 figures, Extended version February 2024, A shortened version is to be published in the 33rd USENIX Security Symposium, for more information, see https://efrenlopez.org

Influence of the Improvement in Thermal Expectation Levels with Adaptive Setpoint Temperatures on Energy Consumption

A sustainable use of active heating, ventilation, and air conditioning (HVAC) systems is crucial for minimum energy consumption. Currently, research studies are increasingly applying adaptive setpoint temperatures, thus reducing considerably the energy consumption without influencing comfort levels excessively. Most of them, however, are focused on the limit values of adaptive comfort standards without considering the tolerance in users’ adaptation capacity. This research study analyzed various tolerance ranges in the recent adaptive thermal comfort model from EN 16798-1:2019 used in setpoint temperatures. The study focused on the south of Europe, considering 47 cities in Spain, 18 cities in Portugal, 13 cities in Greece, and 20 cities in Italy. In addition, such cities were analyzed in three climate scenarios: present time, 2050, and 2100. The results showed that values prefixed by EN 16798-1:2019 for new buildings (tolerance of 0.00 °C) produced significant savings with respect to the static model and that each progressive improvement in users’ thermal expectations in 0.25 °C increased the energy consumption between 6.57 and 9.31% in all scenarios analyzed. Even applying a thermal tolerance of 1.50 °C, energy savings are currently produced with respect to the static model. This tendency increases in future scenarios until a thermal tolerance of 1.75 °C. The results of this paper provide greater knowledge about the possible energy increase that the improvement in users’ expectations would produc

Is selective 5-HT1F receptor agonism an entity apart from that of the triptans in antimigraine therapy?

Migraine is a neurovascular disorder that involves activation of the trigeminovascular system and cranial vasodilation mediated by release of calcitonin gene-related peptide (CGRP).The gold standard for acute migraine treatment are the triptans, 5-HT1B/1D/(1F) receptor agonists. Their actions are thought to be mediated through activation of: (i) 5-HT1B receptors in cranial blood vessels with subsequent cranial vasoconstriction; (ii) prejunctional 5-HT1D receptors on trigeminal fibers that inhibit trigeminal CGRP release; and (iii) 5-HT1B/1D/1F receptors in central nervous system involved in (anti)nociceptive modulation. Unfortunately, coronary arteries also express 5-HT1B receptors whose activation would produce coronary vasoconstriction; hence, triptans are contraindicated in patients with cardiovascular disease. In addition, since migraineurs have an increased cardiovascular risk, it is important to develop antimigraine drugs devoid of vascular (side) effects.Ditans, here defined as selective 5-HT1F receptor agonists, were developed on the basis that most of the triptans activate trigeminal 5-HT1F receptors, which may explain part of the triptans' antimigraine action. Amongst the ditans, lasmiditan: (i) fails to constrict human coronary arteries; and (ii) is effective for the acute treatment of migraine in preliminary Phase III clinical trials. Admittedly, the exact site of action is still unknown, but lasmiditan possess a high lipophilicity, which suggests a direct action on the central descending antinociceptive pathways. Furthermore, since 5-HT1F receptors are located on trigeminal fibers, they could modulate CGRP release.This review will be particularly focussed on the similarities and differences between the triptans and the ditans, their proposed sites of action, side effects and their cardiovascular risk profile

Electronic Descriptors for Supervised Spectroscopic Predictions

Spectroscopic properties of molecules holds great importance for the description of the molecular response under the effect of an UV/Vis electromagnetic radiation. Computationally expensive ab initio (e.g. MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work, we propose a (supervised) Machine Learning approach to model the absorption spectra of organic molecules. Several supervised ML methods have been tested such as Kernel Ridge Regression (KRR), Multiperceptron Neural Networs (MLP) and Convolutional Neural Networks. The use of only geometrical descriptors (e.g. Coulomb Matrix) proved to be insufficient for an accurate training. Inspired on the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences, transition dipole moment between occupied and unoccupied Kohn-Sham orbitals and charge-transfer character of mono-excitations. We demonstrate that with this electronic descriptors and the use of Neural Networks we can predict not only a density of excited states, but also getting very good estimation of the absorption spectrum and charge-transfer character of the electronic excited states, reaching results close to the chemical accuracy (~2 kcal/mol or ~0.1eV)

Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings

In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance