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Abstract Targeted degradation of proteins has emerged as a powerful method for modulating protein homeostasis. Identification of suitable degraders is essential for achieving effective protein degradation. Here, we present a non-covalent degrader construction strategy, based on a modular supramolecular co-assembly system consisting of two self-assembling peptide ligands that bind cell membrane receptors and the protein of interest simultaneously, resulting in targeted protein degradation. The developed lysosome-targeting co-assemblies (LYTACAs) can induce lysosomal degradation of extracellular protein IL-17A and membrane protein PD-L1 in several scavenger receptor A-expressing cell lines. The IL-17A-degrading co-assembly has been applied in an imiquimod-induced psoriasis mouse model, where it decreases IL-17A levels in the skin lesion and alleviates psoriasis-like inflammation. Extending to asialoglycoprotein receptor-related protein degradation, LYTACAs have demonstrated the versatility and potential in streamlining degraders for extracellular and membrane proteins.

ABSTRACT Antibiotic tolerance within a biofilm community presents a serious public health challenge. Here, we report the identification of a 2-aminoimidazole derivative that inhibits biofilm formation by two pathogenic Gram-positive bacteria, Streptococcus mutans and Staphylococcus aureus. In S. mutans, the compound binds to VicR, a key response regulator, at the N-terminal receiver domain, and concurrently inhibits expression of vicR and VicR-regulated genes, including the genes that encode the key biofilm matrix producing enzymes, Gtfs. The compound inhibits S. aureus biofilm formation via binding to a Staphylococcal VicR homolog. In addition, the inhibitor effectively attenuates S. mutans virulence in a rat model of dental caries. As the compound targets bacterial biofilms and virulence through a conserved transcriptional factor, it represents a promising new class of anti-infective agents that can be explored to prevent or treat a host of bacterial infections. IMPORTANCE Antibiotic resistance is a major public health issue due to the growing lack of effective anti-infective therapeutics. New alternatives to treat and prevent biofilm-driven microbial infections, which exhibit high tolerance to clinically available antibiotics, are urgently needed. We report the identification of a small molecule that inhibits biofilm formation by two important pathogenic Gram-positive bacteria, Streptococcus mutans and Staphylococcus aureus. The small molecule selectively targets a transcriptional regulator leading to attenuation of a biofilm regulatory cascade and concurrent reduction of bacterial virulence in vivo. As the regulator is highly conserved, the finding has broad implication for the development of antivirulence therapeutics that selectively target biofilms.

IntroductionThis study aimed to investigate the relationships between perceived teacher-student relationship, growth mindset, student engagement, and foreign language enjoyment (FLE) among Chinese English learners.MethodsA total of 413 Chinese EFL learners participated in the study and completed self-report measures for perceived teacher-student relationship, growth mindset, student engagement in foreign language learning, and FLE. Confirmatory factor analysis was employed to assess the validity of the scales. Structural equation modeling was used to test the hypothesized model.ResultsThe partial mediation model demonstrated the best fit to the data. The results indicated that perceived teacher-student relationship had a direct impact on student engagement. FLE directly influenced student engagement, while growth mindset indirectly affected student engagement through the mediation of FLE.DiscussionThe findings suggest that fostering positive teacher-student relationships and promoting a growth mindset can enhance FLE, leading to increased levels of student engagement. These results emphasize the importance of considering both the interpersonal dynamics between teachers and students and the role of mindset in foreign language learning.

As learner burnout and disengagement affect the functioning and performance of college learners and can also influence future career-related well-being, they can be an issue for higher education organizations. Conversely, the discipline of language education has experienced an emotional turn, primarily triggered by Positive Psychology, and the scholars and students have been affected by various emotions. One of the seldom mentioned constructive emotions concerning learners’ disengagement and burnout is enjoyment in learning a foreign language, as has been demonstrated by literature reviews. It is important to note that this review helps scholastic institutions and policymakers in the scholastic community to take into consideration the role of constructive emotions, specifically enjoyment, and their constructive influence on language education in diminishing learners’ challenges in the learning process.

