A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

An integrated CBLA-Net with fractional discrete wavelet transform and frequency-based CARS to predict heavy metal elements by XRF

BACKGROUND

X-ray fluorescence (XRF) emerges as a promising technique for estimating heavy metal elements. However, XRF spectra typically contain a significant amount of environmental information and signal noise, and the relationship between spectral intensity and element concentration is difficult to quantify using a single model, thereby reducing the predictive performance for low concentration elements.

RESULTS

This paper proposed a comprehensive framework for predicting elemental concentrations, encompassing preprocessing, variable selection, decision-making, to enable fast, non-destructive, and accurate estimation of element concentrations in soil. Firstly, an optimal denoising method based on fractional discrete wavelet transform (FDWT) was introduced to enhance signal quality. Furthermore, the frequency-based competitive adaptive reweighted sampling (FCARS) algorithm was employed for feature selection of XRF spectral variables, allowing extraction of the most informative features from the complex spectral data. Finally, a novel deep learning network, called ConvBiLSTM-Attention (CBLA-Net), was designed to achieve precise estimation of heavy metal elements concentration. Compared with other advanced algorithms, The CBLA-Net demonstrated the highest accuracy for V, Cr, Mn, Zn, Cd, and Pb, achieving the coefficient of determination (R2) of 0.9730, 0.9874, 0.9952, 0.9921, 0.9518, and 0.9741, respectively. The CBLA-Net not only effectively extract local features and capture global information, but also combines attention mechanism to focus on key information.

SIGNIFICANCE

The proposed novel deep learning quantitative framework, including preprocessing, feature selection, and CBLA-Net decision-making, significantly enhances the accuracy of elemental content prediction. It provides a new approach for accurately assessing the concentration of heavy metal elements in soil.

Wavelet denoising of fiber optic monitoring signals in permafrost regions

To address the noise issue in fiber optic monitoring signals in frozen soil areas, this study employs wavelet denoising techniques to process the fiber optic signals. Since existing parameter choices for wavelets are typically based on conventional environments, selecting suitable parameters for frozen soil regions becomes crucial. In this work, an index library is constructed based on commonly used wavelet basis functions in civil engineering. An optimal wavelet basis function is objectively selected through specific criteria. Considering the characteristic of small root mean square error in fiber optic signals in frozen soil areas, a multi-index fusion approach is applied to determine the optimal decomposition level. Field observations validate that denoised signals, with parameters set appropriately, can more accurately identify locations where settlement occurs.

Stationary wavelet denoising of solid-state NMR spectra using multiple similar measurements

Accumulating several scans of free induction decays is always needed to improve the signal-to-noise ratio of NMR spectra, especially for the low gyromagnetic ratio solid-state NMR. In this study, we present a new denoising approach based on the correlations between multiple similar NMR spectra. Contrary to the simple averaging of multiple scans or denoising the final averaged spectrum, we propose a Wavelet-based Denoising technique for Multiple Similar scans(WDMS). Firstly, the stationary wavelet transform is applied to decompose every spectrum into approximation coefficients and detail coefficients. Then, the detail coefficients are multiplied by weights calculated based on Pearson's correlation coefficient and structural similarity index between approximation coefficients of different spectra. Finally, the average of these detailed components is used to denoise the spectra. The proposed method is carried on the assumption that noise between multiple spectra is uncorrelated while peak signal information is similar between different spectra, thus preserving the possibility of applying further processing to the data. As a demonstration, the standard wavelet denoise is applied to the WDMS-processed spectra, achieving a further increase in the S/N ratio. We confirm the reliability of the denoising approach based on multiple scans on 1D/2D solid-state MAS/static NMR spectra. In addition, we also show that this method can be used to deal with a single Car-Purcell-Meiboom-Gill (CPMG) echo train.

An Integrated Method for Tunnel Health Monitoring Data Analysis and Early Warning: Savitzky-Golay Smoothing and Wavelet Transform Denoising Processing

A tunnel health monitoring (THM) system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky-Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control.

Thermal Degradation of Thaumatin at Low pH and Its Prevention Using Alkyl Gallates

Thaumatin, a potent sweet tasting protein extracted from the Katemfe Plant, is emerging as a natural alternative to synthetic non-nutritive sweeteners and flavor enhancer. As a food additive, its stability within the food matrix during thermal processing is of great interest to the food industry. When heated under neutral or basic conditions, thaumatin was found to lose its sweetness due to protein aggregation caused by sulfhydryl catalyzed disulfide bond interchange. At lower pH, while thaumatin was also found to lose sweetness after heating, it does so at a slower rate and shows more resistance to sweetness loss. SDS-PAGE indicated that thaumatin fragmented into multiple smaller pieces under heating in acidic pH. Using BEMPO-3, a lipophilic spin trap, we were able to detect the presence of a free-radical within the hydrophobic region of the protein during heating. Protein carbonyl content, a byproduct of protein oxidation, also increased upon heating, providing additional evidence for protein cleavage by a radical pathway. Hexyl gallate successfully inhibited the radical generation as well as protein carbonyl formation of thaumatin during heating.

