Identification of the proteolytic signature in CVB3-infected cells.

Coxsackievirus B3 (CVB3) encodes proteinases that are essential for processing of the translated viral polyprotein. Viral proteinases also target host proteins to manipulate cellular processes and evade innate antiviral responses to promote replication and infection. While some host protein substrates of the CVB3 3C and 2A cysteine proteinases have been identified, the full repertoire of targets is not known. Here, we utilize an unbiased quantitative proteomics-based approach termed terminal amine isotopic labeling of substrates (TAILS) to conduct a global analysis of CVB3 protease-generated N-terminal peptides in both human HeLa and mouse cardiomyocyte (HL-1) cell lines infected with CVB3. We identified >800 proteins that are cleaved in CVB3-infected HeLa and HL-1 cells including the viral polyprotein, known substrates of viral 3C proteinase such as PABP, DDX58, and HNRNPs M, K, and D and novel cellular proteins. Network and GO-term analysis showed an enrichment in biological processes including immune response and activation, RNA processing, and lipid metabolism. We validated a subset of candidate substrates that are cleaved under CVB3 infection and some are direct targets of 3C proteinase in vitro. Moreover, depletion of a subset of TAILS-identified target proteins decreased viral yield. Characterization of two target proteins showed that expression of 3Cpro-targeted cleaved fragments of emerin and aminoacyl-tRNA synthetase complex-interacting multifunctional protein 2 modulated autophagy and the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway, respectively. The comprehensive identification of host proteins targeted during virus infection provides insights into the cellular pathways manipulated to facilitate infection. IMPORTANCE: RNA viruses encode proteases that are responsible for processing viral proteins into their mature form. Viral proteases also target and cleave host cellular proteins; however, the full catalog of these target proteins is incomplete. We use a technique called terminal amine isotopic labeling of substrates (TAILS), an N-terminomics to identify host proteins that are cleaved under virus infection. We identify hundreds of cellular proteins that are cleaved under infection, some of which are targeted directly by viral protease. Revealing these target proteins provides insights into the host cellular pathways and antiviral signaling factors that are modulated to promote virus infection and potentially leading to virus-induced pathogenesis.

Integration of silicon nanostructures for health and energy applications using MACE: a cost-effective process.

Silicon in its nanoscale range offers a versatile scope in biomedical, photovoltaic, and solar cell applications. Due to its compatibility in integration with complex molecules owing to changes in charge density of as-fabricated Silicon Nanostructures (SiNSs) to realize label-free and real-time detection of certain biological and chemical species with certain biomolecules, it can be exploited as an indicator for ultra-sensitive and cost-effective biosensing applications in disease diagnosis. The morphological changes of SiNSs modified receptors (PNA, DNA, etc) have huge future scope in optimized sensitivity (due to conductance variations of SiNSs) of target biomolecules in health care applications. Further, due to the unique optical and electrical properties of SiNSs realized using the chemical etching technique, they can be used as an indicator for photovoltaic and solar cell applications. In this work, emphasis is given on different critical parameters that control the fabrication morphologies of SiNSs using metal-assisted chemical etching technique (MACE) and its corresponding fabrication mechanisms focusing on numerous applications in energy storage and health care domains. The evolution of MACE as a low-cost, easy process control, reproducibility, and convenient fabrication mechanism makes it a highly reliable-process friendly technique employed in photovoltaic, energy storage, and biomedical fields. Analysis of the experimental fabrication to obtain high aspect ratio SiNSs was carried out using iMAGEJ software to understand the role of surface-to-volume ratio in effective bacterial interfacing. Also, the role of silicon nanomaterials has been discussed as effective anti-bacterial surfaces due to the presence of silver investigated in the post-fabrication energy dispersive x-ray spectroscopy analysis using MACE.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition.

To address the challenge of accurately locating unmanned aerial vehicles (UAVs) in situations where radar tracking is not feasible and visual observation is difficult, this paper proposes an innovative acoustic source localization method based on improved Empirical Mode Decomposition (EMD) within an adaptive frequency window. In this study, the collected flight signals of UAVs undergo smoothing filtering. Additionally, Robust Empirical Mode Decomposition (REMD) is applied to decompose the signals into Intrinsic Mode Function (IMF) components for spectrum analysis. We introduce a sliding frequency window with adjustable bandwidth, which is automatically determined using a Grey Wolf Optimizer (GWO) with a sliding index. This window is used to lock and extract specific frequencies from the IMFs. Based on predefined criteria, the extracted IMF components are reconstructed, and trigger signal times are analyzed and recorded from these reconstructed IMFs. The time differences between sensor receptions are then calculated. Furthermore, this study introduces the Chan-Taylor localization algorithm based on weighted least squares. This advanced algorithm takes sensor time delay parameters as input and solves a set of nonlinear equations to determine the target's location. Simulations and real-world signal tests are used to validate the robustness and performance of the proposed method. The results indicate that the localization error remains below 5% within a 15 m × 15 m measurement area. This provides an efficient and real-time method for detecting the location of small UAVs.