The Chazi geothermal field area is located in the large region of Shigatse in southern Tibet. The geothermal resources in this area are abundant, but their exploitation and utilization are low. By studying the water chemistry and isotope characteristics of geochemical fluids in the study area, information on water chemistry, heat storage temperature, recharge source, recharge elevation, and circulation depth was obtained. These results provide a scientific theoretical basis for improving the genetic mechanism of high-temperature geothermal systems in the study area. The type of geothermal fluid hydrochemicals in this area is mainly HCO3–Na. The isotopic geochemical method was used to determine that the recharge source of geothermal fluids was atmospheric precipitation, and the recharge elevation was 5200–6000 m. The geochemical thermometer, Na–K–Mg equilibrium diagram, and silica-enthalpy mixed model indicated the shallow and deep thermal storage temperatures of approximately 150 and 200°C, respectively, and the average circulation depth of 1163.38 m in the study area. Combined with the fracture structure and magmatic activity characteristics of the southwest Qinghai-Tibet Plateau, the source, storage, cover, and general situation of the area were preliminarily summarised, and the conceptual model of geothermal origin was established. The results can provide a scientific theoretical basis for the mechanism of high-temperature geothermal systems and subsequent drilling and resource development.

Dehydration treatment is commonly applied to treat intracranial hypertension. We previously utilized electrical impedance tomography (EIT) for noninvasively monitoring the efficacy of dehydration treatments and found it could provide references for the rational use of dehydrating drugs. In addition to brain tissues, scalp undergoes dehydration and can cause impedance changes, which may hamper the monitoring results of intracranial dehydration through EIT. To address this problem, we first conducted simulation and animal experiments to evaluate the impact of scalp dehydration on the monitoring results of EIT during dehydration treatments. With the priori of the spatial distribution of brain structures and the statistical results of the contribution of scalp dehydration to the intracranial reconstructed values of the EIT images obtained from animal experiments, we then proposed a weighting algorithm to reduce the impact of scalp dehydration. The experimental results showed that scalp impedance changes would to some extent reduce the accuracy of EIT in imaging intracranial resistive perturbation and account for 16.7% in overall changes of EIT signal. With the modified algorithm, 69.6% of reconstructed values of scalp dehydration in EIT images were eliminated. And the image contrast (IC) of reconstructed EIT images was improved by 21.0%. Therefore, we conclude that scalp dehydration influences the accuracy of EIT in reflecting intracranial dehydration and our proposed method can reduce the impacts of scalp dehydration.

Abstract Background Electrical impedance tomography (EIT) is a noninvasive, radiation-free, and low-cost imaging modality for monitoring the conductivity distribution inside a patient. Nowadays, time-difference EIT (tdEIT) is used extensively as it has fast imaging speed and can reflect the dynamic changes of diseases, which make it attractive for a number of medical applications. Moreover, modeling errors are compensated to some extent by subtraction of voltage measurements collected before and after the change. However, tissue conductivity varies with frequency and tdEIT does not efficiently exploit multi-frequency information as it only uses measurements associated with a single frequency. Methods This paper proposes a tdEIT algorithm that imposes spectral constraints on the framework of the linear least squares problem. Simulation and phantom experiments are conducted to compare the proposed spectral constraints algorithm (SC) with the damped least squares algorithm (DLS), which is a stable tdEIT algorithm used in clinical practice. The condition number and rank of the matrices needing inverses are analyzed, and image quality is evaluated using four indexes. The possibility of multi-tissue imaging and the influence of spectral errors are also explored. Results Significant performance improvement is achieved by combining multi-frequency and time-difference information. The simulation results show that, in one-step iteration, both algorithms have the same condition number and rank, but SC effectively reduces image noise by 20.25% compared to DLS. In addition, deformation error and position error are reduced by 8.37% and 7.86%, respectively. In two-step iteration, the rank of SC is greatly increased, which suggests that more information is employed in image reconstruction. Image noise is further reduced by an average of 32.58%, and deformation error and position error are also reduced by 20.20% and 31.36%, respectively. The phantom results also indicate that SC has stronger noise suppression and target identification abilities, and this advantage is more obvious with iteration. The results of multi-tissue imaging show that SC has the unique advantage of automatically extracting a single tissue to image. Conclusions SC enables tdEIT to utilize multi-frequency information in cases where the spectral constraints are known and then provides higher quality images for applications.

Abstract Background Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction. Methods This study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods. Results To validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators’ increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images. Conclusion In this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.

