Hybrid condition monitoring of nonlinear mechatronic system using biogeography-based optimization particle filter and optimized extreme learning machine.

This paper proposes a hybrid condition monitoring approach, which integrates bond graph model-based diagnostic technique and data-driven remaining useful life (RUL) prediction, for a nonlinear mechatronic system. In this approach, various degrading faults can be considered and the physical degradation model is not required for RUL prediction. Firstly, an integrated fault signature matrix is proposed by the causal path of bicausal-bond graph model to improve fault isolation performance. After that, a biogeography-based optimization (BBO)-particle filter is developed for fault identification. For prognosis, an optimized extreme learning machine (OELM) is proposed where the hidden layer biases and input weights are optimized by BBO. The fault identification results provide data set to train the OELM for prognosis. Finally, the effectiveness of the approach is verified by simulation and experiment results.

Inhibition of bacterial adherence to biomaterials by coating antimicrobial peptides with anionic surfactant.

Medical devices are widely used in modern medicine, but their utilities are often limited by the biofilm formation of bacteria that are tolerant to most antibiotics. In this report, antimicrobial peptides (AMPs) were coated onto biomaterials by the aid of surfactant through hydrophobic interactions. To increase the coating efficiency, stability of AMPs in body fluids and spectrum of antimicrobial activity, pairs of AMPs were coated simultaneously onto various substrates, such as silicone, polyurethane and titanium, which are commonly used components of biomedical devices. These coated AMPs exhibited very low cytotoxicity and hemolytic activities because they were gradually released into urine or serum. The AMP pairs, such as T9W + SAAP159 and T9W + RRIKA, coated onto the silicone discs were able to inhibit in vitro bacterial adherence in urine. Most importantly, AMP pairs coated onto the silicone tubing by surfactant SDBS could prevent bacterial adherence to mouse bladder and the silicone tubing implanted within it. These results provide a promising approach towards circumventing urinary catheter-associated infections caused by bacterial adherence.

Obesity-induced overexpression of miR-802 impairs insulin transcription and secretion.

B cell dysfunction due to obesity can be associated with alterations in the levels of micro-RNAs (miRNAs). However, the role of miRNAs in these processes remains elusive. Here, we show that miR-802 is increased in the pancreatic islets of obese mouse models and demonstrate that inducible transgenic overexpression of miR-802 in mice causes impaired insulin transcription and secretion. We identify Foxo1 as a transcription factor of miR-802 promoting its transcription, and NeuroD1 and Fzd5 as targets of miR-802-dependent silencing. Repression of NeuroD1 in ß cell and primary islets impairs insulin transcription and reduction of Fzd5 in ß cell, which, in turn, impairs Ca2+ signaling, thereby repressing calcium influx and decreasing insulin secretion. We functionally create a novel network between obesity and ß cell dysfunction via miR-802 regulation. Elucidation of the impact of obesity on microRNA expression can broaden our understanding of pathophysiological development of diabetes.

Anionic Surfactant-Facilitated Coating of Antimicrobial Peptide and Antibiotic Reduces Biomaterial-Associated Infection.

Medical devices are widely used in modern medicine, but the high prevalence of biomaterial-associated infections still presents a major problem. Especially problematic is the formation of biofilms that are tolerant to most antibiotics. In this report, antimicrobial peptides (AMPs) were driven into an amphipathic structure by anionic surfactant. To increase the coating efficacy and spectrum of antimicrobial activity, the AMPs were coated simultaneously with antibiotic, Polymyxin B, by surfactant onto polystyrene, silicone, polyurethane, and titanium which are commonly used with biomedical devices. These coated antimicrobials stably adhered to the substrate and were gradually released into urine and serum. They exhibited high bactericidal activity, but low cytotoxicity and hemolytic activity. Most importantly, the antimicrobials coated onto silicone tubing inhibited the planktonic growth of E. coli in mouse urine and also markedly prevented bacterial adherence to the bladder and the silicone tubing implanted in the bladder. These results provide a promising approach to circumvent catheter-associated infections due to bacterial adherence.

Neural-Network-Based Adaptive Event-triggered Control for Spacecraft Attitude Tracking.

