Mitochondrial stress response and myogenic differentiation.

Regeneration and repair are prerequisites for maintaining effective function of skeletal muscle under high energy demands, and myogenic differentiation is one of the key steps in the regeneration and repair process. A striking feature of the process of myogenic differentiation is the alteration of mitochondria in number and function. Mitochondrial dysfunction can activate a number of transcriptional, translational and post-translational programmes and pathways to maintain cellular homeostasis under different types and degrees of stress, either through its own signaling or through constant signaling interactions with the nucleus and cytoplasm, a process known as the mitochondrial stress responses (MSRs). It is now believed that mitochondrial dysfunction is closely associated with a variety of muscle diseases caused by reduced levels of myogenic differentiation, suggesting the possibility that MSRs are involved in messaging during myogenic differentiation. Also, MSRs may be involved in myogenesis by promoting bioenergetic remodeling and assisting myoblast survival during myogenic differentiation. In this review, we will take MSRs as an entry point to explore its concrete regulatory mechanisms during myogenic differentiation, with a perspective to provide a theoretical basis for the treatment and repair of related muscle diseases.

Phylogenomics resolves the higher-level phylogeny of herbivorous eriophyoid mites (Acariformes: Eriophyoidea).

BACKGROUND: Eriophyoid mites (Eriophyoidea) are among the largest groups in the Acariformes; they are strictly phytophagous. The higher-level phylogeny of eriophyoid mites, however, remains unresolved due to the limited number of available morphological characters-some of them are homoplastic. Nevertheless, the eriophyoid mites sequenced to date showed highly variable mitochondrial (mt) gene orders, which could potentially be useful for resolving the higher-level phylogenetic relationships. RESULTS: Here, we sequenced and compared the complete mt genomes of 153 eriophyoid mite species, which showed 54 patterns of rearranged mt gene orders relative to that of the hypothetical ancestor of arthropods. The shared derived mt gene clusters support the monophyly of eriophyoid mites (Eriophyoidea) as a whole and the monophylies of six clades within Eriophyoidea. These monophyletic groups and their relationships were largely supported in the phylogenetic trees inferred from mt genome sequences as well. Our molecular dating results showed that Eriophyoidea originated in the Triassic and diversified in the Cretaceous, coinciding with the diversification of angiosperms. CONCLUSIONS: This study reveals multiple molecular synapomorphies (i.e. shared derived mt gene clusters) at different levels (i.e. family, subfamily or tribe level) from the complete mt genomes of 153 eriophyoid mite species. We demonstrated the use of derived mt gene clusters in unveiling the higher-level phylogeny of eriophyoid mites, and underlines the origin of these mites and their co-diversification with angiosperms.

Brightness Prediction of Large Color Gamut Laser Display Devices.

A brightness-perceived color appearance model tailored for large gamut display devices, exemplified by laser displays, was investigated. Psychophysical experiments on the brightness matching of 30 color stimuli with achromatic white light were conducted by 16 observers. The analysis compares the performance of a number of existing color appearance models and equivalent luminance models in predicting brightness. None of the models performed acceptably due to a severe underestimation of the Helmholtz-Kohlrausch (H-K) effect. A modified model of perceived brightness based on CAM16, taking into account the H-K effect, is proposed. Evaluated by psychophysical experiments, the proposed model exhibits a superior performance compared to the preceding models, especially within the extensive color gamut range stipulated by BT.2020. The results help to optimize the design of laser displays with a wide color gamut and high perceived brightness.

RNA binding protein: coordinated expression between the nuclear and mitochondrial genomes in tumors.