Abstract Polysaccharides are a class of carbohydrates that play pivotal roles in living systems such as being chemical messengers in many vital biological pathways. However, the complexity and heterogeneity of these natural structures have posed daunting challenges on their production, characterization, evaluation, and applications. While there have been various types of synthetic skeletons that could mimic some biological aspects of polysaccharides, a safer and more easily accessed system is still desired to avoid the unnatural components and difficulties in modifying the structures. In this work, conveniently accessible self‐assembling glycopeptide conjugates are developed, where the natural O‐glycosidic linkages and phosphoryl modifications assist the self‐assembly and concurrently reduce the risk of toxicity. The generated nanoparticles in aqueous solution offer a multivalent display of structurally controllable carbohydrates as mimics of polysaccharides, among which a mannosylated version exhibits immunostimulatory effects in both cellular assays and vaccination of mice. The obtained results demonstrate the potential of this glycopeptide conjugate‐derived platform in exploiting the intriguing properties of carbohydrates in a more structurally maneuverable fashion.

The objective of the study was to develop an optimized method for dynamic electrical impedance tomography (EIT) to image large area conductive perturbation (LACP), a new type of imaging target that we found during the monitoring and evaluation of the mannitol dehydration treatment of patients with brain edema based on brain EIT. Previously, we reconstructed LACP images with the commonly used NOSER algorithm with polar driven pattern. However, conductivity changes near the center of the LACP were blurred or remained undetected, and blocky artifacts appeared in the reconstructed images, making the interpretation of the results difficult. To solve this problem, we, for the first time, propose an optimized algorithm for imaging LACP. This algorithm comprises a modified sensitivity matrix to compensate for the blurred conductivity changes near the center. It also uses the Markov random field constraint to reduce blocky artifacts. To verify the performance of the proposed method, we conducted experiments based on head models and human subjects. Specific metrics, including shape error (SE) and image fluctuation (IF) artifacts, were also proposed to evaluate the image quality of the LACP. The experimental results demonstrated that, compared with the NOSER algorithm, the proposed method could respectively reduce SE and IF by 51.7% and 47.5%. Therefore, it can optimize the imaging of LACP and provide references for other applications at present or in the future which involve imaging LACP.

Electrical impedance tomography (EIT) is a promising medical technique for monitoring brain injury through the reflection of electrical impedance changes in internal brain tissues. However, the contact impedances at the electrode-scalp interfaces can still notably affect the EIT accuracy. Hence, we propose a method for real-time monitoring the multi-channel contact impedances of cerebral EIT using a reference electrode attached close to the head vertex. This method allows to approximate inter-channel differences and changes over time of multi-channel contact impedances. The experimental results on 36 volunteers show that the maximum relative inter-channel difference obtained from 16 disposable surface electrodes reaches 18.7%, and the maximum relative change over time reaches 19.2%. The proposed method can assist clinicians in real-time monitoring of contact conditions from the multiple electrode-scalp interfaces during the clinical application of the cerebral EIT.

Abstract Dynamic electrical impedance tomography (EIT) promises to be a valuable technique for monitoring the development of brain injury. But in practical long-term monitoring, noise and interferences may cause insufficient image quality. To help unveil intracranial conductivity changes, signal processing methods were introduced to improve EIT data quality and algorithms were optimized to be more robust. However, gains for EIT image reconstruction can be significantly increased if we combine the two techniques properly. The basic idea is to apply the priori information in algorithm to help de-noise EIT data and use signal processing to optimize algorithm. First, we process EIT data with principal component analysis (PCA) and reconstruct an initial CT-EIT image. Then, as the priori that changes in scalp and skull domains are unwanted, we eliminate their corresponding boundary voltages from data sets. After the two-step denoising process, we finally re-select a local optimal regularization parameter and accomplish the reconstruction. To evaluate performances of the signal processing-priori information based reconstruction (SPR) method, we conducted simulation and in-vivo experiments. The results showed SPR could improve brain EIT image quality and recover the intracranial perturbations from certain bad measurements, while for some measurement data the generic reconstruction method failed.

Abstract Phantom experiments are an important step for testing during the development of new hardware or imaging algorithms for head electrical impedance tomography (EIT) studies. However, due to the sophisticated anatomical geometry and complex resistivity distribution of the human head, constructing an accurate phantom for EIT research remains challenging, especially for skull modelling. In this paper, we designed and fabricated a novel head phantom with anatomically realistic geometry and continuously varying skull resistivity distribution based on 3D printing techniques. The skull model was constructed by simultaneously printing the distinct layers inside the skull with resistivity-controllable printing materials. The entire phantom was composed of saline skin, a 3D-printed skull, saline cerebrospinal fluid (CSF) and 3D-printed brain parenchyma. The validation results demonstrated that the resistivity of the phantom was in good agreement with that of human tissue and was stable over time, and the new phantom performed well in EIT imaging. This paper provides a standardized, efficient and reproducible method for the construction of a head phantom for EIT that could be easily adapted to other conditions for manufacturing head phantoms for brain function research, such as transcranial direct current stimulation (TDCS) and electroencephalography (EEG).