The problem of attitude tracking control for spacecraft with limited communication rate is addressed in this article. To reduce the communication burden, an adaptive event-triggered control scheme is proposed. In the control scheme, only the sampling states at the event-triggering instants are sent to the control module, which can considerably decrease the data transmission rate. To address the inertia uncertainties and external disturbances, a radial basis function neural network (NN) is introduced. The bound of the uncertainties and disturbances is estimated for the proposed control scheme, which can simplify the NN and reduce the computation. Since the event-triggered error signal is discontinuous due to the event-triggered mechanism, the closed-loop system is formulated as an impulsive dynamical system to obtain the stability properties of the system. Finally, simulation results are given to demonstrate the effectiveness of the proposed control scheme.

Therapeutic Mechanisms of Herbal Medicines Against Insulin Resistance: A Review.

Insulin resistance is a condition in which insulin sensitivity is reduced and the insulin signaling pathway is impaired. Although often expressed as an increase in insulin concentration, the disease is characterized by a decrease in insulin action. This increased workload of the pancreas and the consequent decompensation are not only the main mechanisms for the development of type 2 diabetes (T2D), but also exacerbate the damage of metabolic diseases, including obesity, nonalcoholic fatty liver disease, polycystic ovary syndrome, metabolic syndrome, and others. Many clinical trials have suggested the potential role of herbs in the treatment of insulin resistance, although most of the clinical trials included in this review have certain flaws and bias risks in their methodological design, including the generation of randomization, the concealment of allocation, blinding, and inadequate reporting of sample size estimates. These studies involve not only the single-flavored herbs, but also herbal formulas, extracts, and active ingredients. Numerous of in vitro and in vivo studies have pointed out that the role of herbal medicine in improving insulin resistance is related to interventions in various aspects of the insulin signaling pathway. The targets involved in these studies include insulin receptor substrate, phosphatidylinositol 3-kinase, glucose transporter, AMP-activated protein kinase, glycogen synthase kinase 3, mitogen-activated protein kinases, c-Jun-N-terminal kinase, nuclear factor-kappaB, protein tyrosine phosphatase 1B, nuclear factor-E2-related factor 2, and peroxisome proliferator-activated receptors. Improved insulin sensitivity upon treatment with herbal medicine provides considerable prospects for treating insulin resistance. This article reviews studies of the target mechanisms of herbal treatments for insulin resistance.

Effects of Shuganjianpihuatanxingqi decoction on mild subclinical hypothyroidism: A SPIRIT compliant randomized controlled trial study protocol.

INTRODUCTION: Mild subclinical hypothyroidism (SCH) can cause depression, fatigue, cognitive dysfunction, or other hypothyroid symptoms, and even progress to hypothyroidism. The treatment of mild SCH is controversial. Shuganjianpihuatanxingqi decoction (SD) is a frequently prescribed Chinese herbal medicine in patients with mild SCH. However, scientific evidence is needed to confirm the therapeutic effect of SD. METHODS AND ANALYSIS: This study is a randomized, double-blind, and controlled clinical trial. A total of 228 participants with the diagnosis of mild SCH will be randomly assigned to the SD or placebo group in a ratio of 1:1. Participants will receive treatment for 12 weeks and undergo 12-month follow-up. The primary outcome measure is the thyroid-stimulating hormone level, and secondary outcomes will be the differences in the results of Thyroid-related Quality of Life Questionnaire, blood lipids, and Traditional Chinese Medicine Symptom Score Scale between baseline and at 12 weeks after intervention. ETHICS AND DISSEMINATION: The study has been approved by Guang'anmen Hospital of China Academy of Chinese Medical Sciences (no.2018-005-ky-01). The trial results will be published via peer-reviewed journals and the Clinical Research Information Service. TRIAL REGISTRATION NUMBER: ChiCTR1800015781 (approval date: 20 April 2018).

Therapeutic Potential of Ginsenosides as an Adjuvant Treatment for Diabetes.

