Análisis de las sociedades gestoras de fondo de inversión en España

En el presente artículo se realiza un análisis del sector de las Sociedades Gestoras de Instituciones de Inversión Colectiva en España, no desde el punto de vista del inversor, sino desde la perspectiva de los propios gestores de las instituciones. Se lleva a cabo un análisis sectorial basado en su evolución histórica durante la década de los 90, y se estudian los extraordinarios niveles de rentabilidad que el negocio parece ofrecer, estableciendo matizaciones al respecto. Se realiza también un análisis de las características determinantes de las relaciones entre las sociedades gestoras y sus correspondientes empresas matrices, tanto en lo que se refiere al funcionamiento interno como a los intereses en las empresas participadas por sus fondos.In this paper an analysis o f the Mutual Funds Management sector in Spain is done, not from the point of view of the final investor, but from the managers one. The historical evolution of this sector during the 90’s is resumed, and the uncommon rentability rates are analized, offering a wide range of refinements about this topic. Also, we analize the main characteristics of the relationship between the managers and the main companies, according to the rules of the internal performance and the interest of the funds in the companies wich are being invested

Frontiers in Non-invasive Cardiac Mapping: Rotors in Atrial Fibrillation-Body Surface Frequency-Phase Mapping

[EN] Experimental and clinical data demonstrate that atrial fibrillation (AF) maintenance in animals and groups of patients depends on localized reentrant sources localized primarily to the pulmonary veins (PVs) and the left atrium(LA) posterior wall in paroxysmal AF but elsewhere, including the right atrium (RA), in persistent AF. Moreover, AF can be eliminated by directly ablating AFdriving sources or “rotors,” that exhibit high-frequency, periodic activity. The RADAR-AF randomized trial demonstrated that an ablation procedure based on a more target-specific strategy aimed at eliminating high frequency sites responsible for AF maintenance is as efficacious as and safer than empirically isolating all the PVs. In contrast to the standard ECG, global atrial noninvasive frequency analysis allows non-invasive identification of high-frequency sources before the arrival at the electrophysiology laboratory for ablation. Body surface potential map (BSPM) replicates the endocardial distribution of DFs with localization of the highest DF (HDF) and can identify small areas containing the high-frequency sources. Overall, BSPM had a sensitivity of 75% and specificity of 100% for capturing intracardiac EGMs as having LARA DF gradient. However, raw BSPM data analysis of AF patterns of activity showed incomplete and instable reentrant patterns of activation. Thus, we developed an analysis approach whereby a narrow band-pass filtering allowed selecting the electrical activity projected on the torso at the HDF, which stabilized the projection of rotors that potentially drive AF on the surface. Consequently, driving reentrant patterns (“rotors”) with spatiotemporal stability during >70% of the AF time could be observed noninvasibly after HDFfiltering. Moreover, computer simulations found that the combination of BSPM phase mapping with DF analysis enabled the discrimination of true rotational patterns even during the most complex AF. Altogether, these studies show that the combination of DF analysis with phase maps of HDF-filtered surface ECG recordings allows noninvasive localization of atrial reentries during AF and further a physiologically-based rationale for personalized diagnosis and treatment of patients with AF.Study supported in part by the Spanish Society of Cardiology (Becas Investigacio´ n Clı´nica 2009); the Universitat Polite` cnica de Vale`ncia through its research initiative program; the Generalitat Valenciana Grants (ACIF/2013/021); the Ministerio de Economia y Competividad, Red RIC; the Centro Nacional de Investigaciones Cardiovasculares (proyecto CNIC-13); the Coulter Foundation from the Biomedical Engineering Department (University of Michigan); the Gelman Award from the Cardiovascular Division (University of Michigan); the National Heart, Lung, and Blood Institute grants (P01-HL039707, P01-HL087226 and R01-HL118304), and the Leducq FoundationAtienza, F.; Climent, A.; Guillem Sánchez, MS.; Berenfeld, O. (2015). Frontiers in Non-invasive Cardiac Mapping: Rotors in Atrial Fibrillation-Body Surface Frequency-Phase Mapping. Cardiac Electrophysiology Clinics. 7(1):59-69. https://doi.org/10.1016/j.ccep.2014.11.002S59697