A Simulation Independent Analysis of Single- and Multi-Component cw ESR Spectra

The accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understanding their structure-function relationships and electrochemical properties. The current methods of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users' defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly/partially resolved and unresolved spectra, which is common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

DESPERATE: A Python library for processing and denoising NMR spectra

NMR spectroscopy is an inherently insensitive technique with respect to the amount of observable signal. A common element in all NMR spectra is random thermal noise that is often characterized by a signal-to-noise ratio (SNR). SNR can be generically improved experimentally with repetitive signal averaging or during post-processing with apodization; the former of which often results in long experimental times and the latter results in the loss of spectral resolution. Denoising techniques can instead be used during post-processing to enhance SNR without compromising resolution. The most common approach relies on the singular-value decomposition (SVD) to discard noisy components of NMR data. SVD-based approaches work well, such as Cadzow and PCA, but are computationally expensive when used for large datasets that are often encountered in NMR (e.g., Carr-Purcell/Meiboom-Gill and nD datasets). Herein, we describe the implementation of a new wavelet transform (WT) routine for the fast and robust denoising of 1D and 2D NMR spectra. Several simulated and experimental datasets are denoised with both SVD-based Cadzow or PCA and WT's, and the resulting SNR enhancements and spectral uniformity are compared. WT denoising offers similar and improved denoising compared with SVD and operates faster by several orders-of-magnitude in some cases. All denoising and processing routines used in this work are included in a free and open-source Python library called DESPERATE.

Analysis of Small-Molecule Mixtures by Super-Resolved 1H NMR Spectroscopy

Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology, and many other areas of interest. Current barriers preclude from identifying the constituent molecules in a mixture as overlapping clusters of NMR lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. We demonstrate the efficacy of the method on the model mixtures of saccharides and amino acids in the presence of significant noise.

A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping

PURPOSE

Undersampling is used to reduce the scan time for high-resolution three-dimensional magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover R 2 ∗ $$ {R}_2^{\ast } $$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data.

THEORY

Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients.

METHODS

Undersampling takes place in the y-z plane of k-space according to the Poisson-disk pattern. Retrospective undersampling is performed to evaluate the performances of different reconstruction approaches, prospective undersampling is performed to demonstrate the feasibility of undersampling in practice.

RESULTS

The proposed AMP with parameter estimation (AMP-PE) approach successfully recovers R 2 ∗ $$ {R}_2^{\ast } $$ maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state-of-the-art l 1 $$ {l}_1 $$ -norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP-PE.

CONCLUSION

AMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image.

Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-13C]Pyruvate

Hyperpolarized carbon-13 MRI has the advantage of allowing the study of glycolytic flow in vivo or in vitro dynamically in real-time. The apparent exchange rate constant of a metabolite dynamic signal reflects the metabolite changes of a disease. Downstream metabolites can have a low signal-to-noise ratio (SNR), causing apparent exchange rate constant inconsistencies. Thus, we developed a method that estimates a more accurate metabolite signal. This method utilizes a kinetic model and background noise to estimate metabolite signals. Simulations and in vitro studies with photon-irradiated and control groups were used to evaluate the procedure. Simulated and in vitro exchange rate constants estimated using our method were compared with the raw signal values. In vitro data were also compared to the Area-Under-Curve (AUC) of the cell medium in ¹³C Nuclear Magnetic Resonance (NMR). In the simulations and in vitro experiments, our technique minimized metabolite signal fluctuations and maintained reliable apparent exchange rate constants. In addition, the apparent exchange rate constants of the metabolites showed differences between the irradiation and control groups after using our method. Comparing the in vitro results obtained using our method and NMR, both solutions showed consistency when uncertainty was considered, demonstrating that our method can accurately measure metabolite signals and show how glycolytic flow changes. The method enhanced the signals of the metabolites and clarified the metabolic phenotyping of tumor cells, which could benefit personalized health care and patient stratification in the future.

Wavelet neural network and complete ensemble empirical decomposition method to traffic control prediction

Traffic control prediction is one of the important issues of smart cities in that, by studying traffic parameters, there can be provided more peace and comfort in appropriate traffic routes. Combination of new and different technologies and scientific technical models for this complex prediction has always been paid attention to by researchers. In this paper, by presenting and improving one of the new methods of data collection with traffic congestion index, the appropriate models for predicting traffic control have been compared. Rapid and inexpensive collection of information and, the dynamics and momentary changes of traffic flows showed that the use of wavelet neural network was more accurate than other models of traffic control prediction. The application of combined Wavelet Neural Network with Complete Ensemble Empirical Mode Decompositionin traffic control prediction in this paper as CEEMD & WNN showed that the prediction accuracy increased compared to ARIMA, WNN, HYBRID ARIMA & WNN, TN methods and this new method has reasonable performance against the evaluation criteria to predict traffic control.

Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization

Noise significantly reduces the detection accuracy of transient power quality disturbances. It is critical to denoise the disturbance. The purpose of this research is to present an improved wavelet threshold denoising method and an adaptive parameter selection strategy based on energy optimization to address the issue of unclear parameter values in existing improved wavelet threshold methods. To begin, we introduce the peak-to-sum ratio and combine it with an adaptive correction factor to modify the general threshold. After calculating the energy of each layer of wavelet coefficient, the scale with the lowest energy is chosen as the optimal critical scale, and the correction factor is adaptively adjusted according to the critical scale. Following that, an improved threshold function with a variable factor is proposed, with the variable factor being controlled by the critical scale in order to adapt to different disturbance types’ denoising. The simulation results show that the proposed method outperforms existing methods for denoising various types of power quality disturbance signals, significantly improving SNR and minimizing MSE, while retaining critical information during disturbance mutation. Meanwhile, the effective location of the denoised signal based on the proposed method is realized by singular value decomposition. The minimum location error is 0%, and the maximum is three disturbance points.

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov’s algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

Hyperfine Decoupling of ESR Spectra Using Wavelet Transform

The objective of spectral analysis is to resolve and extract relevant features from experimental data in an optimal fashion. In continuous-wave (cw) electron spin resonance (ESR) spectroscopy, both g values of a paramagnetic center and hyperfine splitting (A) caused by its interaction with neighboring magnetic nuclei in a molecule provide important structural and electronic information. However, in the presence of g- and/or A-anisotropy and/or large number of resonance lines, spectral analysis becomes highly challenging. Either high-resolution experimental techniques are employed to resolve the spectra in those cases or a range of suitable ESR frequencies are used in combination with simulations to identify the corresponding g and A values. In this work, we present a wavelet transform technique in resolving both simulated and experimental cw-ESR spectra by separating the hyperfine and super-hyperfine components. We exploit the multiresolution property of wavelet transforms that allow the separation of distinct features of a spectrum based on simultaneous analysis of spectrum and its varying frequency. We retain the wavelet components that stored the hyperfine and/or super-hyperfine features, while eliminating the wavelet components representing the remaining spectrum. We tested the method on simulated cases of metal–ligand adducts at L-, S-, and X-band frequencies, and showed that extracted g values, hyperfine and super-hyperfine coupling constants from simulated spectra, were in excellent agreement with the values of those parameters used in the simulations. For the experimental case of a copper(II) complex with distorted octahedral geometry, the method was able to extract g and hyperfine coupling constant values, and revealed features that were buried in the overlapped spectra.

Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss

BACKGROUND

Ultrasound imaging has been recognized as a powerful tool in clinical diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of ultrasound images. Various denoising algorithms cannot fully reduce speckle noise and retain image features well for ultrasound imaging. The application of deep learning in ultrasound image denoising has attracted more and more attention in recent years.

METHODS

In the article, we propose a generative adversarial network with residual dense connectivity and weighted joint loss (GAN-RW) to avoid the limitations of traditional image denoising algorithms and surpass the most advanced performance of ultrasound image denoising. The denoising network is based on U-Net architecture which includes four encoder and four decoder modules. Each of the encoder and decoder modules is replaced with residual dense connectivity and BN to remove speckle noise. The discriminator network applies a series of convolutional layers to identify differences between the translated images and the desired modality. In the training processes, we introduce a joint loss function consisting of a weighted sum of the L1 loss function, binary cross-entropy with a logit loss function and perceptual loss function.

RESULTS

We split the experiments into two parts. First, experiments were performed on Berkeley segmentation (BSD68) datasets corrupted by a simulated speckle. Compared with the eight existing denoising algorithms, the GAN-RW achieved the most advanced despeckling performance in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual effect. When the noise level was 15, the average value of the GAN-RW increased by approximately 3.58% and 1.23% for PSNR and SSIM, respectively. When the noise level was 25, the average value of the GAN-RW increased by approximately 3.08% and 1.84% for PSNR and SSIM, respectively. When the noise level was 50, the average value of the GAN-RW increased by approximately 1.32% and 1.98% for PSNR and SSIM, respectively. Secondly, experiments were performed on the ultrasound images of lymph nodes, the foetal head, and the brachial plexus. The proposed method shows higher subjective visual effect when verifying on the ultrasound images. In the end, through statistical analysis, the GAN-RW achieved the highest mean rank in the Friedman test.