Ginseng, one of the oldest traditional Chinese medicinal herbs, has been used widely in China and Asia for thousands of years. Ginsenosides extracted from ginseng, which is derived from the roots and rhizomes of Panax ginseng C. A. Meyer, have been used in China as an adjuvant in the treatment of diabetes mellitus. Owing to the technical complexity of ginsenoside production, the total ginsenosides are generally extracted. Accumulating evidence has shown that ginsenosides exert antidiabetic effects. In vivo and in vitro tests revealed the potential of ginsenoside Rg1, Rg3, Rg5, Rb1, Rb2, Rb3, compound K, Rk1, Re, ginseng total saponins, malonyl ginsenosides, Rd, Rh2, F2, protopanaxadiol (PPD) and protopanaxatriol (PPT)-type saponins to treat diabetes and its complications, including type 1 diabetes mellitus, type 2 diabetes mellitus, diabetic nephropathy, diabetic cognitive dysfunction, type 2 diabetes mellitus with fatty liver disease, diabetic cerebral infarction, diabetic cardiomyopathy, and diabetic erectile dysfunction. Many effects are attributed to ginsenosides, including gluconeogenesis reduction, improvement of insulin resistance, glucose transport, insulinotropic action, islet cell protection, hepatoprotective activity, anti-inflammatory effect, myocardial protection, lipid regulation, improvement of glucose tolerance, antioxidation, improvement of erectile dysfunction, regulation of gut flora metabolism, neuroprotection, anti-angiopathy, anti-neurotoxic effects, immunosuppression, and renoprotection effect. The molecular targets of these effects mainly contains GLUTs, SGLT1, GLP-1, FoxO1, TNF-α, IL-6, caspase-3, bcl-2, MDA, SOD, STAT5-PPAR gamma pathway, PI3K/Akt pathway, AMPK-JNK pathway, NF-κB pathway, and endoplasmic reticulum stress. Rg1, Rg3, Rb1, and compound K demonstrated the most promising therapeutic prospects as potential adjuvant medicines for the treatment of diabetes. This paper highlights the underlying pharmacological mechanisms of the anti-diabetic effects of ginsenosides.

Robust Recurrent Kernel Online Learning.

We propose a robust recurrent kernel online learning (RRKOL) algorithm based on the celebrated real-time recurrent learning approach that exploits the kernel trick in a recurrent online training manner. The novel RRKOL algorithm guarantees weight convergence with regularized risk management through the use of adaptive recurrent hyperparameters for superior generalization performance. Based on a new concept of the structure update error with a variable parameter length, we are the first one to propose the detailed structure update error, such that the weight convergence and robust stability proof can be integrated with a kernel sparsification scheme based on a solid theoretical ground. The RRKOL algorithm automatically weighs the regularized term in the recurrent loss function, such that we not only minimize the estimation error but also improve the generalization performance through sparsification with simulation support.

A new biocompatible microemulsion increases extraction yield and bioavailability of Andrographis paniculata.

The purpose of this study was to design and prepare a biocompatible microemulsion of Andrographis paniculata (BMAP) containing both fat-soluble and water-soluble constituents. We determined the contents of active constituents of BMAP and evaluated its bioavailability. The biocompatible microemulsion (BM), containing lecithin and bile salts, was optimized in the present study, showing a good physical stability. The mean droplet size was 19.12 nm, and the average polydispersity index (PDI) was 0.153. The contents of andrographolide and dehydroandrographolide in BMAP, as determined by high performance liquid chromatography (HPLC), were higher than that in ethanol extraction. The pharmacokinetic results of BMAP showed that the AUC0-7 and AUC0→∞ values of BMAP were 2.267 and 27.156 µg·mL(-1)·h(-1), respectively, and were about 1.41-fold and 6.30-fold greater than that of ethanol extraction, respectively. These results demonstrated that the bioavailability of and rographolide extracted by BMAP was significantly higher than that extracted by ethanol. In conclusion, the BMAP preparation displayed ann improved dose form for future clinical applications.

Enhanced Data-Driven Optimal Terminal ILC Using Current Iteration Control Knowledge.

In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain, which is a function about the system partial derivative with respect to the time-varying control input. In addition, a parameter updating law is designed to estimate the unknown partial derivatives iteratively. The input signals of the proposed E-DDOTILC are time-varying and updated utilizing not only the terminal tracking error of the previous run but also the input signals of the previous time instants in the current iteration. The proposed approach is a data-driven control strategy and only the I/O data are required for the controller design and analysis. The monotonic convergence and effectiveness of the proposed approach is further verified by both the rigorous mathematical analysis and the simulation results.

