Impaired cardiac and skeletal muscle bioenergetics in children, adolescents, and young adults with Barth syndrome

Barth syndrome (BTHS) is an X‐linked condition characterized by altered cardiolipin metabolism and cardioskeletal myopathy. We sought to compare cardiac and skeletal muscle bioenergetics in children, adolescents, and young adults with BTHS and unaffected controls and examine their relationships with cardiac function and exercise capacity. Children/adolescents and young adults with BTHS (n = 20) and children/adolescent and young adult control participants (n = 23, total n = 43) underwent (31)P magnetic resonance spectroscopy ((31)P‐MRS) of the lower extremity (calf) and heart for estimation of skeletal muscle and cardiac bioenergetics. Peak exercise testing (VO (2peak)) and resting echocardiography were also performed on all participants. Cardiac PCr/ATP ratio was significantly lower in children/adolescents (BTHS: 1.5 ± 0.2 vs. Control: 2.0 ± 0.3, P < 0.01) and adults (BTHS: 1.9 ± 0.2 vs. Control: 2.3 ± 0.2, P < 0.01) with BTHS compared to Control groups. Adults (BTHS: 76.4 ± 31.6 vs. Control: 35.0 ± 7.4 sec, P < 0.01) and children/adolescents (BTHS: 71.5 ± 21.3 vs. Control: 31.4 ± 7.4 sec, P < 0.01) with BTHS had significantly longer calf PCr recovery (τ PCr) postexercise compared to controls. Maximal calf ATP production through oxidative phosphorylation (Qmax‐lin) was significantly lower in children/adolescents (BTHS: 0.5 ± 0.1 vs. Control: 1.1 ± 0.3 mmol/L per sec, P < 0.01) and adults (BTHS: 0.5 ± 0.2 vs. Control: 1.0 ± 0.2 mmol/L sec, P < 0.01) with BTHS compared to controls. Blunted cardiac and skeletal muscle bioenergetics were associated with lower VO(2peak) but not resting cardiac function. Cardiac and skeletal muscle bioenergetics are impaired and appear to contribute to exercise intolerance in BTHS

IDEALIST control and service management solutions for dynamic and adaptive flexi-grid DWDM networks

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Muñoz, V. López, R. Casellas, O. González de Dios, F. Cugini, N. Sambo, A. d'Errico, O. Gerstel, D. King, S. López-Buedo, P. Layec, A. Cimmino, R. Martínez, and R. Moro, "IDEALIST control and service management solutions for dynamic and adaptive flexi-grid DWDM networks", in Future Network and Mobile Summit, 2013, pp. 1-10Wavelength Switched Optical Networks (WSON) were designed with the premise that all channels in a network have the same spectrum needs, based on the ITU-T DWDM grid. However, this rigid grid-based approach is not adapted to the spectrum requirements of the signals that are best candidates for long-reach transmission and high-speed data rates of 400Gbps and beyond. An innovative approach is to evolve the fixed DWDM grid to a flexible grid, in which the optical spectrum is partitioned into fixed-sized spectrum slices. This allows facilitating the required amount of optical bandwidth and spectrum for an elastic optical connection to be dynamically and adaptively allocated by assigning the necessary number of slices of spectrum. The ICT IDEALIST project will provide the architectural design, protocol specification, implementation, evaluation and standardization of a control plane and a network and service management system. This architecture and tools are necessary to introduce dynamicity, elasticity and adaptation in flexi-grid DWDM networks. This paper provides an overview of the objectives, framework, functional requirements and use cases of the elastic control plane and the adaptive network and service management system targeted in the ICT IDEALIST project.This work was partially funded by the European Community’s Seventh Framework Programme FP7/2007-2013 through the Integrated Project (IP) IDEALIST under grant agreement nº 317999

Self-learning approaches for real optical networks

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Self-learning approaches to facilitate the deployment of ML algorithms in real networks are analyzed and their performance evaluated through an illustrative use case. Results show large benefits of collective self-learning with centralized retraining.Peer ReviewedPostprint (author's final draft

Power-consumption analysis for different IPoWDM network architectures with ZR/ZR+ and long-haul muxponders

Operators are constantly faced with the need to increase optical-network capacity to accommodate rapid traffic growth while minimizing the cost-per-bit and power-per-bit. The drastic reduction of the power consumption of IP routers and ZR/ZR+ pluggable transponders seen in the past several years has renewed the interest inopaque optical-network architectures, where no optical bypassing is allowed. In this work, we aim to quantify and compare the power consumption of four IP over wavelength division multiplexingâ? (IPoWDM) transport network architectures employing ZR/ZR+ modules versus long-haul muxponders, considering different grooming, regeneration, and optical bypassing capabilities. We first propose a power consumption model for different IPoWDM node architectures with ZR/ZR+ modules and long-haul muxponders. Then, to obtain the power consumption of different architectures, we propose a compact auxiliary-graph-based network-design algorithm extensible to different network architectures. Moreover, we investigate how the continuous decrease in the power consumption of ZR/ZR+ and IP routers can impact the power consumption of different architectures through a sensitivity analysis. Illustrative numerical results on networks of different sizes show that, despite drastic reductions of power consumption at the IP layer, optical bypassing is still the most power-efficient solution, reducing consumption by up to 48%

