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Dose-response effect of pre-exercise carbohydrates under muscle glycogen unavailability:Insights from McArdle disease

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Journal of Sport and Health science 2024年第3期13卷 398-408页

作者： Pedro L.Valenzuela Alfredo Santalla Lidia B.Alejo Andrea Merlo Asuncion Bustos Laura Castellote-Belles Roser Ferrer-Costa Nicola A.Maffiuletti David Barranco-Gil Tomas Pinos Alejandro Lucia Physical Activity and Health Research Group(“PaHerg”) Research Institute of Hospital“12 de Octubre”(“imas12”)Madrid 28041Spain Department of Systems Biology University of AlcalaMadrid 28871Spain Department of Sport and Computer Science Section of Physical Education and SportsFaculty of SportUniversidad Pablo de OlavideSevilla 41013Spain EVOPRED Research Group Universidad Europea de CanariasTenerife 38300Spain Faculty of Sport Sciences Universidad Europea de MadridMadrid 28670Spain Gait&Motion Analysis Laboratory Sol et Salus HospitalTorre Pedrera di Rimini(RN)47922Italy Department of Clinical Biochemistry Laboratoris ClınicsHospital Universitari Vall d’HebronBarcelona 08035Spain Human Performance Lab Schulthess ClinicZurich 8008Switzerland Biomedical Research Networking Center on Rare Disorders(CIBERER) Barcelona 08035Spain Mitochondrial and Neuromuscular Disorders Unit Vall d’Hebron Institut de RecercaUniversitat Autonoma de BarcelonaBarcelona 08035Spain

Background:This study aimed to determine the effect of different carbohydrate(CHO)doses on exercise capacity in patients with McArdle disease—the paradigm of“exercise intolerance”,characterized by complete muscle glycogen unavailability—and to determine whether higher exogenous glucose levels affect metabolic responses at the McArdle muscle cell(in vitro)***:Patients with McArdle disease(n=8)and healthy controls(n=9)underwent a 12-min submaximal cycling constant-load bout followed by a maximal ramp test 15 min after ingesting a non-caloric *** a randomized,double-blinded,cross-over design,patients repeated the tests after consuming either 75 g or 150 g of CHO(glucose:fructose=2:1).Cardiorespiratory,biochemical,perceptual,and electromyographic(EMG)variables were ***,glucose uptake and lactate appearance were studied in vitro in wild-type and McArdle mouse myotubes cultured with increasing glucose concentrations(0.35,1.00,4.50,and 10.00 g/L).Results:Compared with controls,patients showed the“classical”second-wind phenomenon(after prior disproportionate tachycardia,myalgia,and excess electromyographic activity during submaximal exercise,all p<0.05)and an impaired endurance exercise capacity(-51%ventilatory threshold and55%peak power output,both p<0.001).Regardless of the CHO dose(p<0.05 for both doses compared with the placebo),CHO intake increased blood glucose and lactate levels,decreased fat oxidation rates,and attenuated the second wind in the ***,only the higher dose increased ventilatory threshold(+27%,p=0.010)and peak power output(+18%,p=0.007).In vitro analyses revealed no differences in lactate levels across glucose concentrations in wild-type myotubes,whereas a doseresponse effect was observed in McArdle ***:CHO intake exerts beneficial effects on exercise capacity in McArdle disease,a condition associated with total muscle glycogen *** of these benefits are dose dependent.

关键词： Endurance Glycogen storage disease Glycogenosis Nutrition Supplement

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Striving for Authentic and Sustained Technology Use In the Classroom: Lessons Learned from a Longitudinal Evaluation of a Sensor-based science Education Platform

arXiv

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arXiv 2023年

作者： Chua, Yvonne Cooray, Sankha Cortes, Juan Pablo Forero Denny, Paul Dupuch, Sonia Garbett, Dawn L. Nassani, Alaeddin Cao, Jiashuo Qiao, Hannah Reis, Andrew Reis, Deviana Scholl, Philipp M. Sridhar, Priyashri Kamlesh Suriyaarachchi, Hussel Taimana, Fiona Tang, Vanessa Weerasinghe, Chamod Wen, Elliott Wu, Michelle Wu, Qin Zhang, Haimo Nanayakkara, Suranga Augmented Human Lab. Auckland Bioengineering Institute The University of Auckland New Zealand Augmented Human Lab. Department of Information Systems and Analytics National University of Singapore Singapore School of Computer Science The University of Auckland New Zealand Faculty of Education The University of Auckland Auckland New Zealand Engineering Product Development Singapore University of Technology and Design Singapore OPPO Research Institute Shanghai China

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in realistic classroom settings. However, sustained and authentic classroom use requires technology platforms to be seen by teachers as both easy to use and of value. We describe our development of a sensor-based platform to support science teaching that followed a 14-month user-centered design process. We share insights from this design and development approach, and report findings from a 6-month large-scale evaluation involving 35 schools and 1245 students. We share lessons learnt, including that technology integration is not an educational goal per se and that technology should be a transparent tool to enable students to achieve their learning goals. © 2023, CC BY-NC-ND.

