[Effects of branched-chain amino acid supplementation on skeletal muscle proteolytic pathways after exercise-induced muscle injury in rats].

OBJECTIVE: To explore the role of branched-chain amino acid(BCAA) supplementation on muscle damage and the regulation of Krüppel-like factor 15(KLF15) and nuclear factor kappa B(NF-κB) mediated proteolytic pathways after an acute eccentric exercise. METHODS: Male SD rats were divided into placebo group(PLA) and BCAA group(BCAA) randomly, 32 rats per group. Both group were then placed into subgroups: placebo and pre-exercise group(PC), placebo and immediately after exercise group(PE), placebo and 6 h after exercise group(PE6), placebo and 12 h after exercise group(PE12), BCAA and pre-exercise group(BC), BCAA and immediately after exercise group(BE), BCAA and 6 h after exercise group(BE6), BCAA and 12 h after exercise group(BE12), 8 rats per group. Rats in BCAA groups were supplied with BCAA(1 g/(kg·d·BW), 3 days) before the exercise day and placebo groups with equal volume of distilled water. The exercised groups performed a 2 h eccentric exercise on treadmill(16 m/min, -16° slope). Blood and gastrocnemius were collected according to the time points. RT-qPCR was used to measure the mRNA expression of KLF15, NF-κB, FoxO1, Atrogin-1 and MuRF1 in gastrocnemius. RESULTS: (1) No damage was found in myocytes of BC and PC group. The process of morphological damage in BCAA group was relatively faster. (2) The mRNA expression levels of KLF15, FoXO1, Atrogin-1 and MuRF1 in PE were higher than those in PC(P<0.05, P<0.01), NF-κB and Atrogin-1 in PE12 were higher than those in PC(P<0.05). The mRNA expression levels of FoXO1 in BE were higher than those in BC(P<0.05). Compared with PE, the mRNA expression levels of KLF15, Atrogin-1 and MuRF1 in BE were lower(P<0.05, P<0.01), NF-κB and Atrogin-1 in BE12 were lower than those in PE12(P<0.05). The level of serum 3-MH in PE12 group was higher than that in PC group(P<0.05). CONCLUSION: The proteolysis of skeletal muscle after high-intensity eccentric exercise is mediated by two different pathways: KLF15 and NF-κB, whose activation is time-dependent. BCAA may reduce skeletal muscle proteolysis by lowering the level of gene transcription in the KLF15 and NF-κB related protein degradation pathway, which occurs immediately after exercise.

Which Specific Exercise Models Are Most Effective on Global Cognition in Patients with Cognitive Impairment? A Network Meta-Analysis.

(1) Introduction: Physical exercise interventions can impart significant cognitive benefits to older adults suffering from cognitive impairment (CI). However, the efficacy of these interventions can vary widely, depending on the type, intensity, duration and frequency of exercise. (2) Aim: To systematically review the efficacy of exercise therapy on global cognition in patients with CI using a network meta-analysis (NMA). (3) Methods: The PubMed, Embase, Sport Discus (EBSCO) and Cochrane Library databases were electronically searched to collect randomized controlled trials (RCTs) on exercise for patients with CI from inception to 7 August 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. The NMA was performed using the consistency model. (4) Results: A total of 29 RCTs comprising 2458 CI patients were included. The effects of different types of exercise on patients with CI were ranked as follows: multicomponent exercise (SMD = 0.84, 95% CI 0.31 to 1.36, p = 0.002), short duration (≤45 min) (SMD = 0.83, 95% CI 0.18 to 1.19, p = 0.001), vigorous intensity (SMD = 0.77, 95% CI 0.18 to 1.36, p = 0.011) and high frequency (5-7 times/week) (SMD = 1.28, 95% CI 0.41 to 2.14, p = 0.004). (5) Conclusion: These results suggested that multicomponent, short-duration, high-intensity, and high-frequency exercise may be the most effective type of exercise in improving global cognition in CI patients. However, more RCTs based on direct comparison of the effects of different exercise interventions are needed. (6) NMA registration identifier: CRD42022354978.