Brain Microtubule Electrical Oscillations-Empirical Mode Decomposition Analysis.

Microtubules (MTs) are essential cytoskeletal polymers of eukaryote cells implicated in various cell functions, including cell division, cargo transfer, and cell signaling. MTs also are highly charged polymers that generate electrical oscillations that may underlie their ability to act as nonlinear transmission lines. However, the oscillatory composition and time-frequency differences of the MT electrical oscillations have not been identified. Here, we applied the Empirical Mode Decomposition (EMD) to bovine brain MT sheet recordings to determine the number and fundamental frequencies of the Intrinsic Modes Functions (IMF) and evaluate their energetic contribution to the electrical signal. As previously reported, raw signals were obtained from cow brain MTs (Cantero et al. Sci Rep 6:27143, 2016), sampled, filtered, and subjected to signal decomposition from representative experiments. Filtered signals (200 Hz) allowed us to identify either six or seven IMFs. The reconstructed tracings faithfully resembled the original signals, with identifiable frequency peaks. To extend the analysis to obtain time-frequency information and the energy implicated in each IMF, we applied the Hilbert-Huang Transform (HHT) and the Continuous Wavelet Transform (CWT) to the same samples. The analyses disclosed the presence of more fundamental frequency peaks than initially reported and evidenced the advantages and disadvantages of each transform. The study indicates that the EMD is a robust approach to quantifying signal decomposition of brain MT oscillations and suggests novel similarities with human brain wave electroencephalogram (EEG) recordings. The evidence points to the potentially fundamental role of MT oscillations in brain electrical activity.

Impact of extramedullary disease in AML patients undergoing sequential RIC for HLA-matched transplantation: occurrence, risk factors, relapse patterns, and outcome.

We sought to evaluate the role of extramedullary disease (EMD) in sequential RIC retrospectively analyzing data of 144 high-risk AML patients undergoing HLA-matched transplantation. Median long-term follow-up was 11.6 years. Eighteen percent of patients (n = 26/144) presented with extramedullary AML (EM AML) or a history of EMD at time of transplantation. Overall relapse rate was 25% (n = 36/144) with 15% (n = 21/144) of all patients developing isolated BM relapse and 10% (n = 15/144) developing EM AML relapse with or without concomitant BM relapse (EM ± BM). Manifestation of EM relapse after transplantation occurred frequently at multiple sites and presented mostly as solid tumor mass. Only 3/15 patients with EM ± BM relapse showed a prior EMD manifestation. EMD prior to allogeneic transplantation had no impact on post-transplant OS when compared to non-EMD (median post-transplant OS 3.8 years versus 4.8 years; ns). Risk factors (p = < 0.1) for EM ± BM relapse included younger age and a higher number of prior intensive chemotherapies, whereas the presence of chronic GVHD was a protective factor. Median post-transplant OS (15.5 months vs. 15.5 months), RFS (9.6 months vs 7.3 months), and post-relapse OS (6.7 months vs. 6.3 months) were not significantly different between patients with isolated BM vs. EM ± BM relapse. Taken together, occurrence of EMD prior to as well as of EM ± BM AML relapse after transplantation was moderate, presenting mostly as solid tumor mass after transplantation. However, diagnosis of those does not seem to influence outcomes after sequential RIC. A higher number of chemotherapy cycles prior to transplantation was identified as recent risk factor for EM ± BM relapse.

Evaluation of CI electrode position from imaging: comparison of an automated technique with the established manual method.