Mitochondria are the only organelles regulated by two genomes. The coordinated translation of nuclear DNA (nDNA) and mitochondrial DNA (mtDNA), which together co-encode the subunits of the oxidative phosphorylation (OXPHOS) complex, is critical for determining the metabolic plasticity of tumor cells. RNA-binding protein (RBP) is a post-transcriptional regulatory factor that plays a pivotal role in determining the fate of mRNA. RBP rapidly and effectively reshapes the mitochondrial proteome in response to intracellular and extracellular stressors, mediating the cytoplasmic and mitochondrial translation balance to adjust mitochondrial respiratory capacity and provide energy for tumor cells to adapt to different environmental pressures and growth needs. This review highlights the ability of RBPs to use liquid-liquid phase separation (LLPS) as a platform for translation regulation, integrating nuclear-mitochondrial positive and retrograde signals to coordinate cross-department translation, reshape mitochondrial energy metabolism, and promote the development and survival of tumor cells.

Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach.

Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, an inability to measure some parameters and disturbances. This paper considers an event-triggered learning control problem of the HSA with unknown dynamics based on adaptive dynamic programming (ADP) via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP-based data driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. Then, an event-based feedback strategy is introduced to the closed-loop system to save the communication resources and reduce the number of control updates. The convergence of the ADP-based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSAs.

Data-Driven Practical Cooperative Output Regulation Under Actuator Faults and DoS Attacks.

This article addresses the resilient practical cooperative output regulation problem (RPCORP) for multiagent systems subjected to both denial-of-service (DoS) attacks and actuator faults. Fundamentally different from the existing solutions to RPCORPs, the system parameters considered in this article are unknown to each agent, and a novel data-driven control approach is introduced to handle such an issue. The solution starts with developing resilient distributed observers for each follower in the presence of DoS attacks. Then, a resilient communication mechanism and a time-varying sampling period are introduced to, respectively, ensure the neighbor state is available as soon as attacks disappear and to avoid targeted attacks launched by intelligent attackers. Furthermore, a model-based fault-tolerant and resilient controller is designed based on the Lyapunov approach and the output regulation theory. In order to remove the reliance on system parameters, we leverage a new data-driven algorithm to learn controller parameters via the collected data. Rigorous analysis shows that the closed-loop system can resiliently achieve practical cooperative output regulation. Finally, a simulation example is given to illustrate the effectiveness of the achieved results.

Reinforcement Learning-Based Cooperative Optimal Output Regulation via Distributed Adaptive Internal Model.

In this article, a data-driven distributed control method is proposed to solve the cooperative optimal output regulation problem of leader-follower multiagent systems. Different from traditional studies on cooperative output regulation, a distributed adaptive internal model is originally developed, which includes a distributed internal model and a distributed observer to estimate the leader's dynamics. Without relying on the dynamics of multiagent systems, we have proposed two reinforcement learning algorithms, policy iteration and value iteration, to learn the optimal controller through online input and state data, and estimated values of the leader's state. By combining these methods, we have established a basis for connecting data-distributed control methods with adaptive dynamic programming approaches in general since these are the theoretical foundation from which they are built.

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach.

The dynamics of a robot may vary during operation due to both internal and external factors, such as non-ideal motor characteristics and unmodeled loads, which would lead to control performance deterioration and even instability. In this paper, the adaptive optimal output regulation (AOOR)-based controller is designed for the wheel-legged robot Ollie to deal with the possible model uncertainties and disturbances in a data-driven approach. We test the AOOR-based controller by forcing the robot to stand still, which is a conventional index to judge the balance controller for two-wheel robots. By online training with small data, the resultant AOOR achieves the optimality of the control performance and stabilizes the robot within a small displacement in rich experiments with different working conditions. Finally, the robot further balances a rolling cylindrical bottle on its top with the balance control using the AOOR, but it fails with the initial controller. Experimental results demonstrate that the AOOR-based controller shows the effectiveness and high robustness with model uncertainties and external disturbances.

Catalytic synthesis of nanodiamond based on CDC principle: influence of different catalysts on types and sizes.