Development of Sliding Mode Controller Based on Internal Model Controller for Higher Precision Electro-Optical Tracking System

The electro-optical tracking system (ETS) on moving platforms is affected by the vibration of the moving carrier, the wind resistance torque in motion, the uncertainty of mechanisms and the nonlinear friction between frames and other disturbances, which may lead to the instability of the electro-optical tracking platform. Sliding mode control (SMC) has strong robustness to system disturbances and unknown dynamic external signals, which can enhance the disturbance suppression ability of ETSs. However, the strong robustness of SMC requires greater switching gain, which causes serious chattering. At the same time, the tracking accuracy of SMC has room for further improvement. Therefore, in order to solve the chattering problem of SMC and improve the tracking accuracy of SMC, an SMC controller based on internal model control (IMC) is proposed. Compared with traditional SMC, the proposed method can be used to suppress the strongest disturbance with the smallest switching gain, effectively solving the chattering problem of the SMC, while improving the tracking accuracy of the system. In addition, to reduce the adverse influence of sensor noise on the control effect, lifting wavelet threshold de-noising is introduced into the control structure to further improve the tracking accuracy of the system. The simulation and experimental results verify the superiority of the proposed control method.

Denoising of an ultraviolet light received signal based on improved wavelet transform threshold and threshold function

In this paper, the wavelet transform algorithm is used to reduce the noise of ultraviolet (UV) light received signals. An improved calculation method of the wavelet thresholds and a new threshold function are proposed. The new threshold function avoids the discontinuity of the traditional hard threshold function. It can also avoid the constant deviation caused by the traditional soft threshold function. The improved threshold calculation method takes into account the effect of the wavelet decomposition level, and the simulation results show the effectiveness of the proposed method. Compared with other methods, the method proposed in this paper can obtain a better denoising effect.

Highly Basic Clusters in the Herpes Simplex Virus 1 Nuclear Egress Complex Drive Membrane Budding by Inducing Lipid Ordering

During replication of herpesviruses, capsids escape from the nucleus into the cytoplasm by budding at the inner nuclear membrane. This unusual process is mediated by the viral nuclear egress complex (NEC) that deforms the membrane around the capsid by oligomerizing into a hexagonal, membrane-bound scaffold. Here, we found that highly basic membrane-proximal regions (MPRs) of the NEC alter lipid order by inserting into the lipid headgroups and promote negative Gaussian curvature. We also find that the electrostatic interactions between the MPRs and the membranes are essential for membrane deformation. One of the MPRs is phosphorylated by a viral kinase during infection, and the corresponding phosphomimicking mutations block capsid nuclear egress. We show that the same phosphomimicking mutations disrupt the NEC-membrane interactions and inhibit NEC-mediated budding in vitro, providing a biophysical explanation for the in vivo phenomenon. Our data suggest that the NEC generates negative membrane curvature by both lipid ordering and protein scaffolding and that phosphorylation acts as an off switch that inhibits the membrane-budding activity of the NEC to prevent capsid-less budding. IMPORTANCE Herpesviruses are large viruses that infect nearly all vertebrates and some invertebrates and cause lifelong infections in most of the world's population. During replication, herpesviruses export their capsids from the nucleus into the cytoplasm by an unusual mechanism in which the viral nuclear egress complex (NEC) deforms the nuclear membrane around the capsid. However, how membrane deformation is achieved is unclear. Here, we show that the NEC from herpes simplex virus 1, a prototypical herpesvirus, uses clusters of positive charges to bind membranes and order membrane lipids. Reducing the positive charge or introducing negative charges weakens the membrane deforming ability of the NEC. We propose that the virus employs electrostatics to deform nuclear membrane around the capsid and can control this process by changing the NEC charge through phosphorylation. Blocking NEC-membrane interactions could be exploited as a therapeutic strategy.

Extraction of Weak Spectroscopic Signals with High Fidelity: Examples from ESR

Noise impedes experimental studies by reducing signal resolution and/or suppressing weak signals. Signal averaging and filtering are the primary methods used to reduce noise, but they have limited effectiveness and lack capabilities to recover signals at low signal-to-noise ratios (SNRs). We utilize a wavelet transform-based approach to effectively remove noise from spectroscopic data. The wavelet denoising method we use is a significant improvement on standard wavelet denoising approaches. We demonstrate its power in extracting signals from noisy spectra on a variety of signal types ranging from hyperfine lines to overlapped peaks to weak peaks overlaid on strong ones, drawn from electron-spin-resonance spectroscopy. The results show that one can accurately extract details of complex spectra, including retrieval of very weak ones. It accurately recovers signals at an SNR of ∼1 and improves the SNR by about 3 orders of magnitude with high fidelity. Our examples show that one is now able to address weaker SNR signals much better than by previous methods. This new wavelet approach can be successfully applied to other spectroscopic signals.

Automatic determination of the regularization weighting for wavelet-based compressed sensing MRI reconstructions

PURPOSE

To present a method that automatically, rapidly, and in a noniterative manner determines the regularization weighting for wavelet-based compressed sensing reconstructions. This method determines level-specific regularization weighting factors from the wavelet transform of the image obtained from zero-filling in k-space.

METHODS

We compare reconstruction results obtained by our method, λ auto , to the ones obtained by the L-curve, λ Lcurve , and the minimum NMSE, λ NMSE . The comparisons are done using in vivo data; then, simulations are used to analyze the impact of undersampling and noise. We use NMSE, Pearson's correlation coefficient, high-frequency error norm, and structural similarity as reconstruction quality indices.