关键词： Students

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An Explainable Deep Neural Network Model Based on Multi-Granularity Co-Attention to Improve Prediction of Drug-Target Binding Affinity

SSRN

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SSRN 2024年

作者： Lin, Yanmei Cao, Aiqing Deng, Lei Liao, Mingzhi Zhang, Ru Zhang, Hao Peng, Yuzhong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530001 China Guangxi Academy of Sciences Nanning530007 China School of Computer Science and Engineering Central South University Changsha410083 China Center of Bioinformatics College of Life Sciences Northwest A&F University Shaanxi Yangling712100 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Center for Applied Mathematics of Guangxi Nanning Normal University Nanning530100 China

The prediction of drug-target interactions is pivotal in modern new drug development and drug repositioning. Nonetheless, current methods for predicting drug-target interactions often lose essential molecular structure information during the modeling process, leading to low prediction accuracy. This work proposes rMolGIN, a novel graph neural network with residual connectivity for molecular feature learning. rMolGIN not only takes into account the differences in chemical bonding properties and atomic neighborhood interactions, but also captures global and local structural information of drug molecules. Furthermore, we propose a novel drug-target affinity model based on rMolGIN and deep neural networks, coupled with a multi-granularity co-attention mechanism called HMCoDTA to capture comprehensive structural insights from both drugs and proteins. The proposed innovative model allows for comprehensive insight into the structural relationships between drugs and proteins, facilitating a more accurate prediction of binding affinities. To validate the effectiveness of our proposed methods, we conducted extensive experiments on three benchmark datasets of drug-target binding affinity, comparing HMCoDTA against 19 state-of-the-art models. Notably, the results show that HMCoDTA outperforms existing methods, demonstrating the effectiveness and advantages of our model in predicting drug-target binding affinity. © 2024, The Authors. All rights reserved.

关键词： Prediction models

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SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches 16th

SceneSketcher: Fine-Grained Image Retrieval with Scene Sketc...

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16th European Conference on computer Vision, ECCV 2020

作者： Liu, Fang Zou, Changqing Deng, Xiaoming Zuo, Ran Lai, Yu-Kun Ma, Cuixia Liu, Yong-Jin Wang, Hongan State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China HMI Laboratory Huawei Technologies Shenzhen China Cardiff University Cardiff United Kingdom Tsinghua University Beijing China

ISBN: (纸本)9783030585280

Sketch-based image retrieval (SBIR) has been a popular research topic in recent years. Existing works concentrate on mapping the visual information of sketches and images to a semantic space at the object level. In this paper, for the first time, we study the fine-grained scene-level SBIR problem which aims at retrieving scene images satisfying the user’s specific requirements via a freehand scene sketch. We propose a graph embedding based method to learn the similarity measurement between images and scene sketches, which models the multi-modal information, including the size and appearance of objects as well as their layout information, in an effective manner. To evaluate our approach, we collect a dataset based on SketchyCOCO and extend the dataset using Coco-stuff. Comprehensive experiments demonstrate the significant potential of the proposed approach on the application of fine-grained scene-level image retrieval. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Nested Named Entity Recognition as Holistic Structure Parsing

arXiv

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arXiv 2022年

作者： Yang, Yifei Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previous studies on nested NER usually apply linear structure to model the nested NEs which are actually accommodated in a hierarchical structure. Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Besides, there is no research on applying corpus-level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpus-level. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art. Further empirical studies show that our proposed corpus-aware features can substantially improve NER domain adaptation, which demonstrates the surprising advantage of our proposed corpus-level holistic structure modeling. © 2022, CC BY.

