THE EFFECT OF ACUTE BEETROOT JUICE SUPPLEMENTATION ON MUSCLE FATIGUE IN KNEE EXTENSOR EXERCISE

To examine the effect of acute beetroot juice supplementation on the rate of fatigue as measured by changes in peak torque. Placebo-controlled, double-blind, cross-over study, 35 recreationally active subjects consumed beetroot (BR) juice or black currant juice (PL) 12 and 2.5 hours before the exercise procedure. Peak torque was measured on the BIODEX dynamometer by performing 50, maximal effort, concentric knee extensions at 90°/s. Blood pressure (BP) was recorded before and after exercise. No significant difference between BR and PL in the rate of fatigue measured by change in peak torque. By stage 3, subjects retained 87.6±6.9% of strength with BR and 86.7±6.3% with PL (p= 0.363). Stages 10 was as follows: BR 47.9±12.6 vs. PL 46.9±12.9% (p= 0.419). The rate of work fatigue showed no significant differences. By stage 4, mean percent work fatigue showed 20.6±9% with BR and 21.8±10.1% with PL (p= 0.224). Stage 10 was as follows: BR 52.5±12.6% vs. PL 53.2±13% (p= 0.571). Post-exercise diastolic BP (BR: 67.2±9.8 vs. PL: 64.5±7.9mmHg, p= 0.039) and MAP (BR: 91.6±9.3 vs. PL: 88.8±8.2mmHg, p= 0.011) were higher with BR supplementation. Acute bouts of beetroot juice supplementation had no significant effect on knee extensor muscle fatigue measured during isokinetic contraction

Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization

Solving for adversarial examples with projected gradient descent has been demonstrated to be highly effective in fooling the neural network based classifiers. However, in the black-box setting, the attacker is limited only to the query access to the network and solving for a successful adversarial example becomes much more difficult. To this end, recent methods aim at estimating the true gradient signal based on the input queries but at the cost of excessive queries. We propose an efficient discrete surrogate to the optimization problem which does not require estimating the gradient and consequently becomes free of the first order update hyperparameters to tune. Our experiments on Cifar-10 and ImageNet show the state of the art black-box attack performance with significant reduction in the required queries compared to a number of recently proposed methods. The source code is available at https://github.com/snu-mllab/parsimonious-blackbox-attack.Comment: Accepted and to appear at ICML 201

Improvement of Stability of a Grid-Connected Inverter with an LCL filter by Robust Strong Active Damping and Model Predictive Control

This study addresses development and implementation of robust control methods for a three-phase grid-connected voltage source inverter (VSI) accompanied by an inductive-capacitive-inductive (LCL) filter. A challenge of current control for the VSI is LCL filter resonance near to the control stability boundary, which interacts with the inverter control switching actions and creates the possibility of instability. In general, active damping is needed to stabilize the system and ensure robust performance in steady-state and dynamic responses. While many active damping methods have been proposed to resolve this issue, capacitor-current-feedback active damping has been most widely used for its simple implementation. There has been no clear consensus regarding design of a control system including capacitor-current-feedback active damping. This is due to the fact that simulation/experiment results are not congruent with the design analyses on which the control is designed. This study explains the incoherence between theory and practice when it comes to a capacitor-currents-feedback active damping system. Proposed capacitor-current-estimate active damping utilizing a developed posteriori Kalman estimator gives coherent simulation results as expected from the design analyses. This reveals that the highly oscillatory capacitor currents containing the inverter switching effects bring about uncertainty in the system performance. The switching effects are not incorporated in the analyses and control system design. Therefore, it is required to remove the switching noise from the capacitor currents in order to yield consistent results. It has been confirmed that the proportional-negative feedback of the capacitor current is equivalent to virtual impedance connected in parallel with the filter capacitor. In a digitally controlled system, the computation delay causes the equivalent resistance of the virtual impedance to become negative in the frequency range of fs/6 to fs/2, which produces a pair of open-loop unstable poles in RHP. This happens when the displaced resonance peak by active damping is in that region. Thus, an a priori Kalman estimator has been developed to generate one-sample-ahead state variable estimates to reconstruct the capacitor currents for active damping, which can compensate for the delay. The one-sample-ahead capacitor-current estimates are computed from the inverter-side and grid-side current estimates. The proposed method provides extended limits of the active damping gain that improve robustness against system parameter variation. It also allows strong active damping which can sufficiently attenuate the resonance. Grid condition is another significant factor affecting the stability of the system. In particular, a weak grid tends to provide high impedance. The system employing the proposed active damping method stably operates in a weak grid, ensuring robustness under grid impedance variation. The developed Kalman estimators offer an effective and easy way of determining the stability status of a system in addition to the functions of filtering and estimation. Stability analysis can be easily made since state variable estimates go to infinity when a system is unstable. As a promising approach, model predictive control (MPC) has been designed for the system. This study suggests that MPC including active damping can be employed for a grid-connected VSI with an LCL filter with good dynamic performance