RESULTS

Our method, λ auto , provides improved reconstructed image quality to that obtained by λ Lcurve regardless of undersampling or SNR and comparable quality to λ NMSE at high SNR. The method determines the regularization weighting prospectively with negligible computational time.

CONCLUSION

Our main finding is an automatic, fast, noniterative, and robust procedure to determine the regularization weighting. The impact of this method is to enable prospective and tuning-free wavelet-based compressed sensing reconstructions.

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are "db9" and "sym9" from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

The influence of errors in the processes of detection and then reduction of surface topography measurement noise is of great importance; many research papers are concerned with the definition of this type of measurement error. This paper presents the influence of high-frequency measurement noise, defined for various types of surface textures, e.g., two-process plateau-honed, turned, ground, or isotropic. Procedures for the processing of raw measured data as a detection of the high-frequency errors from the results of surface topography measurements were proposed and verified (compared) according to the commonly used (available in the commercial software of the measuring equipment) algorithms. It was assumed that commonly used noise-separation algorithms did not always provide consistent results for two process textures with the valley-extraction analysis; as a result, some free-of-dimple (part of the analyzed detail where dimples do not exist) areas were not carefully considered. Moreover, the influence of measured data processing errors on surface topography parameter calculation was not comprehensively studied with high-frequency measurement noise assessments. It was assumed that the application of the Wavelet Noise Extraction Procedure (WNEP) might be exceedingly valuable when the reduction of a disparate range of measured frequencies (measurement noise) was carefully considered.

Application of adaptive wavelet thresholding to recovery geoacoustic signal pulse waveforms

Recorded geoacoustic signals often contain noise and interference. Their appearance is caused by various reasons, e.g. of propagation environment heterogeneity, weather condition influence, human activity, etc. So, geoacoustic emission signals contain a persistent background noise that changes in intensity over time. This noise significantly distorts the geoacoustic pulse waveforms and thus complicates analysis of the signal characteristics. The article presents results of estimating the geoacoustic signal background noise. On the basis of these estimates, a method of adaptive wavelet thresholding is proposed to remove noise from the signal and recovery the single pulse waveforms. In conclusion, the results of a computational experiment are presented. They confirm effectiveness of using the chosen method for the geoacoustic signal preprocessing. The work was carried out as part of the implementation of the state task AAAA-A21-121011290003-0.

Microsecond Exchange Processes Studied by Two-Dimensional ESR at 95 GHz

Exchange processes which include conformational change, protonation/deprotonation, and binding equilibria are routinely studied by 2D exchange NMR techniques, where information about the exchange of nuclei between environments with different NMR shifts is obtained from the development of cross-peaks. Whereas 2D NMR enables the real time study of millisecond and slower exchange processes, 2D ESR in the form of 2D-ELDOR (two-dimensional electron-electron double resonance) has the potential for such studies over the nanosecond to microsecond real time scales. Cross-peak development due to chemical exchange has been seen previously for semiquinones in ESR, but this is not possible for most common ESR probes, such as nitroxides, studied at typical ESR frequencies because, unlike NMR, the exchanging states yield ESR signals that are not resolved from each other within their respective line widths. But at 95 GHz, it becomes possible to resolve them in many cases because of the increased g-factor resolution. The 95 GHz instrumental developments occurring at ACERT now enable such studies. We demonstrate these new capabilities in two studies: (A) the protonation/deprotonation process for a pH-sensitive imidazoline spin label in aqueous solution where the exchange rate and the population ratio of the exchanging states are controlled by the concentration and pH of the buffer solution, respectively, and (B) a nitroxide radical partitioning between polar (aqueous) and nonpolar (phospholipid) environments in multilamellar lipid vesicles, where the cross-peak development arises from the exchange of the nitroxide between the two phases. This work represents the first example of the observation and analysis of cross-peaks arising from chemical exchange processes involving nitroxide spin labels.

A Global Minimization Toolkit for Batch-Fitting and χ2 Cluster Analysis of CW-EPR Spectra

Electron paramagnetic resonance spectroscopy (EPR) is a uniquely powerful technique for characterizing conformational dynamics at specific sites within a broad range of molecular species in water. Computational tools for fitting EPR spectra have enabled dynamics parameters to be determined quantitatively. These tools have dramatically broadened the capabilities of EPR dynamics analysis, however, their implementation can easily lead to overfitting or problems with self-consistency. As a result, dynamics parameters and associated properties become difficult to reliably determine, particularly in the slow-motion regime. Here, we present an EPR analysis strategy and the corresponding computational tool for batch-fitting EPR spectra and cluster analysis of the χ2 landscape in Linux. We call this tool CSCA (Chi-Squared Cluster Analysis). The CSCA tool allows us to determine self-consistent rotational diffusion rates and enables calculations of activation energies of diffusion from Arrhenius plots. We demonstrate CSCA using a model system designed for EPR analysis: a self-assembled nanoribbon with radical electron spin labels positioned at known distances off the surface. We anticipate that the CSCA tool will increase the reproducibility of EPR fitting for the characterization of dynamics in biomolecules and soft matter.