关键词： Natural language processing systems

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UNSUPERVISED DATA SELECTION FOR TTS: USING ARABIC BROADCAST NEWS AS A CASE STUDY

arXiv

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arXiv 2023年

作者： Baali, Massa Hayashi, Tomoki Mubarak, Hamdy Maiti, Soumi Watanabe, Shinji El-Hajj, Wassim Ali, Ahmed KANARI AI CA United States Carnegie Mellon University Pittsburgh United States Qatar Computing Research Institute HBKU Doha Qatar Nagoya University Japan Human Dataware Lab. Co. Ltd. Japan American University of Beirut Computer Science Department Beirut Lebanon

Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech. In contrast, low-resource languages, including Arabic, have very limited TTS systems due to the lack of resources. We propose a fully unsupervised method for building TTS, including automatic data selection and pre-training/fine-tuning strategies for TTS training, using broadcast news as a case study. We show how careful selection of data, yet smaller amounts, can improve the efficiency of TTS system in generating more natural speech than a system trained on a bigger dataset. We adopt to propose different approaches for the: 1) data: we applied automatic annotations using DNSMOS, automatic vowelization, and automatic speech recognition (ASR) for fixing transcriptions’ errors;2) model: we used transfer learning from high-resource language in TTS model and fine-tuned it with one hour broadcast recording then we used this model to guide a FastSpeech2-based Conformer model for duration. Our objective evaluation shows 3.9% character error rate (CER), while the ground truth has 1.3% CER. As for the subjective evaluation, where 1 is bad and 5 is excellent, our FastSpeech2-based Conformer model achieved a mean opinion score (MOS) of 4.4 for intelligibility and 4.2 for naturalness, where many annotators recognized the voice of the broadcaster, which proves the effectiveness of our proposed unsupervised method. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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Acquisition evaluation on outdoor scanning for archaeological artifact digitalization 15

Acquisition evaluation on outdoor scanning for archaeologica...

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15th International Joint Conference on computer Vision, Imaging and computer Graphics Theory and Applications, VISIGRAPP 2020

作者： Kusuma Frisky, Aufaclav Zatu Fajri, Adieyatna Brenner, Simon Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Wien Austria Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9789897584022

Archaeological archives are important assets because they provide primary information resources for research, mainly digital archives that not degraded. Instead of directly visiting a site, an archaeologist can examine and manipulate the data without harming the real object. However, choosing an efficient scanning scheme with detailed results is a challenging task. In this work, we present new sculpture models obtained in three different ways and assess it in two comparison approaches: A quantitative and qualitative assessment. The quantitative comparison architecture provides a detailed assessment of three different scanning mechanisms in two stages: point cloud and mesh comparison. This evaluation is purposed to describe the differences between unmodified data. Finally, a qualitative evaluation is performed by an expert and practitioner to explain the difference based on four different produced models to help their needs in the real application. Copyright © 2020 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Scanning

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A virtual reality methodology for cardiopulmonary resuscitation training with and without a physical mannequin

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Journal of Biomedical Informatics 2020年 111卷 103590-103590页

作者： Buttussi, Fabio Chittaro, Luca Valent, Francesca Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Udine Italy SOC Istituto di Igiene ed Epidemiologia Clinica Azienda Sanitaria Universitaria Friuli Centrale Udine Italy

Background: Cardiopulmonary resuscitation (CPR) is an emergency procedure that can increase survival after a cardiac arrest. Performing CPR effectively requires both procedural knowledge and manual skills. Traditional CPR training methodology includes lessons led by instructors and supervised practice on mannequins, thus requiring considerable resources. Objective: This paper proposes a new methodology for low-cost CPR training based on virtual reality (VR) with and without the addition of a physical mannequin. Moreover, it describes an experimental evaluation of the methodology that assessed gain in manual skills during training, transfer of procedural knowledge and manual skills in a final assessment, and changes in self-efficacy with three measurements over time (pre-training, post-training, and post-assessment). Methods: We implemented a VR application that supports the proposed methodology, and can thus be used with or without a mannequin. The experimental evaluation involved 30 participants who tried CPR in VR twice, performing two repetitions of 30 chest compressions per trial. Half participants tried the VR application with the mannequin and half without it. Final assessment required all participants to perform CPR on the mannequin without the assistance of VR. To assess self-efficacy, participants filled in a questionnaire at the three times of measurement. Results: Mixed-design ANOVAs showed effects of repetition, effects of group, or interaction between the two variables on manual skills assessed during training. In the final assessment, participants in both groups correctly remembered most of the steps of the procedure. ANOVAs revealed differences between the two groups only in pressure-related skills (better with mannequin) and in the number of wrong steps added to the procedure (better without mannequin). Mixed-design ANOVA showed a self-efficacy increase in both groups after training, which was maintained after final assessment. Conclusions: The propo

关键词： Resuscitation

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach

arXiv

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arXiv 2025年

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan United States

—This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2025, CC BY-NC-ND.

关键词： Anthropomorphic robots

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Solution of DeBERTaV3 on CommonsenseQA

arXiv

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arXiv 2022年

作者： Peng, Letian Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language inference ability of DeBERTaV3 helps its single and ensemble model set the new state-of-the-art on CommonsenseQA. Copyright © 2022, The Authors. All rights reserved.

关键词： Text processing

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