Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue

Generation and Role of Oscillatory Contractions in Mouse Airway Smooth Muscle

Background/Aims: Tetraethylammonium chloride (TEA) induces oscillatory contractions in mouse airway smooth muscle (ASM); however, the generation and maintenance of oscillatory contractions and their role in ASM are unclear. Methods: In this study, oscillations of ASM contraction and intracellular Ca2+ were measured using force measuring and Ca2+ imaging technique, respectively. TEA, nifedipine, niflumic acid, acetylcholine chloride, lithium chloride, KB-R7943, ouabain, 2-Aminoethoxydiphenyl borate, thapsigargin, tetrodotoxin, and ryanodine were used to assess the mechanism of oscillatory contractions. Results: TEA induced depolarization, resulting in activation of L-type voltage-dependent Ca2+ channels (LVDCCs) and voltage-dependent Na+ (VNa) channels. The former mediated Ca2+ influx to trigger a contraction and the latter mediated Na+ entry to enhance the contraction via activating LVDCCs. Meanwhile, increased Ca2+-activated Cl- channels, inducing depolarization that resulted in contraction through LVDCCs. In addition, the contraction was enhanced by intracellular Ca2+ release from Ca2+ stores mediated by inositol (1,4,5)-trisphosphate receptors (IP3Rs). These pathways together produce the contractile phase of the oscillatory contractions. Furthermore, the increased Ca2+ activated the Na+-Ca2+ exchanger (NCX), which transferred Ca2+ out of and Na+ into the cells. The former induced relaxation and the latter activated Na+/K+-ATPase that induced hypopolarization to inactivate LVDCCs causing further relaxation. This can also explain the relaxant phase of the oscillatory contractions. Moreover, the depolarization induced by VNa channels and NCX might be greater than the hypopolarization caused by Na+/K+-ATPase alone, inducing LVDCC activation and resulting in further contraction. Conclusions: These data indicate that the TEA-induced oscillatory contractions were cooperatively produced by LVDCCs, VNa channels, Ca2+-activated Cl- channels, NCX, Na+/K+ ATPase, IP3Rs-mediated Ca2+ release, and extracellular Ca2+

Semen cassiae Extract Inhibits Contraction of Airway Smooth Muscle

β2-adrenoceptor agonists are commonly used as bronchodilators to treat obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (COPD), however, they induce severe side effects. Therefore, developing new bronchodilators is essential. Herbal plants were extracted and the extracts’ effect on airway smooth muscle (ASM) precontraction was assessed. The ethyl alcohol extract of semen cassiae (EESC) was extracted from Semen cassia. The effects of EESC on the ACh- and 80 mM K+-induced sustained precontraction in mouse and human ASM were evaluated. Ca2+ permeant ion channel currents and intracellular Ca2+ concentration were measured. HPLC analysis was employed to determine which compound was responsible for the EESC-induced relaxation. The EESC reversibly inhibited the ACh- and 80 mM K+-induced precontraction. The sustained precontraction depends on Ca2+ influx, and it was mediated by voltage-dependent L-type Ca2+ channels (LVDCCs), store-operated channels (SOCs), TRPC3/STIM/Orai channels. These channels were inhibited by aurantio-obtusin, one component of EESC. When aurantio-obtusin removed, EESC’s action disappeared. In addition, aurantio-obtusin inhibited the precontraction of mouse and human ASM and intracellular Ca2+ increases. These results indicate that Semen cassia-contained aurantio-obtusin inhibits sustained precontraction of ASM via inhibiting Ca2+-permeant ion channels, thereby, which could be used to develop new bronchodilators

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A 2D system approach to the design of a robust modified repetitive-control system with a dynamic output-feedback controller

This paper is concerned with the problem of designing a robust modified repetitive-control system with a dynamic output feedback controller for a class of strictly proper plants. Employing the continuous lifting technique, a continuous-discrete two-dimensional (2D) model is built that accurately describes the features of repetitive control. The 2D control input contains the direct sum of the effects of control and learning, which allows us to adjust control and learning preferentially. The singular-value decomposition of the output matrix and Lyapunov stability theory are used to derive an asymptotic stability condition based on a Linear Matrix Inequality (LMI). Two tuning parameters in the LMI manipulate the preferential adjustment of control and learning. A numerical example illustrates the tuning procedure and demonstrates the effectiveness of the method

A 2D system approach to the design of a robust modified repetitive-control system with a dynamic output-feedback controller

This paper is concerned with the problem of designing a robust modified repetitive-control system with a dynamic output feedback controller for a class of strictly proper plants. Employing the continuous lifting technique, a continuous-discrete two-dimensional (2D) model is built that accurately describes the features of repetitive control. The 2D control input contains the direct sum of the effects of control and learning, which allows us to adjust control and learning preferentially. The singular-value decomposition of the output matrix and Lyapunov stability theory are used to derive an asymptotic stability condition based on a Linear Matrix Inequality (LMI). Two tuning parameters in the LMI manipulate the preferential adjustment of control and learning. A numerical example illustrates the tuning procedure and demonstrates the effectiveness of the method

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics

Control-Force Spectrum Considering Both Natural Period and Damping Ratio for Active Base-Isolated Building

The active structural control (ASC) has been applied to base-isolated buildings to achieve a high-damping system. The critical step for designing an ASC system is selecting control parameters and isolation parameters that satisfy the design restrictions. However, the conventional methods are limited in theoretically estimating the maximum control force, which requires great demand for trial-and-error approaches and numerical simulations. This paper constructed the equivalent model of the feedback control system that theoretically expresses the dependence of vibration characteristics (natural period and damping ratio) of the control system on the feedback gain. Then, the control-force spectrum is proposed that estimates the maximum control force for a feedback control system, adjusting both the natural period and damping ratio of the control system. The maximum responses and control force are estimated without additional numerical simulations and trial-and-error approaches using the equivalent model and control-force spectrum. Moreover, a design method was devised for determining the allowance range of the vibration characteristics of structures (damping ratio and natural period) and controllers that satisfy the design limitations (maximum responses and maximum control force). The design method does not require trial-and-error and numerical simulations, thus simplifying the design procedure. Finally, this paper uses numerical examples and a design example to verify the validity of the control-force spectrum and design method

Iterative Learning Control for Nonlinear Weighing and Feeding Process

Due to the nonlinear dynamics in weighing and feeding process, it is difficult to achieve high accuracy with conventional control methods. This paper uses a piecewise linearization method for the nonlinear problem and discusses the application of iterative learning control in weighing and feeding process. First, the nonlinear problem and the repeatability are discussed based on dynamic analysis of weighing and feeding process. Next, a linear state space model is established with a piecewise linearization method. Then, an iterative learning controller is presented by utilizing repetitive characteristics, and the controller parameters are obtained by using a multi-objective optimization method. Finally, simulation results show that the presented control method improves the control performances and the accuracy of feeding