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

Demodulation Method for Loran-C at Low SNR Based on Envelope Correlation-Phase Detection

Loran-C is the most important backup and supplement system for the global navigation satellite system (GNSS). However, existing Loran-C demodulation methods are easily affected by noise and skywave interference (SWI). Therefore, this article proposes a demodulation method based on Loran-C pulse envelope correlation-phase detection (EC-PD), in which EC has two implementation schemes, namely moving average-cross correlation and matched correlation, to reduce the effects of noise and SWI. The mathematical models of the EC, calculation of the signal-to-noise ratio (SNR) gain, and selection of the EC schemes are given. The simulation results show that compared with an existing method, the proposed method has clear advantages: (1) The demodulation SNR threshold under Gaussian channel is only -2 dB, a reduction of 12.5 dB; (2) The probability of the demodulated SNR threshold, being less than zero under the SWI environment, can reach 0.78, a 26-fold increase. The test results show that the average data availability of the proposed method is 3.3 times higher than that of the existing method. Thus, our demodulation method has higher engineering application value. This will improve the performance of the modern Loran-C system, making it a more reliable backup for the GNSS.

Adjustable Handheld Probe Design for Photoacoustic Imaging:Experimental Validation

Photoacoustic (PA) imaging is an emerging imaging technique that combines light illumination and ultrasound detection. Conventional design of PA probe usually combines light and sound in a straightforward way without adjustable capability. In this paper, we propose a new PA imaging system based on light-adjustable handheld probe. The proposed PA imaging system includes a control unit that can adaptively find the light illumination pattern for the PA signal generation with optimized signal-to-noise ratio (SNR). Compared with traditional design of PA probe, our proposed apparatus enables the following advantages: (1) The system can automatically find the specific position parameters of the illumination scheme, and obtain the PA signal with optimized SNR by implementing a scanning process. (2) The proposed apparatus can be fully automatic and adaptive by electronic control. By scanning the sample, it can automatically obtain the optimized light exposure position. (3) Spot size and distance are adjustable. Both simulation and experimental results demonstrate the feasibility of the proposed system to have great potential in biomedical imaging with versatile configurations.

Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

Wavelet-based detection of scaling behavior in noisy experimental data

The detection of power laws in real data is a demanding task for several reasons. The two most frequently met are that (i) real data possess noise, which affects the power-law tails significantly, and (ii) there is no solid tool for discrimination between a power law, valid in a specific range of scales, and other functional forms like log-normal or stretched exponential distributions. In the present report we demonstrate, employing simulated and real data, that using wavelets it is possible to overcome both of the above-mentioned difficulties and achieve secure detection of a power law and an accurate estimation of the associated exponent.

Calcium Ions Directly Interact with the Ebola Virus Fusion Peptide To Promote Structure-Function Changes That Enhance Infection

Ebola virus disease is a serious global health concern given its periodic occurrence, high lethality, and the lack of approved therapeutics. Certain drugs that alter intracellular calcium, particularly in endolysosomes, have been shown to inhibit Ebola virus infection; however, the underlying mechanism is unknown. Here, we provide evidence that Zaire ebolavirus (EBOV) infection is promoted in the presence of calcium as a result of the direct interaction of calcium with the EBOV fusion peptide (FP). We identify the glycoprotein residues D522 and E540 in the FP as functionally critical to EBOV's interaction with calcium. We show using spectroscopic and biophysical assays that interactions of the fusion peptide with Ca²⁺ ions lead to lipid ordering in the host membrane during membrane fusion, and these changes are promoted at low pH and can be correlated with infectivity. We further demonstrate using circular dichroism spectroscopy that calcium interaction with the fusion peptide promotes α-helical structure of the fusion peptide, a conformational change that enhances membrane fusion, as validated using functional assays of membrane fusion. This study shows that calcium directly targets the Ebola virus fusion peptide and influences its conformation. As these residues are highly conserved across the Filoviridae, calcium's impact on fusion, and subsequently infectivity, is a key interaction that can be leveraged for developing strategies to defend against Ebola infection. This mechanistic insight provides a rationale for the use of calcium-interfering drugs already approved by the FDA as therapeutics against Ebola and enables further development of novel drugs to combat the virus.

SNR enhancement in brillouin microspectroscopy using spectrum reconstruction

Brillouin spectroscopy can suffer from low signal-to-noise ratios (SNRs). Such low SNRs can render common data analysis protocols unreliable, especially for SNRs below ∼10. In this work we exploit two denoising algorithms, namely maximum entropy reconstruction (MER) and wavelet analysis (WA), to improve the accuracy and precision in determination of Brillouin shifts and linewidth. Algorithm performance is quantified using Monte-Carlo simulations and benchmarked against the Cramér-Rao lower bound. Superior estimation results are demonstrated even at low SNRs (≥ 1). Denoising is furthermore applied to experimental Brillouin spectra of distilled water at room temperature, allowing the speed of sound in water to be extracted. Experimental and theoretical values were found to be consistent to within ±1% at unity SNR.