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase remains a challenge, particularly for biomedical wearable systems. Traditional training algorithms might not be suitable for these applications due to the substantial memory requirements and high computational costs associated with processing the large number of bits involved in neural network operations. In this paper, we introduce a novel learning procedure specifically designed for low-power wearable systems, dubbed Bio-BPfree (deep neural network training without backpropagation for low-power wearable systems). Using a two-class classification task, Bio-BPfree replaces conventional forward and backward backpropagation passes with four forward passes, two for data of the positive class and two for data of the negative class. Each layer is equipped with a unique objective function aimed at minimizing the distance between data points within the same class while maximizing the distance between data points from different classes. Our experimental results, which were obtained by conducting rigorous evaluations on the MIT-BIH dataset that features electrocardiogram (ECG) signals, effectively demonstrate the superior performance and suitability of Bio-BPfree for two-class classification tasks, particularly within the challenging environment of low-power wearable systems designed for continuous health monitoring and assessment.RYC2021-032853-

Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study

Background: Ablation is an effective therapy in atrial fibrillation (AF) patients in which an electrical driver can be identified. Objective: The aim of this study is to present and discuss a novel and strictly non-invasive approach to map and identify atrial regions responsible for AF perpetuation. Methods: Surface potential recordings of 14 patients with AF were recorded using a 67-lead recording system. Singularity points (SPs) were identified in surface phase maps after band-pass filtering at the highest dominant frequency (HDF). Mathematical models of combined atria and torso were constructed and used to investigate the ability of surface phase maps to estimate rotor activity in the atrial wall. Results: The simulations show that surface SPs originate at atrial SPs, but not all atrial SPs are reflected at the surface. Stable SPs were found in AF signals during 8.3±5.7% vs. 73.1±16.8% of the time in unfiltered vs. HDF-filtered patient data respectively (p<0.01). The average duration of each rotational pattern was also lower in unfiltered than in HDF-filtered AF signals (160±43 vs. 342±138 ms, p<0.01) resulting in 2.8±0.7 rotations per rotor. Band-pass filtering reduced the apparent meandering of surface HDF rotors by reducing the effect of the atrial electrical activity taking place at different frequencies. Torso surface SPs representing HDF rotors during AF were reflected at specific areas corresponding to the fastest atrial location. Conclusion: Phase analysis of surface potential signals after HDF-filtering during AF shows reentrant drivers localized to either the LA or RA, helping in localizing ablation targetsThis work was supported in part by the Spanish Society of Cardiology (Becas Investigacion Clinica 2009); the Universitat Politecnica de Valencia through its research initiative program; the Generalitat Valenciana grant (ACIF/2013/021); the Ministerio de Economia y Competitividad, Rod RIC; the Centro Nacional de Investigaciones Cardiovasculares (proyecto CNIC-13); the Coulter Foundation from the Biomedical Engineering Department, University of Michigan; the Gelman Award from the Cardiovascular Division, University of Michigan; the National Heart, Lung, and Blood Institute grants (P01411.039707, P01-1111187226, and R01-11L118304); and the Leducq Foundation. Dr Femandez-Aviles served on the advisory board of Medtronic and has received research funding from St Jude Medical Spain. Dr Berenfeld has received research support from Medtronic and St Jude Medical; he is a colbunder and scientific officer of Rhythm Solutions. None of the companies disclosed financed the research described in this article.Rodrigo Bort, M.; Guillem Sánchez, MS.; Climent, AM.; Pedrón Torrecilla, J.; Liberos Mascarell, A.; Millet Roig, J.; Fernandez-Aviles, F.... (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm. 11(9):1584-1591. https://doi.org/10.1016/j.hrthm.2014.05.013S1584159111

Método nuevo facil, breve y curioso de Arismética teórica y práctica : con el que cualquiera podrá sin maestro aprender á contar...

Sign.: B-

Electrophysiological characteristics of permanent atrial fibrillation: insights from research models of cardiac remodeling

[EN] Atrial fibrillation (AF) results in a remodeling of the electrical and structural characteristics of the cardiac tissue which dramatically reduces the efficacy of pharmacological and catheter-based ablation therapies. Recent experimental and clinical results have demonstrated that the complexity of the fibrillatory process significantly differs in paroxysmal versus persistent AF; however, the lack of appropriate research models of remodeled atrial tissue precludes the elucidation of the underlying AF mechanisms and the identification of appropriated therapeutic targets. Here, we summarize the different research models used to date, highlighting the lessons learned from them and pointing to the new doors that should be open for the development of innovative treatments for AF.The authors were supported by grants from the Spanish Ministry of Science and Innovation (PLE2009-0152), the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-01882 and PI13-00903) the Red de Investigacion Cardiovacular (RIC) from Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain). F Atienza served on the advisory board of Medtronic and has received research funding from St. Jude Medical Spain. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Climent, A.; Guillem Sánchez, MS.; Atienza Fernández, F.; Fernandez-Aviles, F. (2014). Electrophysiological characteristics of permanent atrial fibrillation: insights from research models of cardiac remodeling. Expert Review of Cardiovascular Therapy. 13(1):1-3. https://doi.org/10.1586/14779072.2015.986465S1313