240 research outputs found
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The effects of carbohydrate and amino acids on muscle protein synthesis after acute resistance exercise and muscle adaptation following chronic resistance training
Resistance exercise (RE) is purported to induce muscle protein accretion primarily by stimulating muscle protein synthesis (MPS), with its effect potentiated by providing a protein or amino acid (AA) supplement post exercise. Glutamine, a conditionally essential AA, is increasingly recommended to improve exercise performance, but it is poorly soluble and unstable in sports drinks. This limitation can be overcome by combining L-glutamine with L-alanine to form a dipeptide (AlaGln). The first study demonstrated that AlaGln supplementation post resistance exercise significantly reduced the phosphorylation of AMPK and NF-kB p65 without activating intracellular signals for MPS. This study also showed that whey protein (WP) activated the mTOR signaling pathways without affecting signaling proteins that controls muscle protein breakdown (MPB). The results provide indirect evidence that AlaGln blocks MPB via suppressing the activation of AMPK-FOXO3A and NF-kB p65, while WP promotes MPS. Recent research suggests that adding carbohydrate (CHO) to a protein supplement post exercise can produce greater protein accretion and subsequently increase the magnitude of resistance training adaptation relative to protein supplementation alone. Early studies from our laboratory found that co-ingestion of CHO and protein, as compared with CHO or protein intake individually, had a greater effect on the activation of anabolic signaling proteins. However, the effect of CHO plus protein on MPS and muscle adaptation is controversial. The second study demonstrated that adding CHO to a protein supplement (CP) accelerated MPS via activating the mTOR-signaling pathway in comparison with placebo (PLA) and WP during early exercise recovery, but CP did not affect signaling proteins that regulate MPB. The third study found that CP enhanced muscle strength relative to PLA and WP. The greater strength development in CP appeared due to increased myofibrillar protein content. Increased muscle hypertrophy occurred without an increase in myonuclei suggesting satellite cell activation was not required for muscle fiber development. Taken together, the results of this series of studies suggest that 1) AlaGln inhibits MPB following acute RE; 2) Adding CHO to a protein supplement accelerates muscle recovery by stimulating MPS, and increases the magnitude of muscle strength by accumulating more myofibrillar protein in comparison with PLA and WP.Kinesiology and Health Educatio
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Essays on Investor Information Processing
This dissertation presents a collection of three essays on how investors process information, one of the most fundamental questions in finance. It employs advanced empirical techniques such as textual analysis and machine learning and spans various investor groups, including institutional investors, retail investors, and investors of emerging asset classes. The first essay “Does Partisanship Affect Mutual Fund Information Processing? Evidence from Textual Analysis on Earnings Calls” shows that partisan funds react stronger to information more consistent with their pre-existing beliefs. The second essay “Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic”, co-authored with Jinfei Sheng and Zheng Sun, explores how political beliefs affect asset prices during the COVID pandemic. The third essay “How Do Investors Value Technology in Cryptocurrency? Evidence from Textual Analysis”, co-authored with Jinfei Sheng and Yukun Liu, examines how investors evaluate and process information about new technologies within the cryptocurrency market. Overall, this dissertation offers valuable insights into how various factors shape investor information processing and asset prices
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Associations between health behaviors and adolescents life satisfaction using structural equation modeling (SEM)
Life satisfaction is an important indicator in suicidal behavior. The purpose of this study was to investigate the influences of health-related behaviors on adolescent life satisfaction using structural equation modeling (SEM). Data were obtained from the Health Behavior in School-Age Children (HBSC), 2001-2002. Because of the complex nature, SEM was preferred to be used over regression models in the present study. The results indicated that good eating habits and high scores of self-reported health played the greatest roles in promoting life satisfaction. The effects of both factors on life satisfaction were also mediated by academic achievement. Physical activity was a positive predictor of life satisfaction, but its effect appears to be mediated by health and academic achievement, rather than affecting life satisfaction directly. Moreover, physical activity was positively associated with good eating habits. These results generated from SEM were also compared with that from multiple linear regressions. Slight differences in the standardized coefficients for the total effects between SEM and regression models were detected due to the existing latent variable in SEM, but the general proportion variance accounted for in each outcome variable were similar across the two analyses. In summary, although there were some limitations for the study design and the building of the model, this study suggested that good habits with respect to diets may be beneficial for improvements in health and academic achievement, which in turn lead to positive scores of adolescent life satisfaction. High frequent physical activity and low BMI were poor but acceptable predictors of life satisfaction.Statistic
Fault-Tolerant Consensus of Multi-Agent Systems Subject to Multiple Faults and Random Attacks
This paper explores the consensus control problem of nonlinear multi-agent systems (MASs) under complex cyber-physical threats (CPTs), which encompass sensor/actuator faults, input/output channel noises, and random cyber-attacks. The multiple sensor/actuator faults are uniformly modeled as an exponential type, while random cyber-attacks are characterized by a Markov chain. To enhance the safety and security of MASs under CPTs, the distributed normalized observers are first developed, enabling precise estimations of unknown state and fault information. Subsequently, the distributed fault-tolerant consensus control (FTCC) scheme with a positive reconstruction mechanism is proposed to maintain resilience against attacks, compensation for faults, and robustness to noises in MASs under adverse CPTs. The two notable innovations can be outlined as follows: i) The achievement of FTCC objectives under complex CPTs, demonstrating strong algorithmic transferability in both non-attack and random attack scenarios. ii) The adoption of a double-layer distributed framework in the estimation layer and control layer, balancing computational complexity and efficiency improvements compared to a combination of decentralized and distributed approaches. Simulation results finally confirm the efficacy and feasibility of the proposed FTCC algorithm
Endoscopic rhizotomy for chronic lumbar zygapophysial joint pain.