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.

Improved Low-Rank Filtering of MR Spectroscopic Imaging Data With Pre-Learnt Subspace and Spatial Constraints

OBJECTIVE

To investigate the use of pre-learnt subspace and spatial constraints for denoising magnetic resonance spectroscopic imaging (MRSI) data.

METHOD

We exploit the partial separability or subspace structures of high-dimensional MRSI data for denoising. More specifically, we incorporate a subspace model with pre-learnt spectral basis into the low-rank approximation (LORA) method. Spectral basis is determined based on empirical prior distributions of the spectral parameters variations learnt from auxiliary training data; spatial priors are also incorporated as is done in LORA to further improve denoising performance.

RESULTS

The effects of the explicit subspace and spatial constraints in reducing estimation bias and variance have been analyzed using Cramér-Rao Lower bound analysis, Monte-Carlo study, and experimental study.

CONCLUSION

The denoising effectiveness of LORA can be significantly improved by incorporating pre-learnt spectral basis and spatial priors into LORA.

SIGNIFICANCE

This study provides an effective method for denoising MRSI data along with comprehensive analyses of its performance. The proposed method is expected to be useful for a wide range of studies using MRSI.

Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations

Nowadays, the high rate GNSS (Global Navigation Satellite Systems) positioning methods are widely used as a complementary tool to other geotechnical sensors, such as accelerometers, seismometers, and inertial measurement units (IMU), to evaluate dynamic displacement responses of engineering structures. However, the most common problem in structural health monitoring (SHM) using GNSS is the presence of surrounding structures that cause multipath errors in GNSS observations. Skyscrapers and high-rise buildings in metropolitan cities are generally close to each other, and long-span bridges have towers, main cable, and suspender cables. Therefore, multipath error in GNSS observations, which is typically added to the measurement noise, is inevitable while monitoring such flexible engineering structures. Unlike other errors like atmospheric errors, which are mostly reduced or modeled out, multipath errors are the largest remaining unmanaged error sources. The high noise levels of high-rate GNSS solutions limit their structural monitoring application for detecting load-induced semi-static and dynamic displacements. This study investigates the estimation of accurate dynamic characteristics (frequency and amplitude) of structural or seismic motions derived from multipath-affected high-rate GNSS observations. To this end, a novel hybrid model using both wavelet-based multiscale principal component analysis (MSPCA) and wavelet transform (MSPCAW) is designed to extract the amplitude and frequency of both GNSS relative- and PPP- (Precise Point Positioning) derived displacement motions. To evaluate the method, a shaking table with a GNSS receiver attached to it, collecting 10 Hz data, was set up close to a building. The table was used to generate various amplitudes and frequencies of harmonic motions. In addition, 50-Hz linear variable differential transformer (LVDT) observations were collected to verify the MSMPCAW model by comparing their results. The results showed that the MSPCAW could be efficiently used to extract the dynamic characteristics of noisy dynamic movements under seismic loads. Furthermore, the dynamic behavior of seismic motions can be extracted accurately using GNSS-PPP, and its dominant frequency equals that extracted by LVDT and relative GNSS positioning method. Its accuracy in determining the amplitude approaches 91.5% relative to the LVDT observations.

Quo vadis EPR?

Complexity of paramagnetic catalysts and materials increases, and the same applies to systems targeted by integrative structural biology. Hence, EPR spectroscopists must find ways to enhance information content of their data. I argue that a third major wave of method development in EPR spectroscopy, which is triggered by recent advances in digital electronics and computing, can achieve this. Transfer of NMR methods to EPR will go on, but part of the new EPR methodology will depend on completely new concepts.

Robust EOG-based saccade recognition using multi-channel blind source deconvolution

Human activity recognition (HAR) is a research hotspot in the field of artificial intelligence and pattern recognition. The electrooculography (EOG)-based HAR system has attracted much attention due to its good realizability and great application potential. Focusing on the signal processing method of the EOG-HAR system, we propose a robust EOG-based saccade recognition using the multi-channel convolutional independent component analysis (ICA) method. To establish frequency-domain observation vectors, short-time Fourier transform (STFT) is used to process time-domain EOG signals by applying the sliding window technique. Subsequently, we apply the joint approximative diagonalization of eigenmatrix (JADE) algorithm to separate the mixed signals and choose the "clean" saccadic source to extract features. To address the problem of permutation ambiguity in a case with a six-channel condition, we developed a constraint direction of arrival (DOA) algorithm that can automatically adjust the order of eye movement sources according to the constraint angle. Recognition experiments of four different saccadic EOG signals (i.e. up, down, left and right) were conducted in a laboratory environment. The average recognition ratios over 13 subjects were 95.66% and 97.33% under the between-subjects test and the within-subjects test, respectively. Compared with "bandpass filtering", "wavelet denoising", "extended infomax algorithm", "frequency-domain JADE algorithm" and "time-domain JADE algorithm, the recognition ratios obtained relative increments of 4.6%, 3.49%, 2.85%, 2.81% and 2.91% (within-subjects test) and 4.91%, 3.43%, 2.21%, 2.24% and 2.28% (between-subjects test), respectively. The experimental results revealed that the proposed algorithm presents robust classification performance in saccadic EOG signal recognition.