Recently, we have successfully realized the catalytic synthesis of nanodiamond (ND) by embedding the Fe catalyst into carbide under high stress, followed by chlorine-etching at atmospheric pressure. In this work, we selected Fe, Co and Ni as the catalyst, and TiC as the precursor, aiming at investigating the influence of the catalyst type on the synthesis of NDs. The results have shown that all the three catalysts can catalyze the synthesis of ND structure, where various types of NDs have been observed. Furthermore, the crystal type and plasticity of the catalyst may have an important influence on the type and size of the resultant ND. In the case of Fe and Ni as the catalyst, both of which have a face centered cubic crystal structure, the types of NDs obtained are mainly C-type and R-type but only a few H-type. However, when the Co with a close-packed hexagonal crystal structure is used as the catalyst, more H-type NDs can be catalytically synthesized. Moreover, more small-sized NDs have been catalytically synthesized by Co, which may be ascribed to the worse plasticity of Co by comparison to Fe and Ni.

Event-Triggered Adaptive Optimal Control With Output Feedback: An Adaptive Dynamic Programming Approach.

This article presents an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. First, it is shown that the unmeasurable states can be reconstructed by using the measured input and output data. An event-based feedback strategy is then proposed to reduce the number of controller updates and save communication resources. The discrete-time algebraic Riccati equation is iteratively solved through event-triggered adaptive dynamic programming based on both policy iteration (PI) and value iteration (VI) methods. The convergence of the proposed algorithm and the closed-loop stability is carried out by using the Lyapunov techniques. Two numerical examples are employed to verify the effectiveness of the design methodology.

Regeneration of skeletal system with genipin crosslinked biomaterials.

Natural biomaterials, such as collagen, gelatin, and chitosan, are considered as promising candidates for use in tissue regeneration treatment, given their similarity to natural tissues regarding components and structure. Nevertheless, only receiving a crosslinking process can these biomaterials exhibit sufficient strength to bear high tensile loads for use in skeletal system regeneration. Recently, genipin, a natural chemical compound extracted from gardenia fruits, has shown great potential as a reliable crosslinking reagent, which can reconcile the crosslinking effect and biosafety profile simultaneously. In this review, we briefly summarize the genipin extraction process, biosafety, and crosslinking mechanism. Subsequently, the applications of genipin regarding aiding skeletal system regeneration are discussed in detail, including the advances and technological strategies for reconstructing cartilage, bone, intervertebral disc, tendon, and skeletal muscle tissues. Finally, based on the specific pharmacological functions of genipin, its potential applications, such as its use in bioprinting and serving as an antioxidant and anti-tumor agent, and the challenges of genipin in the clinical applications in skeletal system regeneration are also presented.

New Insight of Circular RNAs in Human Musculoskeletal Diseases.

Circular RNAs (circRNAs), a novel group of noncoding RNAs, are present in most eukaryotic cells. Different from messenger RNAs, circRNAs have a covalently closed single-stranded stable structure and often act in cell type and tissue-specific manners, indicating that they can be used as biomarkers. With the advance of high-throughput RNA sequencing technology and bioinformatics, a large number of circRNAs have been identified in association with musculoskeletal diseases, but the functions of most circRNAs have not been clarified. circRNAs regulate biological processes by adsorbing microRNA as "sponges," binding to proteins, acting as transcriptional regulators, and participating in translation of proteins. In this study, we discuss the latest understanding of biogenesis and gene regulatory mechanisms of circRNAs with special emphasis on new targets for musculoskeletal disease diagnosis and clinical treatment.

Loss of high-temperature requirement protein A2 protease activity induces mitonuclear imbalance via differential regulation of mitochondrial biogenesis in sarcopenia.