BACKGROUND: Chronic lumbar zygapophysial joint pain is a common cause of chronic low back pain. Percutaneous radiofrequency ablation (RFA) is one of the effective management options; however, the results from the traditional RFA need to be improved in certain cases. The aim of this study is to investigate the effect of percutaneous radiofrequency ablation under endoscopic guidance (ERFA) for chronic low back pain secondary to facet joint arthritis.
METHODS: This is a prospective study enrolled 60 patients. The cases were randomized into two groups: 30 patients in the control group underwent traditional percutaneous radiofrequency ablation, others underwent ERFA. The lumbar visual analog scale (VAS), MacNab score, and postoperative complications were used to evaluate the outcomes. All outcome assessments were performed at postoperative 1 day, 1 month, 3 months, 6 months, and 12 months.
RESULTS: There was no difference between the two groups in preoperative VAS (P \u3e 0.05). VAS scores, except the postoperative first day, in all other postoperative time points were significantly lower than preoperative values each in both groups (P \u3c 0.05). There was no significant difference between the two groups in VAS at 1 day, 1 month, and 3 months after surgery (P \u3e 0.05). However, the EFRA demonstrated significant benefits at the time points of 3 months and 6 months (P \u3e 0.05). The MacNab scores of 1-year follow-up in the ERFA group were higher than that in the control group (P \u3c 0.05). The incidence of complications in the ERFA group was significantly less than that in the control group (P \u3c 0.05).
CONCLUSIONS: ERFA may achieve more accurate and definite denervation on the nerves, which leads to longer lasting pain relief
Retrospective respiration-gated whole-body photoacoustic computed tomography of mice
Photoacoustic tomography (PAT) is an emerging technique that has a great potential for preclinical whole-body imaging. To date, most whole-body PAT systems require multiple laser shots to generate one cross-sectional image, yielding a frame rate of <1 Hz. Because a mouse breathes at up to 3 Hz, without proper gating mechanisms, acquired images are susceptible to motion artifacts. Here, we introduce, for the first time to our knowledge, retrospective respiratory gating for whole-body photoacoustic computed tomography. This new method involves simultaneous capturing of the animal’s respiratory waveform during photoacoustic data acquisition. The recorded photoacoustic signals are sorted and clustered according to the respiratory phase, and an image of the animal at each respiratory phase is reconstructed subsequently from the corresponding cluster. The new method was tested in a ring-shaped confocal photoacoustic computed tomography system with a hardware-limited frame rate of 0.625 Hz. After respiratory gating, we observed sharper vascular and anatomical images at different positions of the animal body. The entire breathing cycle can also be visualized at 20 frames/cycle
Progressive Transfer Learning for Dexterous In-Hand Manipulation with Multi-Fingered Anthropomorphic Hand
Dexterous in-hand manipulation for a multi-fingered anthropomorphic hand is
extremely difficult because of the high-dimensional state and action spaces,
rich contact patterns between the fingers and objects. Even though deep
reinforcement learning has made moderate progress and demonstrated its strong
potential for manipulation, it is still faced with certain challenges, such as
large-scale data collection and high sample complexity. Especially, for some
slight change scenes, it always needs to re-collect vast amounts of data and
carry out numerous iterations of fine-tuning. Remarkably, humans can quickly
transfer learned manipulation skills to different scenarios with little
supervision. Inspired by human flexible transfer learning capability, we
propose a novel dexterous in-hand manipulation progressive transfer learning
framework (PTL) based on efficiently utilizing the collected trajectories and
the source-trained dynamics model. This framework adopts progressive neural
networks for dynamics model transfer learning on samples selected by a new
samples selection method based on dynamics properties, rewards and scores of
the trajectories. Experimental results on contact-rich anthropomorphic hand
manipulation tasks show that our method can efficiently and effectively learn
in-hand manipulation skills with a few online attempts and adjustment learning
under the new scene. Compared to learning from scratch, our method can reduce
training time costs by 95%.Comment: 12 pages, 7 figures, submitted to TNNL
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