BACKGROUND: A manual evaluation of the CI electrode position from CT and DVT scans may be affected by diagnostic errors due to cognitive biases. The aim of this study was to compare the CI electrode localization using an automated method (image-guided cochlear implant programming, IGCIP) with the clinically established manual method. METHODS: This prospective experimental study was conducted on a dataset comprising N=50 subjects undergoing cochlear implantation with a Nucleus® CI532 or CI632 Slim Modiolar electrode. Scalar localization, electrode-to-modiolar axis distances (EMD) and angular insertion depth (aDOI) were compared between the automated IGCIP tool and the manual method. Two raters made the manual measurements, and the interrater reliability (±1.96·SD) was determined as the reference for the method comparison. The method comparison was performed using a correlation analysis and a Bland-Altman analysis. RESULTS: Concerning the scalar localization, all electrodes were localized both manually and automatically in the scala tympani. The interrater differences ranged between ±0.2 mm (EMD) and ±10° (aDOI). There was a bias between the automatic and manual method in measuring both localization parameters, which on the one hand was smaller than the interrater variations. On the other hand, this bias depended on the magnitude of the EMD respectively aDOI. A post-hoc analysis revealed that the deviations between the methods were likely due to a different selection of mid-modiolar axis. CONCLUSIONS: The IGCIP is a promising tool for automated processing of CT and DVT scans and has useful functionality such as being able to segment the cochlear using post-operative scans. When measuring EMD, the IGCIP tool is superior to the manual method because the smallest possible distance to the axis is determined depending on the cochlear turn, whereas the manual method selects the helicotrema as the reference point rigidly. Functionality to deal with motion artifacts and measurements of aDOI according to the consensus approach are necessary, otherwise the IGCIP is not unrestrictedly ready for clinical use.

Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.

A New Method of Identifying Characteristic Points in the Impedance Cardiography Signal Based on Empirical Mode Decomposition.

The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)-an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information.

π-FBG Fiber Optic Acoustic Emission Sensor for the Crack Detection of Wind Turbine Blades.

Wind power is growing rapidly as a green and clean energy source. As the core part of a wind turbine, the blades are subjected to enormous stress in harsh environments over a long period of time and are therefore extremely susceptible to damage, while at the same time, they are costly, so it is important to monitor their damage in a timely manner. This paper is based on the detection of blade damage using acoustic emission signals, which can detect early minor damage and internal damage to the blades. Instead of conventional piezoelectric sensors, we use fiber optic gratings as sensing units, which have the advantage of small size and corrosion resistance. Furthermore, the sensitivity of the system is doubled by replacing the conventional FBG (fiber Bragg grating) with a π-phase-shifted FBG. For the noise problem existing in the system, this paper combines the traditional WPD (wavelet packet decomposition) denoising method with EMD (empirical mode decomposition) to achieve a better noise reduction effect. Finally, small wind turbine blades are used in the experiment and their acoustic emission signals with different damage are collected for feature analysis, which sets the stage for the subsequent detection of different damage degrees and types.

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Efficacy of rh-PDGF-BB and Emdogain with or without DFDBA using M-MIST in the treatment of intrabony defects.

Background: The versatile combination of emdogain or enamel matrix derivative (EMD), recombinant human platelet-derived growth factor-BB (rhPDGF-BB), and demineralized freeze-dried bone allograft (DFDBA) has not been utilized in the treatment of intrabony defects yet. Aim: The present study attempted to investigate the efficacy of a combination of simple, uncomplicated nature of modified minimally invasive surgical technique (M-MIST) with EMD, rhPDGF-BB, and DFDBA in the surgical management of intrabony defects and to assess the possible favorable effects for a period of 6 months. Patients and Methods: Thirty healthy subjects were included in the present double-blind, randomized controlled, two-arm parallel study. The test group was treated with M-MIST by using rhPDGF-BB, EMD, and DFDBA, and the control group was treated with M-MIST by using rhPDGF-BB and EMD. Results: Differences between the mean values of primary clinical parameters including relative attachment level, probing depth, and gingival recession at baseline and those at 6 months after surgery were statistically significant in both groups. Inter-group comparison for clinical attachment level gain, probing depth reduction, and change in the position of gingival margin revealed no statistically significant differences. Inter-group comparison revealed significant differences in linear bone growth (LBG) and percentage bone fill (% BF) but no significant differences in the residual defect depth and change in the alveolar crest position. Conclusion: The additional use of DFDBA provides superior benefits in terms of LBG and % BF in intrabony defects. This improvement might be attributed to the use of an osteoinductive scaffold.

Using the hybrid EMD-BPNN model to predict the incidence of HIV in Dalian, Liaoning Province, China, 2004-2018.

BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV (human immunodeficiency virus, and up to now there are no curable drugs or effective vaccines. In order to understand AIDS's development trend, we establish hybrid EMD-BPNN (empirical modal decomposition and Back-propagation artificial neural network model) model to forecast new HIV infection in Dalian and to evaluate model's performance. METHODS: The monthly HIV data series are decomposed by EMD method, and then all decomposition results are used as training and testing data to establish BPNN model, namely BPNN was fitted to each IMF (intrinsic mode function) and residue separately, and the predicted value is the sum of the predicted values from the models. Meanwhile, using yearly HIV data to established ARIMA and using monthly HIV data to established BPNN, and SARIMA (seasonal autoregressive integrated moving average) model to compare the predictive ability with EMD-BPNN model. RESULTS: From 2004 to 2017, 3310 cases of HIV were reported in Dalian, including 101 fatal cases. The monthly HIV data series are decomposed into four relatively stable IMFs and one residue item by EMD, and the residue item showed that the incidence of HIV increases firstly after declining. The mean absolute percentage error value for the EMD-BPNN, BPNN, SARIMA (1,1,2) (0,1,1)12 in 2018 is 7.80%, 10.79%, 9.48% respectively, and the mean absolute percentage error value for the ARIMA (3,1,0) model in 2017 and 2018 is 8.91%. CONCLUSIONS: The EMD-BPNN model was effective and reliable in predicting the incidence of HIV for annual incidence, and the results could furnish a scientific reference for policy makers and health agencies in Dalian.

Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA.

In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual information (EEMI) was introduced to comprehensively consider the energy distribution of the modes and the dependence between the modes and the original signal, and the EEMI was used as the objective function. In addition, TVF-EMD was optimized by IGOA and the optimal parameters matching the input signal were obtained. Finally, the feature frequency of the signal was extracted by analyzing the sensitive mode with larger kurtosis. The optimization experiments of 23 sets of benchmark functions showed that IGOA not only enhanced the balance between exploration and development, but also improved the global and local search ability and stability of the algorithm. The analysis of the simulation signal and bearing signal shows that the parameter-adaptive TVF-EMD method can separate the modes with specific physical meanings accurately. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), TVF-EMD with fixed parameters and GOA-TVF-EMD, the decomposition performance of the proposed method is better. The proposed method not only improved the under-decomposition, over-decomposition and modal aliasing problems of TVF-EMD, but could also accurately separate the frequency components of the signal and extract the included feature information, so it has practical significance in mechanical fault diagnosis.

New Denoising Method for Lidar Signal by the WT-VMD Joint Algorithm.

Light detection and ranging (LIDAR) is an active remote sensing system. Lidar echo signal is non-linear and non-stationary, which is often accompanied by various noises. In order to filter out the noise and extract valid signal information, a suitable method should be chosen for noise reduction. Some denoising methods are commonly used, such as the wavelet transform (WT), the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and their improved algorithms. In this paper, a new denoising method named the WT-VMD joint algorithm based on the sparrow search algorithm (SSA), for lidar signal is selected by comparative experiment analysis. It is shown that this method is the most suitable one with the maximum signal-to-noise ratio (SNR), the minimum root-mean-square error (RMSE), and a relatively small indicator of smoothness when it is used in three kinds (50, 100, and 1000 pulses) of simulate lidar signals. The SNR is increased by 138.5%, 77.8% and 42.8% and the RMSE is decreased by 81.8%, 72.0% and 68.8% when being used to the three kinds of cumulative signal without pollution. Then, the SNR is increased by 83.3%, 60.4% and 24.0% and the RMSE is decreased by 70.8%, 66.0% and 50.5% when being used to the three kinds of cumulative signal with aerosol and clouds. The WT-VMD joint algorithm based on SSA is used in the denoising process for the actual lidar signal, showing extraordinary denoising effect and will improve the inversion accuracy of the lidar signal.

A Wi-Fi Indoor Positioning Method Based on an Integration of EMDT and WKNN.

In indoor positioning, signal fluctuation is one of the main factors affecting positioning accuracy. To solve this problem, a new method based on an integration of the empirical mode decomposition threshold smoothing method (EMDT) and improved weighted K nearest neighbor (WKNN), named EMDT-WKNN, is proposed in this paper. First, the nonlinear and non-stationary received signal strength indication (RSSI) sequences are constructed. Secondly, intrinsic mode functions (IMF) selection criteria based on energy analysis method and fluctuation coefficients is proposed. Thirdly, the EMDT method is employed to smooth the RSSI fluctuation. Finally, to further avoid the influence of RSSI fluctuation on the positioning accuracy, the deviated matching points are removed, and more precise combined weights are constructed by combining the geometric distance of the matching points and the Euclidean distance of fingerprints in the positioning method-WKNN. The experimental results show that, on an underground parking dataset, the positioning accuracy based on EMDT-WKNN can reach 1.73 m in the 75th percentile positioning error, which is 27.6% better than 2.39 m of the original RSSI positioning method.

An Approach for Estimating Lightning Current Parameters Using the Empirical Mode Decomposition Method.