Cellular homeostasis requires tight coordination between nucleus and mitochondria, organelles that each possesses their own genomes. Disrupted mitonuclear communication has been found to be implicated in many aging processes. However, little is known about mitonuclear signaling regulator in sarcopenia which is a major contributor to the risk of poor health-related quality of life, disability, and premature death in older people. High-temperature requirement protein A2 (HtrA2/Omi) is a mitochondrial protease and plays an important role in mitochondrial proteostasis. HtrA2mnd2(-/-) mice harboring protease-deficient HtrA2/Omi Ser276Cys missense mutants exhibit premature aging phenotype. Additionally, HtrA2/Omi has been established as a signaling regulator in nervous system and tumors. We therefore asked whether HtrA2/Omi participates in mitonuclear signaling regulation in muscle degeneration. Using motor functional, histological, and molecular biological methods, we characterized the phenotype of HtrA2mnd2(-/-) muscle. Furthermore, we isolated the gastrocnemius muscle of HtrA2mnd2(-/-) mice and determined expression of genes in mitochondrial unfolded protein response (UPRmt ), mitohormesis, electron transport chain (ETC), and mitochondrial biogenesis. Here, we showed that HtrA2/Omi protease deficiency induced denervation-independent skeletal muscle degeneration with sarcopenia phenotypes. Despite mitochondrial hypofunction, upregulation of UPRmt and mitohormesis-related genes and elevated total reactive oxygen species (ROS) production were not observed in HtrA2mnd2(-/-) mice, contrary to previous assumptions that loss of protease activity of HtrA2/Omi would lead to mitochondrial dysfunction as a result of proteostasis disturbance and ROS burst. Instead, we showed that HtrA2/Omi protease deficiency results in different changes between the expression of nuclear DNA- and mitochondrial DNA-encoded ETC subunits, which is in consistent with their transcription factors, nuclear respiratory factors 1 and 2, and coactivator peroxisome proliferator-activated receptor γ coactivator 1α. These results reveal that loss of HtrA2/Omi protease activity induces mitonuclear imbalance via differential regulation of mitochondrial biogenesis in sarcopenia. The novel mechanistic insights may be of importance in developing new therapeutic strategies for sarcopenia.

Adaptive optimal target controlled infusion algorithm to prevent hypotension associated with labor epidural: An adaptive dynamic programming approach.

Patients receiving labor epidurals commonly experience arterial hypotension as a complication of neuraxial block. The purpose of this study was to design an adaptive optimal controller for an infusion system to regulate mean arterial pressure. A state-space model relating mean arterial pressure to Norepinephrine (NE) infusion rate was derived for controller design. A data-driven adaptive optimal control algorithm was developed based on adaptive dynamic programming (ADP). The stability and disturbance rejection ability of the closed-loop system were tested via a simulation model calibrated using available clinical data. Simulation results indicated that the settling time was six minutes and the system showed effective disturbance rejection. The results also demonstrate that the adaptive optimal control algorithm would achieve individualized control of mean arterial pressure in pregnant patients with no prior knowledge of patient parameters.

Adaptive Optimal Output Regulation of Time-Delay Systems via Measurement Feedback.

This brief proposes a novel solution to problems related to the measurement feedback adaptive optimal output regulation of discrete-time linear systems with input time-delay. Based on reinforcement learning and adaptive dynamic programming, an approximate optimal control policy is obtained via recursive numerical algorithms using online information. Convergence proofs for the proposed algorithms are given. Notably, the exact knowledge of the plant and the exosystem is not needed. The learned control policy is only a function of retrospective input and measurement output data. Theoretical analysis and an application to a grid-connected inverter show that the proposed methodologies serve as effective tools for solving adaptive and optimal output regulation problems.

ABT737 reverses cisplatin resistance by targeting glucose metabolism of human ovarian cancer cells.