Curved continuous steel box girders are extensively utilized in bridge construction due to their efficiency and environmental benefits. However, in regions with significant temperature fluctuations, temperature effects can result in cumulative deformation and stress concentration, which may severely impact structural safety and durability. This study examines the structural response of curved continuous steel box girders with five spans under diverse temperature conditions and also develops a comprehensive parameterized thermodynamic numerical model. The model assesses the influence of cross-sectional shape parameters, including the number of cross-sectional box chambers, diaphragm thickness, and height-to-width ratio, as well as longitudinal structural parameters such as planar configurations, width-to-span ratio, and support arrangements, along with the arrangement of stiffening ribs on the temperature-induced effects in the girders. The results indicate that optimizing the width and eccentricity of support stiffeners to 30% and 25%, respectively, in support plate size can significantly alleviate local temperature-induced stresses. Additionally, variations in longitudinal and transverse stiffeners manifest minimal impact on thermal performance. These findings provide a theoretical foundation for improved design and construction practices, offering practical design recommendations to mitigate temperature effects and enhance the longevity and safety of such structures.

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang–Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

Repairing injuries to the nervous system has always been a prominent topic in clinical research. Direct suturing and nerve displacement surgery are the primary treatment options, but they may not be suitable for long nerve injuries and may require sacrificing the functionality of other autologous nerves. With the emergence of tissue engineering, hydrogel materials have been identified as a promising technology with clinical translation potential for repairing nervous system injuries due to their excellent biocompatibility and ability to release or deliver functional ions. By controlling their composition and structure, hydrogels can be Functionalized and almost fully matched with nerve tissue and even simulate nerve conduction function and mechanical properties. Thus, they are suitable for repairing injuries to both the central and peripheral nervous systems. This article provides a review of recent research progress in functionalized hydrogels for nerve injury repair, highlighting the design differences among various materials and future research directions. We strongly believe that the development of functionalized hydrogels has great potential for improving the clinical treatment of nerve injuries.

Babesia gibsoni is a tick-borne apicomplexan protozoan causing canine babesiosis. This parasite has diploid sexual reproduction in ticks, during which genetic exchanges can occur leading to increased genetic diversity, which is an important factor in adapting to environmental changes. Exploring the genetic variation of B. gibsoni population can provide a foundation for understanding the patterns of disease transmission and developing babesiosis control strategies. Partial 18S rRNA fragment sequences were obtained from 11 B. gibsoni isolates collected from different regions in China and 117 publicly available sequences were from 12 geographical areas including China. The genetic variation, demographic expansion and population structure were examined. A total of 34 haplotypes were identified among B. gibsoni populations. Analysis of molecular variance, pairwise Fst and structure analysis showed that high genetic variation within populations, low genetic differentiation and obvious mixture haplotype were apparent in a single continent, but higher genetic differentiation was detected across different continents. Neutrality tests implied that B. gibsoni populations had experienced population extension. These findings will contribute to understand the genetics and evolution of B. gibsoni and will be useful for formulating effective management strategies to prevent and control this parasite.

An image analysis-based Reflection Coefficient (RC) estimation method of femtosecond laser surface processing for the blackening of X-ray imaging sensor shell is proposed. The Support Vector Regression (SVR) is used for RC computation and both an offline and an online steps are considered in this method. Regarding offline step, the typical laser process parameters are set to perform surface processing and Scanning Electron Microscope (SEM) images are recorded. Then a series of image features are computed and both the computed image features and typical laser parameters are used to train SVR: the training dataset includes the laser line space, laser beam diameter, natural logarithm of laser power divided by laser frequency, and image features of Gray-Level Co-occurrence Matrix (GLCM); the supervising data are laser ablation diameters. As for online step, when SEM image data are recorded after laser processing, the trained SVR is used to predict laser ablation diameter and then the RC can be computed by laser ablation model. Many experiment results have verified the effectiveness of our proposed method, and the RC estimation accuracy can be better than 90.0%.

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.

