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Curcumin, a polyphenolic compound derived from the turmeric plant (Curcuma longa), has been extensively studied for its anti-inflammatory and anti-proliferative properties. The safety and efficacy of curcumin have been thoroughly validated. Nevertheless, the underlying mechanism for treating osteoarthritis remains ambiguous. This study aims to reveal the potential mechanism of curcumin in treating osteoarthritis by using metabolomics and transcriptomics. Firstly, we validated the effect of curcumin on inflammatory factors in human articular chondrocytes. Secondly, we explored the cellular metabolism mechanism of curcumin against osteoarthritis using cell metabolomics. Thirdly, we assessed the differences in gene expression of human articular chondrocytes through transcriptomics. Lastly, to evaluate the essential targets and elucidate the potential mechanism underlying the therapeutic effects of curcumin in osteoarthritis, we conducted a screening of the proteins within the shared pathway of metabolomics and transcriptomics. Our results demonstrated that curcumin significantly decreased the levels of inflammatory markers, such as IL-β, IL-6, and TNF-α, in human articular chondrocytes. Cell metabolomics identified 106 differential metabolites, including beta-aminopropionitrile, 3-amino-2-piperidone, pyrrole-2-carboxaldehyde, and various other components. The transcriptomic analysis yielded 1050 differential mRNAs. Enrichment analysis showed that the differential metabolites and mRNAs were significantly enriched in seven pathways, including glycine, serine, and threonine metabolism; pentose and glucuronate interconversions; glycerolipid metabolism; histidine metabolism; mucin-type o-glycan biosynthesis; inositol phosphate metabolism; and cysteine and methionine metabolism. A total of 23 key targets were identified to be involved in these pathways. We speculate that curcumin may alleviate osteoarthritis by targeting key proteins involved in glycine, serine, and threonine metabolism; inhibiting pyruvate production; and modulating glycolysis., (© 2023. The Author(s).)

Network pharmacology and bioinformatics were used to study puerarin's molecular mechanism in treating osteoarthritis from the perspective of ferroptosis, revealing a new treatment target. Ferroptosis-related targets were obtained from FerrDb. Puerarin action targets were retrieved from TCMSP, Pharmmappe, SwissTargetPrediction, and Targetnet databases, and supplemented with PubMed. The gene expression profiles of GSE12021, GSE55235, and GSE82107 were obtained using "Osteoarthritis" as the search term in the GEO database, and the differential expression gene screening analysis was performed for osteoarthritis. The intersection targets between puerarin, iron death, and osteoarthritis were obtained using Venn diagrams. GO and KEGG analyses were conducted with R software. Molecular docking and visualization of puerarin and core targets were performed using Autodock Vina and PyMol software. The effects of puerarin on the cell viability and the TNFα, IL6, and Ilβ levels of human inflammation articular chondrocytes were tested in vitro experiments. Puerarin, ferroptosis, and osteoarthritis share four targets: PLIN2, PTGS2, VEGFA, and IL6. GO enrichment analysis showed that puerarin maintained the blood-brain barrier, regulated peptide serine phosphorylation, and had anti-inflammatory effects. KEGG analysis showed that puerarin's anti-inflammatory effects were mainly through VEGF, IL-17, C-type lectin receptor, HIF-1, TNF, and other signaling pathways. Puerarin closely bound PLIN2, PTGS2, VEGFA, and IL6 targets in molecular docking. In vitro, puerarin prevented osteoarthritis. Network pharmacology and bioinformatics explained puerarin's multi-target and multi-pathway treatment of OA, which may be related to ferroptosis, and confirmed its anti-inflammatory effect., (© 2023. The Author(s).)

This article presents an active disturbance rejection adaptive precision pointing controller for all-electric tank bidirectional stability system in high speed driving environment. First, a comprehensive dynamic model for mechatronics and control integration of bidirectional stability system is modeled. Second, the backstepping idea is introduced to perfectly incorporate the parameter adaptive technique and the extended state observer to design a targeted control strategy. The matched and mismatched uncertainties of the system can be estimated separately with the construction of two extended state observers, while a novel adaptive function updated simultaneously via tracking and observation errors is synthesized to deal with the parametric uncertainties. Finally, theoretical analysis and extensive comparative results verify that the proposed controller is effective., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

In this article, a novel neural network (NN)-based adaptive dynamic surface asymptotic tracking controller with guaranteed transient performance is proposed for n -degrees of freedom (DOF) hydraulic manipulators. To fulfill the work, the entire manipulator system model, including hydraulic actuator dynamics, is first established. Then, the neural adaptive dynamic surface controller is designed, in which the NN is utilized to approximate the unknown joint coupling dynamics, while the approximation error and uncertainties of the actuator dynamics are addressed by the nonlinear robust control law with adaptive gains. In addition, a modified funnel function that ensures the joint tracking errors remains within a predefined funnel boundary and is skillfully incorporated into the adaptive dynamic surface control (ADSC) design to achieve a guaranteed transient tracking performance. The theoretical analysis reveals that both the guaranteed transient tracking performance and asymptotic stability can be achieved with the proposed controller. Contrastive simulations are performed on a 2-DOF hydraulic manipulator to demonstrate the superiority of the proposed controller.