Neurovascular Interaction and Exercise Training for Bone Regeneration

The nervous and vascular systems are widely distributed in the skeletal system and play an important role in bone metabolism and bone formation, respectively. Their independent impact on the skeletal system has received keen attention in bone-related research over the decade. However, the mechanism of neurovascular coupling during physiological bone remodeling and regeneration has recently been highlighted, considering the importance of spatial relationships between bone-associated skeletal nerves and blood vessels. In addition, the positive effect of exercise on the bones has been continuously emphasized, as evident by the improved number and function of skeletal nerves and blood vessels following exercise training. Skeletal nerve-vascular crosstalk and exercise training are essential for bone development and regeneration, respectively; however, the effect of exercise on neurovascular interactions has not yet been studied. This review aims to summarize the regulatory roles of the nerves and blood vessels in bone metabolism and regeneration and to highlight a combination of potential cellular processes of neurogenesis and angiogenesis in bone regeneration. Moreover, given the significance of the spatial relationship between nerves and blood vessels in bones and the role of exercise training, this review aims to discuss the potential physiological neurovascular coupling that occurs following exercise and physical activity

The Potential Role of Exercise Training and Mechanical Loading on Bone-Associated Skeletal Nerves

: The spatial distribution, innervation, and functional role of the bone-associated skeletal nerves have been previously reported in detail. However, studies examining exercise-induced associations between skeletal nerves and bone metabolism are limited. This review introduces a potential relationship between exercise and the skeletal nerves and discusses how it can contribute to exercise-induced bone anabolism. First, the background and current understanding of nerve fiber types and their functions in the skeleton are provided. Next, the influence of exercise and mechanical loading on the skeletal nervous system is elaborated. Effective synthesis of recent studies could serve as an established baseline for the novel discovery of the effects of exercise on skeletal nerve density and bone anabolic activity in the future. Therefore, this review overviews the existing evidence for the neural control of bone metabolism and the potential positive effects of exercise on the peripheral skeletal nervous system. The influence of exercise training models on the relationships of sensory nerve signals with osteoblast-mediated bone formation and the increased bone volume provides the first insight on the potential importance of exercise training in stimulating positive adaptations in the skeletal nerve-bone interaction and its downstream effect on bone metabolism, thereby highlighting its therapeutic potential in a variety of clinical populations

High-flow nasal cannula for respiratory failure in adult patients

The high-flow nasal cannula (HFNC) has been recently used in several clinical settings for oxygenation in adults. In particular, the advantages of HFNC compared with low-flow oxygen systems or non-invasive ventilation include enhanced comfort, increased humidification of secretions to facilitate expectoration, washout of nasopharyngeal dead space to improve the efficiency of ventilation, provision of a small positive end-inspiratory pressure effect, and fixed and rapid delivery of an accurate fraction of inspired oxygen (FiO2) by minimizing the entrainment of room air. HFNC has been successfully used in critically ill patients with several conditions, such as hypoxemic respiratory failure, hypercapneic respiratory failure (exacerbation of chronic obstructive lung disease), post-extubation respiratory failure, pre-intubation oxygenation, and others. However, the indications are not absolute, and much of the proven benefit remains subjective and physiologic. This review discusses the practical application and clinical uses of HFNC in adults, including its unique respiratory physiologic effects, device settings, and clinical indications

Fashion Style Editing with Generative Human Prior

Image editing has been a long-standing challenge in the research community with its far-reaching impact on numerous applications. Recently, text-driven methods started to deliver promising results in domains like human faces, but their applications to more complex domains have been relatively limited. In this work, we explore the task of fashion style editing, where we aim to manipulate the fashion style of human imagery using text descriptions. Specifically, we leverage a generative human prior and achieve fashion style editing by navigating its learned latent space. We first verify that the existing text-driven editing methods fall short for our problem due to their overly simplified guidance signal, and propose two directions to reinforce the guidance: textual augmentation and visual referencing. Combined with our empirical findings on the latent space structure, our Fashion Style Editing framework (FaSE) successfully projects abstract fashion concepts onto human images and introduces exciting new applications to the field.Comment: 5 page

Differentiable Display Photometric Stereo

Photometric stereo leverages variations in illumination conditions to reconstruct per-pixel surface normals. The concept of display photometric stereo, which employs a conventional monitor as an illumination source, has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper, we introduce Differentiable Display Photometric Stereo (DDPS), a method designed to achieve high-fidelity normal reconstruction using an off-the-shelf monitor and camera. DDPS addresses a critical yet often neglected challenge in photometric stereo: the optimization of display patterns for enhanced normal reconstruction. We present a differentiable framework that couples basis-illumination image formation with a photometric-stereo reconstruction method. This facilitates the learning of display patterns that leads to high-quality normal reconstruction through automatic differentiation. Addressing the synthetic-real domain gap inherent in end-to-end optimization, we propose the use of a real-world photometric-stereo training dataset composed of 3D-printed objects. Moreover, to reduce the ill-posed nature of photometric stereo, we exploit the linearly polarized light emitted from the monitor to optically separate diffuse and specular reflections in the captured images. We demonstrate that DDPS allows for learning display patterns optimized for a target configuration and is robust to initialization. We assess DDPS on 3D-printed objects with ground-truth normals and diverse real-world objects, validating that DDPS enables effective photometric-stereo reconstruction