Sistema de comisiones de las Administradoras de Fondos de Pensiones en Colombia.

El actual sistema de comisiones en Colombia no genera los incentivos necesarios para que las Administradoras de Fondos de Pensiones (AFP) incrementen la rentabilidad de los fondos de pensiones. Dado que la mesada pensional futura de los afiliados depende principalmente de la capitalización de los aportes, es necesario generar un esquema en el cual las AFP estén incentivadas a incrementar los rendimientos de los fondos. Por medio de un modelo en que se calcula el saldo de capital de un afiliado, en este documento se muestran las ventajas de un esquema de comisiones por rendimientos, y cómo este esquema puede ser combinado con un esquema de comisiones sobre aportes como el actual. Finalmente se señalan algunos aspectos a tener en cuenta a la hora de implementar un esquema en esta dirección.

New RED-type TCP-AQM algorithms based on beta distribution drop functions

In recent years, Active Queue Management (AQM) mechanisms to improve the performance of TCP/IP networks have acquired a relevant role. In this paper we present a simple and robust RED-type algorithm together with a couple of dynamical variants with the ability to adapt to the specific characteristics of different network environments, as well as to the user needs. We first present a basic version called Beta RED (BetaRED), where the user is free to adjust the parameters according to the network conditions. The aim is to make the parameter setting easy and intuitive so that a good performance is obtained over a wide range of parameters. Secondly, BetaRED is used as a framework to design two dynamic algorithms, which we will call Adaptive Beta RED (ABetaRED) and Dynamic Beta RED (DBetaRED). In those new algorithms certain parameters are dynamically adjusted so that the queue length remains stable around a predetermined reference value and according to changing network traffic conditions. Finally, we present a battery of simulations using the Network Simulator 3 (ns-3) software with a two-fold objective: to guide the user on how to adjust the parameters of the BetaRED mechanism, and to show a performance comparison of ABetaRED and DBetaRED with other representative algorithms that pursue a similar objective

Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing

The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played. We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.Spanish Government PGC2018-098813-BC32 PGC2018-098813-B-C31Junta de Andalucia UMA20-FEDERJA-086 P18-RT-1624European CommissionBioSiP research group TIC-251MCIN/AEI by "ESF Investing in your future" PRE2019-087350 MICINN "Juan de la Cierva -Incorporacion" FellowshipLeeduca research groupJunta de Andalucia Spanish Governmen

Enhancing Neuronal Coupling Estimation by NIRS/EEG Integration.

Neuroimaging techniques have had a major impact on medical science, allowing advances in the research of many neurological diseases and improving their diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the functional activity of the brain processes related to low-level language processing of skilled and dyslexic seven-year-old readers. We have transformed EEG signals into image sequences considering the interaction between different frequency bands by means of cross-frequency coupling (CFC), and applied an activation mask sequence obtained from the local functional brain activity inferred from simultaneously recorded fNIRS signals. Thus, the resulting image sequences preserve spatial and temporal information of the communication and interaction between different neural processes and provide discriminative information that enables differentiation between controls and dyslexic subjects.This research is part of the PID2022-137461NB-C32, PID2022-137629OA-I00 and PID2022-137451OB-I00 projects, funded by the MCIN/AEI/10.13039/501100011033, by FSE+, UMA20-FEDERJA-086 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF), as well as the BioSiP (TIC-251) research group and University of Málaga (UMA)-Campus of International Excellence Andalucía Tech. Marco A. Formoso grant PRE2019-087350 funded by MCIN/AEI/ 10.13039/501100011033 by “ESF Investing in your future”

Optimized One vs One approach in multiclass classification for early Alzheimer’s Disease and Mild Cognitive Impairment diagnosis

The detection of Alzheimer’s Disease in its early stages is crucial for patient care and drugs development. Motivated by this fact, the neuroimaging community has extensively applied machine learning techniques to the early diagnosis problem with promising results. The organization of challenges has helped the community to address different raised problems and to standardize the approaches to the problem. In this work we use the data from international challenge for automated prediction of MCI from MRI data to address the multiclass classification problem. We propose a novel multiclass classification approach that addresses the outlier detection problem, uses pairwise t-test feature selection, project the selected features onto a Partial-Least-Squares multiclass subspace, and applies one-versus-one error correction output codes classification. The proposed method yields to an accuracy of 67 % in the multiclass classification, outperforming all the proposals of the competition.Ministerio de Innovacion y Ciencia Project DEEP-NEUROMAPS RTI2018-098913-B100Consejeria de Economia, Innovacion, Ciencia, y Empleo of the Junta de Andalucia A-TIC-080-UGR18 TIC FRONTERAGerman Research Foundation (DFG) FPU 18/04902United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Neurological Disorders & Stroke (NINDS) U01 AG024904DOD ADNI Department of Defense W81XWH-12-2-001

Mammalian Adaptation of an Avian Influenza A Virus Involves Stepwise Changes in NS1

Influenza A viruses (IAVs) are common pathogens of birds that occasionally establish endemic infections in mammals. The processes and mechanisms that result in IAV mammalian adaptation are poorly understood. The viral non-structural 1 (NS1) protein counteracts the interferon (IFN) response, a central component of the host-species barrier. We characterised the NS1 proteins of equine influenza virus (EIV), a mammalian IAV lineage of avian origin. We showed that evolutionary distinct NS1s counteract the IFN response using different and mutually exclusive mechanisms: while the NS1s of early EIVs block general gene expression by binding to the cellular polyadenylation specific factor 30 (CPSF30), NS1s from more evolved EIVs specifically block the induction of IFN-stimulated genes by interfering with the JAK/STAT pathway. These contrasting anti-IFN strategies are associated with two mutations that appeared sequentially and became rapidly selected during EIV evolution, highlighting the importance of evolutionary processes on immune evasion mechanisms during IAV adaptation

Diagnostic yield of early repeat colonoscopy after suboptimal bowel preparation in a fecal immunochemical test-based screening program

Background Current guidelines regarding surveillance after screening colonoscopy assume adequate bowel preparation. However, follow-up intervals after suboptimal cleansing are highly heterogeneous. We aimed to determine the diagnostic yield of early repeat colonoscopy in patients with suboptimal bowel preparation in fecal immunochemical test (FIT)-based screening colonoscopy. Methods An observational study including patients who underwent colonoscopy with suboptimal bowel preparation after positive FIT screening and then repeat colonoscopy within 1 year. Suboptimal preparation was defined as a Boston Bowel Preparation Scale (BBPS) score of 1 in any segment. Patients with a BBPS score of 0 in any segment or incomplete examination were excluded. The adenoma detection rate (ADR), advanced ADR (AADR), and colorectal cancer rate were calculated for the index and repeat colonoscopies. Results Of the 2474 patients with FIT-positive colonoscopy at our center during this period, 314 (12.7%) had suboptimal preparation. Of the 259 (82.5%) patients who underwent repeat colonoscopy, suboptimal cleansing persisted in 22 (9 %). On repeat colonoscopy, the ADR was 38.7% (95% CI 32.6% to 44.8%) and the AADR was 14.9% (95%CI 10.5% to 19.4%). The per-adenoma miss rate was 27.7% (95 %CI 24.0% to 31.6%), and the per-advanced adenoma miss rate was 17.6% (95%CI 13.3% to 22.7%). After repeat colonoscopy, the post-polypectomy surveillance recommendation changed from 10 to 3 years in 14.7% of the patients with previous 10-year surveillance recommendation. Conclusions Patients with suboptimal bowel preparation on FIT-positive colonoscopy present a high rate of advanced adenomas in repeat colonoscopy, with major changes in post-polypectomy surveillance recommendations