Branched-chain amino acids promotes the repair of exercise-induced muscle damage via enhancing macrophage polarization.

The repair of exercise-induced muscle damage (EIMD) is closely related with inflammation. Branched-chain amino acids (BCAAs), as a nutritional supplement, promote EIMD repair; however, the underlying mechanism remains unclear. In vivo, Sprague-Dawley rats were subjected to Armstrong's eccentric exercise (a 120-min downhill run with a slope of -16° and a speed of 16 m min-1) to induce EIMD and BCAA supplement was administered by oral gavage. Protein expression of macrophages (CD68 and CD163) and myogenic regulatory factors (MYOD and MYOG) in gastrocnemius was analyzed. Inflammatory cytokines and creatine kinase (CK) levels in serum was also measured. In vitro, peritoneal macrophages from mice were incubated with lipopolysaccharide (LPS) or IL-4 with or without BCAAs in culture medium. For co-culture experiment, C2C12 cells were cultured with the conditioned medium from macrophages prestimulated with LPS or IL-4 in the presence or absence of BCAAs. The current study indicated BCAA supplementation enhanced the M1/M2 polarization of macrophages in skeletal muscle during EIMD repair, and BCAAs promoted M1 polarization through enhancing mTORC1-HIF1α-glycolysis pathway, and promoted M2 polarization independently of mTORC1. In addition, BCAA-promoted M1 macrophages further stimulated the proliferation of muscle satellite cells, whereas BCAA-promoted M2 macrophages stimulated their differentiation. Together, these results show macrophages mediate the BCAAs' beneficial impacts on EIMD repair via stimulating the proliferation and differentiation of muscle satellite cells, shedding light on the critical role of inflammation in EIMD repair and the potential nutritional strategies to ameliorate muscle damage.

A New Double-Layer Decentralized Consistency Algorithm for the Multi-Satellite Autonomous Mission Allocation Based on a Block-Chain.

The traditional on-board centralized-distributed mission negotiation architecture has poor security and reliability. It can easily give rise to the collapse of the whole system when the master node is attacked by malicious nodes. To address this issue, the decentralized consistency algorithms commonly used in the internet world are referred to in this paper. Firstly, four typical consistency algorithms suitable for the Internet and which are named RAFT, PBFT, RIPPLE and DPOS are selected and modified for a multi-satellite autonomous mission negotiation. Additionally, based on the above modified consistency algorithms, a new double-layer decentralized consistency algorithm named DDPOS is proposed. It is well known that the above four common consistency algorithms cannot have both a low resource occupation and high security. The DDPOS algorithm can integrate the advantages of four common consistency algorithms due to its freedom of choice attribute, which can enable satellite clusters to flexibly adopt different appropriate consistency algorithms and the number of decentralized network layers. The DDPOS algorithm not only greatly improves the security and reliability of the whole satellite cluster, but also effectively reduces the computing and communication resources occupation of the satellite cluster. Without the presence of a malicious node attack, the resource occupation of the DDPOS algorithm is almost the same as that of the RAFT algorithm. However, in the case of a malicious node attack, compared with the RAFT algorithm, the total computation and total bandwidth occupation of the DDPOS algorithm have decreased by 67% and 75%, respectively. Moreover, it is surprising that although the DDPOS algorithm is more complex, its code size is only about 8% more than the RAFT algorithm. Finally, the effectiveness and feasibility of the DDPOS algorithm in the on-board practical application are analyzed and verified via simulation experiments.

Identifying essential proteins from protein-protein interaction networks based on influence maximization.

BACKGROUND: Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein-protein interaction (PPI) data, computationally identifying essential proteins from protein-protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed. RESULTS: In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism. CONCLUSIONS: We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.

A Controller Design for Approaching Disabled Satellites Based on Discrete Sample Points.