In a three-dimensional (3D) disaster rescue mission environment, multi-drone mission assignments and path planning are challenging. Aiming at this problem, a mission assignment method based on adaptive genetic algorithms (AGA) and a path planning method using sine–cosine particle swarm optimization (SCPSO) are proposed. First, an original 3D digital terrain model is constructed. Second, common threat sources in disaster rescue environments are modeled, including mountains, transmission towers, and severe weather. Third, a cost–revenue function that considers factors such as drone performance, demand for mission points, elevation cost, and threat sources, is formulated to assign missions to multiple drones. Fourth, an AGA is employed to realize the multi-drone mission assignment. To enhance convergence speed and optimize performance in finding the optimal solution, an AGA using both the roulette method and the elite retention method is proposed. Additionally, the parameters of the AGA are adjusted according to the changes in the fitness function. Furthermore, the improved circle algorithm is also used to preprocess the mission sequence for AGA. Finally, based on the sine–cosine function model, a SCPSO is proposed for planning the optimal flight path between adjacent task points. In addition, the inertia and acceleration coefficients of linear weights are designed for SCPSO so as to enhance its performance to escape the local minimum, explore the search space more thoroughly, and achieve the purpose of global optimization. A multitude of simulation experiments have demonstrated the validity of our method.

In highland pastures, grazing is a common method for managing sheep due to the abundance of grassland resources. However, it is easy for sheep to encounter situations such as stray, deviation and attacks from natural enemies; therefore, the remote monitoring of sheep in the highland pastures is an urgent problem to be solved. This paper proposes a mid-infrared sheep segmentation method based on the multi-level region fusion maximum between-class variance algorithm, i.e., OTSU algorithm, for sheep surveillance. First, a mean adjustment OTSU algorithm is designed to better distinguish the interference areas in the background. Second, the Butterworth high-pass filter is combined with the mean adjustment OTSU segmentation algorithm to remove the high-brightness interference areas in the background with slow gray intensity changes. Finally, after filtering out the large area background and small stray point, the two processed results above are fused with the AND logical operation to obtain a final segmentation result. Our algorithm is evaluated using three objective evaluation indicators: the root mean square error (RMSE), structural similarity index metric (SSIM), and peak signal to noise ratio (PSNR). The RMSE, SSIM, PSNR of highland wetland image are 0.43187, 0.99526, and 29.16353. The RMSE, SSIM, PSNR of sandy land image are 0.87472, 0.98388, and 23.87430. The RMSE, SSIM, PSNR of grassland image are 0.65307, 0.99437, and 30.33159. The results show that our algorithm can meet the requirements for the mid-infrared sheep segmentation in highland pastures.

Comparing with the application of charge coupled device (CCD) sensor, a kind of interference stripe noise (ISN) can be observed easily from the complementary metal-oxide-semiconductor (CMOS) sensor-based digital holographic measurement (DHM) system because a series of optic coatings and filters are used in front of that sensor in order to improve its imaging effect. The improper assembly or the micro manufacturing error of these optic components may create the unexpected interference phenomena in the imaging sensor. To reduce that noise, an effective image processing method is developed. On one hand the Laplacian pyramid method is used to highlight the ISN and a cartoon-texture decomposition technique is carried out to remove the additive ISN. On the other hand, an improved Lee filtering is performed to restrain the multiplicative ISN. Extensive experiment results have shown that the image quality can be improved after the ISN reduction and this method is fit for solving a deionized water temperature estimation application where the texture complexity of target image is relatively low.

In this paper, an aliasing noise restraint technique and a system identification-based surface electromyography (sEMG)-force prediction model are proposed to realize a type of robust sEMG and muscle force prediction. For signal denoising, a novel non-negative matrix factorization screening empirical mode decomposition (NMFSEMD) and a fast orthogonal search (FOS)-based muscle force prediction model are developed. First, the NMFSEMD model is used to screen the empirical mode decomposition (EMD) results into the noisy intrinsic mode functions (IMF). Then, the noise matrix is computed using IMF translation and superposition, and the matrix is used as the input of NMF to obtain the denoised IMF. Furthermore, the reconstruction outcome of the NMFSEMD method can be used to estimate the denoised sEMG. Finally, a new sEMG muscle force prediction model, which considers a kind of candidate function in derivative form, is constructed, and a data-training-based linear weighted model is obtained. Extensive experimental results validate the suggested method's correction: after the NMFSEMD denoising of raw sEMG signal, the signal-noise ratio (SNR) can be improved by about 15.0 dB, and the energy percentage (EP) can be greater than 90.0%. Comparing with the muscle force prediction models using the traditional pretreatment and LSSVM, and the NMFSEMD plus LSSVM-based method, the mean square error (MSE) of our approach can be reduced by at least 1.2%.

Background: Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data.Methods: Weighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings.Results: The proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%.Conclusions: In this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion. [ABSTRACT FROM AUTHOR]