The poor prognosis and high mortality of patients with ovarian cancer result in part from their poor response to platinum-based chemotherapy. However, the precise mechanism behind cisplatin resistance is still not fully understood. In the present study, the authors explored the mechanism of resistance to cisplatin from the perspective of glucose metabolism in human ovarian cancer. The experiments using genetically matched ovarian cancer cell lines SKOV3 (cisplatin-sensitive) and SKOV3/DDP (cisplatin-resistant) in the present study provided some important findings. First, in comparison to SKOV3 cells, SKOV3/DDP cells exhibited decreased dependence on aerobic glycolysis and an increased demand for glucose. Secondly, the stable overexpression of Bcl­2 and ability to shift metabolism towards oxidative phosphorylation (OXPHOS) in SKOV3/DDP cells were associated with increased oxygen consumption. Furthermore, the metabolic characteristic of elevated OXPHOS primarily comprised most mitochondrial­derived reactive oxygen species (ROS) and, at least in part, contributed to the slight pro-oxidant state of SKOV3/DDP cells in turn. Thirdly, SKOV3/DDP cells reset the redox balance by overexpressing the key enzyme glucose 6-phosphate dehydrogenase (G6PD) of the pentose phosphate pathway to eliminate the cytotoxicity of highly elevated ROS. Furthermore, the inhibition of Bcl­2 reduced the OXPHOS and sensitivity of SKOV3/DDP cells to cisplatin in a selective manner. Furthermore, when combined with 2-deoxyglucose (2-DG), the anticancer effect of the Bcl­2 inhibitor ABT737 was greatly potentiated and hypoxia-inducible factor 1α (HIF­1α) appeared to be closely associated with Bcl­2 family members in the regulation of glucose metabolism. These results suggested that the special glucose metabolism in SKOV3/DDP cells might be selectively targeted by disrupting Bcl­2-dependent OXPHOS.

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

SIRT3 aggravates metformin-induced energy stress and apoptosis in ovarian cancer cells.

Increasing evidence suggests that mitochondrial respiratory chain complex I participates in carcinogenesis and cancer progression by providing energy and maintaining mitochondrial function. However, the role of complex I in ovarian cancer is largely unknown. In this study we showed that metformin, considered to be an inhibitor of complex I, simultaneously inhibited cell growth and induced mitochondrial-related apoptosis in human ovarian cancer cells. Metformin interrupted cellular energy metabolism mainly by causing damage to complex I that impacted mitochondrial function. Additionally, treatment with metformin increased the activation of sirtuin 3 (SIRT3), a mitochondrial deacetylase. We demonstrated that SIRT3 overexpression aggravated metformin-induced apoptosis, energy stress and mitochondrial dysfunction. Moreover, treatment with metformin or SIRT3 overexpression increased activation of AMP-activated protein kinase (AMPK), a major sensor of cellular energy status. AMPK compensated for energy loss by increasing glycolysis. The impact of this was assessed by reducing glucose levels in the media or by using inhibitors (2-deoxyglucose, Compound C) of glycolysis and AMPK. The combination of these factors with metformin intensified cytotoxicity through further downregulation of ATP. Our study outlines an important role for SIRT3 in the antitumor effect of mitochondrial complex I inhibitors in human ovarian cancer cells. This effect appears to be mediated by induction of energy stress and apoptosis. Strategies that target the mitochondria could be enhanced by modulating glycolysis to further aggravate energy stress that may increase the antitumor effect.

TAT­fused IP3R­derived peptide enhances cisplatin sensitivity of ovarian cancer cells by increasing ER Ca2+ release.

Ovarian cancer is the most common gynecological malignancy. At present, cisplatin is used to treat ovarian cancer; however, the development of cisplatin resistance during therapy is a common obstacle to achieving favorable outcomes. Recently, the B­cell lymphoma 2 (Bcl­2) BH4 domain has been reported to mediate the prosurvival activity of Bcl­2 in cancer; however, the involvement of the BH4 domain of Bcl­2 in the cisplatin resistance of ovarian carcinoma cells is not entirely clear. In this study, we observed the cytoplasmic and mitochondrial levels of Ca2+ by confocal laser microscopy. We also detected cell apoptosis using western blot analysis and flow cytometry. The present study demonstrated that TAT­fused inositol 1,4,5­trisphosphate receptor­derived peptide (TAT­IDPS), which targets the BH4 domain of Bcl­2, increased cisplatin­induced Ca2+ flux from the endoplasmic reticulum (ER) into the cytosol and mitochondria. In addition, TAT­IDPS increased cisplatin­induced expression of mitochondrial apoptosis­associated proteins and ER stress­associated proteins. These results indicated that TAT­IDPS may enhance the cytotoxicity of cisplatin toward ovarian carcinoma cells by increasing ER Ca2+ release.