Lightning parameters are needed in different engineering applications. For the prediction of the severity of transient voltages in power systems, an accurate knowledge of the parameters of lightning currents is essential. All relevant standards and technical brochures recommend that lightning characteristics should be classified according to geographical regions instead of assuming that these characteristics are globally uniform. Many engineers and scientists suggest that better methods for lightning current measurements and analyses need to be developed. A system for direct lightning current measurements installed on Mount Lovcen is described in this paper. Observed data were analyzed, and statistical data on parameters that are of interest for engineering applications were obtained, as well as correlations between various lightning parameters. Furthermore, a novel approach for classifying and analyzing lightning data from direct measurements based on empirical mode decomposition (EMD) is proposed. Matlab was used as a tool for signal processing and statistical analysis. The methodology implemented in this work opens possibilities for automated analysis of large data sets and expressing lightning parameters in probabilistic terms from the data measured on site.

Comparison of Empirical Mode Decomposition and Singular Spectrum Analysis for Quick and Robust Detection of Aerodynamic Instabilities in Centrifugal Compressors.

Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of view. Rapid indication of instabilities can be obtained using a pressure signal from the compressor. Detection of aerodynamic instabilities using pressure signal results in specific challenges, as the signal is often highly contaminated with noise, which can influence the performance of detection methods. The aim of this study is to investigate and compare the performance of two non-linear signal processing methods-Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA)-for aerodynamic instability detection. Two instabilities of different character, local-inlet recirculation and global-surge, are considered. The comparison focuses on the robustness, sensitivity and pace of detection-crucial parameters for a successful detection method. It is shown that both EMD and SSA perform similarly for the analysed machine, despite different underlying principles of the methods. Both EMD and SSA have great potential for instabilities detection, but tuning of their parameters is important for robust detection.

A Non-Destructive Health Evaluation Method for Wooden Utility Poles with Frequency-Modulated Empirical Mode Decomposition and Laplace Wavelet Correlation Filtering.

Wooden utility poles are one of the most commonly used utility carriers in North America. Even though they are given different protection treatments, wooden utility poles are prone to have defects that are mainly caused by temperature, oxygen, moisture, and high potential hydrogen levels after decades of being exposed in open-air areas. In order to meet the growing demand regarding their maintenance and replacement, an effective health evaluation technology for wooden utility poles is essential to ensure normal power supply and safety. However, the commonly used hole-drilling inspection method always causes extra damage to wooden utility poles and the precision of health evaluation highly relies on technician experience at present. Therefore, a non-destructive health evaluation method with frequency-modulated empirical mode decomposition (FM-EMD) and Laplace wavelet correlation filtering based on dynamic responses of wooden utility poles was proposed in this work. Specifically, FM-EMD was used to separate multiple confusing closely-spaced vibration modes due to nonlinear properties of wooden utility poles into several single modes. The instantaneous frequency and damping factor of the decomposed signal of each single mode of the dynamic response of a wooden utility pole could be determined using Laplace wavelet correlation filtering with high precision. The health status of a wooden utility pole could then be estimated according to the extracted instantaneous frequency and damping factor of the decomposed signal of each single mode. The proposed non-destructive health evaluation method for wooden utility poles was tested in the field and achieved successful results.

Cell Metabolomics Reveals the Potential Mechanism of Aloe Emodin and Emodin Inhibiting Breast Cancer Metastasis.

Metastasis is one of the main obstacles for the treatment and prognosis of breast cancer. In this study, the effects and possible mechanisms of aloe emodin (AE) and emodin (EMD) for inhibiting breast cancer metastasis were investigated via cell metabolomics. First, a co-culture model of MCF-7 and HUVEC cells was established and compared with a traditional single culture of MCF-7 cells. The results showed that HUVEC cells could promote the development of cancer cells to a malignant phenotype. Moreover, AE and EMD could inhibit adhesion, invasion, and angiogenesis and induce anoikis of MCF-7 cells in co-culture model. Then, the potential mechanisms behind AE and EMD inhibition of MCF-7 cell metastasis were explored using a metabolomics method based on UPLC-Q-TOF/MS multivariate statistical analysis. Consequently, 27 and 13 biomarkers were identified in AE and EMD groups, respectively, including polyamine metabolism, methionine cycle, TCA cycle, glutathione metabolism, purine metabolism, and aspartate synthesis. The typical metabolites were quantitatively analyzed, and the results showed that the inhibitory effect of AE was significantly better than EMD. All results confirmed that AE and EMD could inhibit metastasis of breast cancer cells through different pathways. Our study provides an overall view of the underlying mechanisms of AE and EMD against breast cancer metastasis.