Learning life cycle to speed up autonomic optical transmission and networking adoption

Autonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new optical equipment and techniques are continuously being deployed in the network. One option is to generate data from simulations and lab experiments, but such data could not cover the whole features space and would translate into inaccuracies in the ML models. In this paper, we propose an ML-based algorithm life cycle to facilitate ML deployment in real operator networks. The dataset for ML training can be initially populated based on the results from simulations and lab experiments. Once ML models are generated, ML retraining can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits of the proposed learning cycle for general use cases. In addition, two specific use cases are proposed and demonstrated that implement different learning strategies: (i) a two-phase strategy performing out-of-field training using data from simulations and lab experiments with generic equipment, followed by an in-field adaptation to support heterogeneous equipment (the accuracy of this strategy is shown for a use case of failure detection and identification), and (ii) in-field retraining, where ML models are retrained after detecting model inaccuracies. Different approaches are analyzed and evaluated for a use case of autonomic transmission, where results show the significant benefits of collective learning.Peer ReviewedPostprint (published version

Predictive autonomic transmission for low-cost low-margin metro optical networks

Low-cost low-margin implementation plays an essential role in upgrading optical metro networks required for future 5G ecosystem. In this regard, low-resolution analog-to-digital converters can be used in coherent optical transponders to reduce cost and power consumption. However, the resulting transmission systems become more sensitive to physical layer fluctuations like the events caused by fiber stressing. Such fluctuations might have a strong impact on the quality of transmission (QoT) of the signals. To guarantee robust operation, soft decision forward error correction (FEC) techniques are required to guarantee zero post-FEC bit error rate (BER) transmission, which could increase the power consumption of the receiver and thus operational expenses. In this paper, we aim at minimizing power consumption while keeping zero post-FEC errors by means of a predictive autonomic transmission agent (ATA) based on machine learning. We present a sophisticated ATA model that, taking advantage of real-time monitoring of state of polarization traces and the corresponding pre-FEC BER, predicts the right FEC configuration for short-term operation, thus requiring minimum power consumption. In addition, we propose a complementary long-term prediction of excessive pre-FEC BER to enable remote reconfiguration at the transmitter side through the network controller. A set of experimental measurements is used to train and validate the proposed ATA system. Exhaustive numerical analysis allows concluding that ATA based on artificial neural network predictors achieves the maximum QoT robustness with 80% power consumption reductions compared to static FEC configuration.The research leading to these results has received funding from the European Commission for the H2020-ICT-2016-2 METRO-HAUL project (G.A. 761727), from the AEI/FEDER TWINS project (TEC2017-90097-R), and from the Catalan Institution for Research and Advanced Studies (ICREA).Peer ReviewedPostprint (author's final draft

Effects of passive and active leg movements to interrupt sitting in mild hypercapnia on cardiovascular function in healthy adults

Prolonged sitting in a mild hypercapnic environment impairs peripheral vascular function. The effects of sitting interruptions using passive or active skeletal muscle contractions are still unclear. Therefore, we sought to examine the vascular effects of brief periods (2 min every half hour) of passive and active lower limb movement to interrupt prolonged sitting with mild hypercapnia in adults. Fourteen healthy adults (24 ± 2 yr) participated in three experimental visits sitting for 2.5 h in a mild hypercapnic environment (CO2 = 1,500 ppm): control (CON, no limb movement), passive lower limb movement (PASS), and active lower limb movement (ACT) during sitting. At all visits, brachial and popliteal artery flow-mediated dilation (FMD), microvascular function, plasmatic levels of nitrate/nitrite and endothelin-1, and heart rate variability were assessed before and after sitting. Brachial and popliteal artery FMDs were reduced in CON and PASS (P \u3c 0.05) but were preserved (P \u3e 0.05) in ACT. Microvascular function was blunted in CON (P \u3c 0.05) but was preserved in PASS and ACT (P \u3e 0.05). In addition, total plasma nitrate/nitrite was preserved in ACT (P \u3e 0.05) but was reduced in CON and PASS (P \u3c 0.05), and endothelin-1 levels were decreased in ACT (P \u3c 0.05). Both passive and active movement induced a greater ratio between the low-frequency and high-frequency bands for heart rate variability (P \u3c 0.05). For the first time, to our knowledge, we found that brief periods of passive leg movement can preserve microvascular function, but that an intervention that elicits larger increases in shear rate, such as low-intensity exercise, is required to fully protect both macrovascular and microvascular function and circulating vasoactive substance balance