The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

In this article, the problem of high precision motion control for motor servo systems with modeling uncertainties and unknown actuator backlash is addressed. The combination of synthesized adaptive laws and continuous nonlinear robust term handles parameter uncertainties and system disturbances. The adaptive technique updates the unknown parameters of actuator backlash in real time and the backlash inverse function eliminates the backlash effect. Meanwhile, the designed controller without knowing the range of the disturbance upper bound but automatically estimates through the adaptive law, which improves the engineering practicability. Finally, the theoretical analysis proves the perfect asymptotic stability of the presented controller even with unmodeled disturbances and unknown actuator backlash. Extensive comparative experiments reveal the superiority of the presented method., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions.

This article proposes an asymptotic adaptive command filtered control approach for uncertain nonlinear systems with parametric uncertainties, mismatched and matched disturbances. To accomplish the task, a disturbance observer (DO) only with one tuning parameter is firstly used to attain the disturbance compensation. The parameter uncertainties can be addressed via composite updated laws. Then, by judiciously combining DO, adaptive control and command filter technique, a novel command filtered controller with adaptive-gain auxiliary systems is developed to attain asymptotic tracking and shun "explosion of complexity". The system stability is proved by utilizing the Lyapunov function. Extensive experimental results uncover the preponderance of the exhibited strategy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

Due to the nonlinearity and various uncertainties, the controller design for hydraulic servo systems with input constraint is more complicated and challenging. This paper first proposes an asymptotic tracking controller for electrohydraulic servomechanisms considering input constraint, parametric uncertainties, and unmodeled disturbances. The core innovation of this controller is to decouple the control input and the input nonlinearity while guaranteeing the nonlinear decoupling term and its derivative to be available and bounded. Meanwhile, the decoupling operation could be skillfully integrated to realize accurate adaptive model-based compensation while remaining the unique feature of asymptotic control for residual disturbances. For this purpose, based on the desired trajectory and the estimated disturbance via an extended state observer (ESO), a desired load pressure signal is constructed to replace the actual load pressure and accomplish the required decoupling operation. In this case, a desired adaptive feedforward compensation in combination with a robust integral of the sign of the error (RISE) feedback is proposed to attenuate parametric uncertainties and residual unmodeled disturbances, respectively. Subsequently, a smooth hyperbolic tangent function is integrated into the controller to handle the input constraint. Theoretical analysis proves that the developed control strategy can achieve semi-global asymptotic tracking performance. Besides, numerical simulations and experimental tests demonstrate that the proposed control scheme can ensure high-precision tracking performance and simultaneously satisfy the preset control input range when encountering the input constraint and modeling uncertainties., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Purpose: Despite popularity of optical coherence tomography (OCT) in glaucoma studies, it's unclear how well OCT-derived metrics compare to traditional measures of retinal ganglion cell (RGC) abundance. Here, Diversity Outbred (J:DO) mice are used to directly compare ganglion cell complex (GCC) thickness measured by OCT to metrics of retinal anatomy measured ex vivo with retinal wholemounts and optic nerve histology., Methods: J:DO mice (n = 48) underwent fundoscopic and OCT examinations, with automated segmentation of GCC thickness. RGC axons were quantified from para-phenylenediamine-stained optic nerve cross-sections and somas from BRN3A-immunolabeled retinal wholemounts, with total inner retinal cellularity assessed by TO-PRO and subsequent hematoxylin staining., Results: J:DO tissues lacked overt disease. GCC thickness, RGC abundance, and total cell abundance varied broadly across individuals. GCC thickness correlated significantly to RGC somal density (r = 0.58) and axon number (r = 0.44), but not total cell density. Retinal area and nerve cross-sectional area varied widely. No metrics were significantly influenced by sex. In bilateral comparisons, GCC thickness (r = 0.95), axon (r = 0.72), and total cell density (r = 0.47) correlated significantly within individuals., Conclusions: Amongst outbred mice, OCT-derived measurements of GCC thickness correlate significantly to RGC somal and axon abundance. Factors limiting correlation are likely both biological and methodological, including differences in retinal area that distort sampling-based estimates of RGC abundance., Translational Relevance: There are significant-but imperfect-correlations between GCC thickness and RGC abundance across genetic contexts in mice, highlighting valid uses and ongoing challenges for meaningful use of OCT-derived metrics.

Objectives: Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy., Methods: Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05., Results: Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04mm and 1.50mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89., Conclusions: This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images., Clinical Significance: Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard., (Copyright © 2022. Published by Elsevier Ltd.)

Purpose: Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axons from sampling limited areas from histologic cross sections of nerve. Both approaches are prone to variability and are time consuming. First-generation automated approaches have begun to emerge, but all have significant shortcomings. Here, we seek improvements through use of deep-learning approaches for segmenting and quantifying axons from cross-sections of mouse optic nerve., Methods: Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework., Results: From comparisons with an independent test set of images with manually marked axon centers and boundaries, both deep-learning approaches outperformed an existing baseline automated approach and similarly to two independent experts. Performance of the semisupervised approach was superior and implemented into AxonDeep., Conclusions: AxonDeep performs automated quantification and segmentation of axons from healthy-appearing nerves and those with mild to moderate degrees of damage, similar to that of experts without the variability and constraints associated with manual performance., Translational Relevance: Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.