Sparse extreme learning machine for classification.

Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM.

Microarray data classification using the spectral-feature-based TLS ensemble algorithm.

The reliable and accurate identification of cancer categories is crucial to a successful diagnosis and a proper treatment of the disease. In most existing work, samples of gene expression data are treated as one-dimensional signals, and are analyzed by means of some statistical signal processing techniques or intelligent computation algorithms. In this paper, from an image-processing viewpoint, a spectral-feature-based Tikhonov-regularized least-squares (TLS) ensemble algorithm is proposed for cancer classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of the atoms of a dictionary. Two types of dictionaries, namely singular value decomposition (SVD)-based eigenassays and independent component analysis (ICA)-based eigenassays, are proposed for the TLS model, and both are extracted via a two-stage approach. The proposed algorithm is inspired by our finding that, among these eigenassays, the categories of some of the testing samples can be assigned correctly by using the TLS models formed from some of the spectral features, but not for those formed from the original samples only. In order to retain the positive characteristics of these spectral features in making correct category assignments, a strategy of classifier committee learning (CCL) is designed to combine the results obtained from the different spectral features. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.

"Whole Chinese angelica" microemulsion: its preparation and in vivo and in vitro evaluations.

CONTEXT: The difference between the chemical polarities of the two categories of active chemical constituents in Chinese angelica volatile oil (CAVO) and Chinese angelica water extract (CAWE) greatly limit the development and clinical application of Chinese angelica preparation. OBJECT: The aim of this study is to design and prepare a "whole Chinese angelica" microemulsion (WCAM) that contains both CAVO and CAWE and at the same time to evaluate it in vivo and in vitro. MATERIALS AND METHODS: CAVO and CAWE extracted from Chinese angelica were used as the oil and aqueous phases, respectively, to prepare the WCAM; its physicochemical property was observed, and its drug potency and oral bioavailability were evaluated. RESULTS: The formula of the WCAM was optimized as Tween-80:ethanol:CAVO:CAWE = 3:3:1:40. The droplet size of the WCAM was 72.64 nm and the WCAM was proved to be physicochemically stable when it was kept at 0 °C, 4 °C, 25 °C and 40 °C for 3 months. The WCAM could markedly prolong blood clotting time, decrease whole blood viscosity and whole blood reduced viscosity at different shear rates, and improve hemorheological parameters. The results of the pharmacokinetic evaluation show that the AUC0-7 of the WCAM was 4510.66 and was about 4.41-fold compared to that of danggui concentrated pills (an existing Chinese angelica pharmaceutical preparation). CONCLUSION: It can be concluded, that the WCAM is a promising Chinese angelica preparation that has great prospects in the treatment of dysmenorrhea and irregular menstruation.

CPG-inspired workspace trajectory generation and adaptive locomotion control for quadruped robots.

This paper deals with the locomotion control of quadruped robots inspired by the biological concept of central pattern generator (CPG). A control architecture is proposed with a 3-D workspace trajectory generator and a motion engine. The workspace trajectory generator generates adaptive workspace trajectories based on CPGs, and the motion engine realizes joint motion imputes. The proposed architecture is able to generate adaptive workspace trajectories online by tuning the parameters of the CPG network to adapt to various terrains. With feedback information, a quadruped robot can walk through various terrains with adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture. A comparison by experiments shows the superiority of the proposed method against the traditional CPG-joint-space control method.

Wavelet transform-based frequency tuning ILC.

In this paper, a discrete wavelet transform-based cutoff frequency tuning method is proposed and experimental investigation is reported. In the method, discrete wavelet packet algorithm, as a time-frequency analysis tool, is employed to decompose the tracking error into different frequency regions so that the maximal error component can be identified at any time step. At each time step, the passband of the filter is from zero to the upper limit of frequency region where the maximal error component resides. Hence, the filter is a function of time as well as index of cycle. The experimental results show that this method can suppress higher frequency error components at proper time steps. While at the time steps where the major tracking error falls into lower frequency range, the cutoff frequency of the filter is set lower to reduce the influence of noises and uncertainties. This way, learning transient and long-term stability can be improved.

A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance.

Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.