When approaching and removing a disabled satellite, the accuracy of the controller is imperative to the success of the mission because if the mission fails, more space debris can be produced due to satellite collision. To address this issue, a controller directly driven by discrete sample data points is proposed in this paper. First, the input vector for the controller is placed into a state space as a point. The state space also contains points constructed by the input vectors of pre-generated samples, which are created by the GPOPS planning algorithm along with control commands as sample output vectors. Then, an adjacent range is selected and the sample points within are collected. To accelerate the process, a series of data processing methods are implemented, including the dichotomy method, table look-up method, and random selection method. Finally, the control commands are computed using the iteratively reweighted least-squares algorithm with the assumption that similar inputs have similar outputs. According to the simulation results, the discrete point controller is more precise than the neural network controller.

A Real-Time Effectiveness Evaluation Method for Remote Sensing Satellite Clusters on Moving Targets.

Recently, remote sensing satellites have become increasingly important in the Earth observation field as their temporal, spatial, and spectral resolutions have improved. Subsequently, the quantitative evaluation of remote sensing satellites has received considerable attention. The quantitative evaluation method is conventionally based on simulation, but it has a speed-accuracy trade-off. In this paper, a real-time evaluation model architecture for remote sensing satellite clusters is proposed. Firstly, a multi-physical field coupling simulation model of the satellite cluster to observe moving targets is established. Aside from considering the repercussions of on-board resource constraints, it also considers the consequences of the imaging's uncertainty effects on observation results. Secondly, a moving target observation indicator system is developed, which reflects the satellite cluster's actual effectiveness in orbit. Meanwhile, an indicator screening method using correlation analysis is proposed to improve the independence of the indicator system. Thirdly, a neural network is designed and trained for stakeholders to realize a rapid evaluation. Different network structures and parameters are comprehensively studied to determine the optimized neural network model. Finally, based on the experiments carried out, the proposed neural network evaluation model can generate real-time, high-quality evaluation results. Hence, the validity of our proposed approach is substantiated.

Human-in-the-Loop Low-Shot Learning.

We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous samples that are quite different from existing labeled novel data can inevitably emerge in the testing phase. To this end, we consider augmenting an uncertainty assessment module into low-shot learning system to account into the disturbance of those out-of-distribution (OOD) samples. Once detected, these OOD samples are passed to human beings for active labeling. Due to the discrete nature of this uncertainty assessment process, the whole Human-In-the-Loop Low-shot (HILL) learning framework is not end-to-end trainable. We hence revisited the learning system from the aspect of reinforcement learning and introduced the REINFORCE algorithm to optimize model parameters via policy gradient. The whole system gains noticeable improvements over existing low-shot learning approaches.

Gated Value Network for Multilabel Classification.

We introduce a gated value network (GVN) for general multilabel classification (MLC) tasks. GVN was motivated by deep value network (DVN) that directly exploits the "compatibility" metric as the learning pursuit for MLC. Meanwhile, it further improves traditional DVN on twofold. First, GVN relaxes the complex variable optimization steps in DVN inference by incorporating a feedforward predictor for straightforward multilabel prediction. Second, GVN also introduces the gating mechanism to block confounding factors from the input data that allows more precise compatibility evaluations for data and their potential multilabels. The whole GVN framework is trained in an end-to-end manner with policy gradient approaches. We show the effectiveness and generalization of GVN on diverse learning tasks, including document classification, audio tagging, and image attribute prediction.

Distributed synchronization of networked drive-response systems: A nonlinear fixed-time protocol.

The distributed synchronization of networked drive-response systems is investigated in this paper. A novel nonlinear protocol is proposed to ensure that the tracking errors converge to zeros in a fixed-time. By comparison with previous synchronization methods, the present method considers more practical conditions and the synchronization time is not dependent of arbitrary initial conditions but can be offline pre-assign according to the task assignment. Finally, the feasibility and validity of the presented protocol have been illustrated by a numerical simulation.

A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels.

Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%.