This paper presents an output feedback adaptive super-twisting sliding mode controller (SSMC) for hydraulic systems with unmodeled disturbances via utilizing an extended state observer (ESO). Both unmeasured system states and unmodeled disturbances are estimated by ESO based on output position signal, which avoids using noise-polluted signals and eliminates most of the disturbance effects on control performance simultaneously. Moreover, a SSMC is developed to further suppress the residual error of disturbance compensation, in which feedback gains are adapted online to further reduce the high-gain feedback. In addition, this proposed controller is continuous and chattering-free, which is beneficial to practical applications. Theoretical analysis indicates that the proposed controller ensures an asymptotic stability when existing constant disturbances, and ultimately bounded tracking performance for the time-variant disturbance case. Comparative experimental results reveal the validity of the developed approach., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

Purpose: To define the temporal relationship of vascular versus neuronal abnormalities in radiation retinopathy., Methods: Twenty-five patients with uveal melanoma treated with brachytherapy and sixteen controls were tested. Functional outcome measures included visual acuity and threshold perimetry (HVF 10-2), while structural outcomes included retinal thickness by OCT and vascular measures by OCT angiography and digital fundus photography. The degree of structural abnormality was determined by intereye asymmetry compared with normal subject asymmetry. Diagnostic sensitivity and specificity of each measure were determined using receiver operating characteristic curves. The relationships between the outcome measures were quantified by Spearman correlation. The effect of time from brachytherapy on visual function, retinal layer thickness, and capillary density was also determined., Results: Within the first 2 years of brachytherapy, outcome measures revealed visual field loss and microvascular abnormalities in 38% and 31% of subjects, respectively. After 2 years, they became more prevalent, increasing to 67% and 67%, respectively, as did retinal thinning (50%). Visual field loss, loss of capillary density, and inner retinal thickness were highly correlated with one another. Diagnostic sensitivity and specificity were highest for abnormalities in digital fundus photography, visual field loss within the central 10°, and decrease in vessel density., Conclusions: Using quantitative approaches, radiation microvasculopathy and visual field defects were detected earlier than loss of inner retinal structure after brachytherapy. Strong correlations eventually developed between vascular pathology, change in retinal thickness, neuronal dysfunction, and radiation dose. Radiation-induced ischemia seems to be a primary early manifestation of radiation retinopathy preceding visual loss.

It is well known that hydraulic systems typically suffer from heavy disturbances including parametric uncertainties and unknown disturbances. In order to attain high performance tracking control, this paper proposes a composite design of nonlinear neural-networks (NN) and continuous robust integral of the sign of the error (RISE) feedback controller. The control development incorporates a NN feedforward component to have a compensation for unknown state-dependent disturbances and to further improve the accuracy of feedforward compensation, meanwhile input parameter is updated online. To achieve asymptotic stability, a novel RISE term with NN-based feedforward component is developed for the first time to enable the incorporation of model reference adaptive control structure where acceleration signal is not employed. The proposed controller guarantees controlled hydraulic system a semi-global asymptotic stability. For the experimental results, the prescribed transient performance is tested under rectangular trajectory and the steady state performance is tested under sinusoidal trajectory., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme., (Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper proposes a continuous adaptive integral robust control with robust integral of the sign of the error (RISE) feedback for a class of uncertain nonlinear systems, in which the RISE feedback gain is adapted online to ensure the robustness against disturbances without the prior bound knowledge of the additive disturbances. In addition, an adaptive compensation integrated with the proposed adaptive RISE feedback term is also constructed to further reduce design conservatism when the system also exists parametric uncertainties. Lyapunov analysis reveals the proposed controllers could guarantee the tracking errors are asymptotically converging to zero with continuous control efforts. To illustrate the high performance nature of the developed controllers, numerical simulations are provided. At the end, an application case of an actual electromechanical servo system driven by motor is also studied, with some specific design consideration, and comparative experimental results are obtained to verify the effectiveness of the proposed controllers., (Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.)

In the paper, the production of wood vinegar from Chinese fir sawdust (FS), cotton stalk (CS) and bamboo sawdust (BS) by carbonization process was addressed. The wood vinegar yield was investigated and the organic compounds contained were determined by gas chromatography and mass spectrometry. It was found that the refined wood vinegar yield of FS increased firstly and then decreased with increasing carbonization temperature and the highest yield reached about 25% in 350-450°C. The relative contents of acids and ketones from FS decreased and that of phenols increased with increasing temperature. The relative contents of acids and phenols in the wood vinegars produced from the samples were in the order of BS>CS>FS and that of ketones reversed. KCl solution treatment caused a decrease in the relative contents of the phenols and ketones, but an increase in that of the acids in FS wood vinegar., (Copyright © 2014 Elsevier Ltd. All rights reserved.)