Insights into histidine kinase activation mechanisms from the monomeric blue light sensor EL346

Translation of environmental cues into cellular behavior is a necessary process in all forms of life. In bacteria, this process frequently involves two-component systems in which a sensor histidine kinase (HK) autophosphorylates in response to a stimulus before subsequently transferring the phosphoryl group to a response regulator that controls downstream effectors. Many details of the molecular mechanisms of HK activation are still unclear due to complications associated with the multiple signaling states of these large, multidomain proteins. To address these challenges, we combined complementary solution biophysical approaches to examine the conformational changes upon activation of a minimal, blue-light-sensing histidine kinase from Erythrobacter litoralis HTCC2594, EL346. Our data show that multiple conformations coexist in the dark state of EL346 in solution, which may explain the enzyme's residual dark-state activity. We also observe that activation involves destabilization of the helices in the dimerization and histidine phosphotransfer-like domain, where the phosphoacceptor histidine resides, and their interactions with the catalytic domain. Similar light-induced changes occur to some extent even in constitutively active or inactive mutants, showing that light sensing can be decoupled from activation of kinase activity. These structural changes mirror those inferred by comparing X-ray crystal structures of inactive and active HK fragments, suggesting that they are at the core of conformational changes leading to HK activation. More broadly, our findings uncover surprising complexity in this simple system and allow us to outline a mechanism of the multiple steps of HK activation.

Wavelet Denoising of High-Bandwidth Nanopore and Ion-Channel Signals

Recent work has pushed the noise-limited bandwidths of solid-state nanopore conductance recordings to more than 5 MHz and of ion channel conductance recordings to more than 500 kHz through the use of integrated complementary metal-oxide-semiconductor (CMOS) integrated circuits. Despite the spectral spread of the pulse-like signals that characterize these recordings when a sinusoidal basis is employed, Bessel filters are commonly used to denoise these signals to acceptable signal-to-noise ratios (SNRs) at the cost of losing many of the faster temporal features. Here, we report improvements to the SNR that can be achieved using wavelet denoising instead of Bessel filtering. When combined with state-of-the-art high-bandwidth CMOS recording instrumentation, we can reduce baseline noise levels by over a factor of 4 compared to a 2.5 MHz Bessel filter while retaining transient properties in the signal comparable to this filter bandwidth. Similarly, for ion-channel recordings, we achieve a temporal response better than a 100 kHz Bessel filter with a noise level comparable to that achievable with a 25 kHz Bessel filter. Improvements in SNR can be used to achieve robust statistical analyses of these recordings, which may provide important insights into nanopore translocation dynamics and mechanisms of ion-channel function.

Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Three-dimensional (3D) mapping of power lines is very important for power line inspection. Many remotely-sensed data products like light detection and ranging (LiDAR) have been already studied for power line surveys. More and more data are being obtained via photogrammetric measurements. This increases the need for the implementation of advanced processing techniques. In recent years, there have been several developments in visualisation techniques using UAV (unmanned aerial vehicle) platform photography. The most modern of such imaging systems have the ability to generate dense point clouds. However, image-based point cloud accuracy is very often various (unstable) and dependent on the radiometric quality of images and the efficiency of image processing algorithms. The main factor influencing the point cloud quality is noise. Such problems usually arise with data obtained via low-cost UAV platforms. Therefore, generated point clouds representing power lines are usually incomplete and noisy. To obtain a complete and accurate 3D model of power lines and towers, it is necessary to develop improved data processing algorithms. The experiment tested the algorithms on power lines with different voltages. This paper presents the wavelet-based method of processing data acquired with a low-cost UAV camera. The proposed, original method involves the application of algorithms for coarse filtration and precise filtering. In addition, a new way of calculating the recommended flight height was proposed. At the end, the accuracy assessment of this two-stage filtration process was examined. For this, point quality indices were proposed. The experimental results show that the proposed algorithm improves the quality of low-cost point clouds. The proposed methods improve the accuracy of determining the parameters of the lines by more than twice. About 10% of noise is reduced by using the wavelet-based approach.

A radical containing injectable in-situ-oleogel and emulgel for prolonged in-vivo oxygen measurements with CW EPR

Molecular oxygen, reactive oxygen species and free radicals derived from oxygen play important roles in a broad spectrum of physiological and pathological processes. The quantitative measurement of molecular oxygen in tissues by electron paramagnetic resonance (EPR) has great potential for understanding and diagnosing a number of diseases, and for developing and guiding therapies. This requires improvements in the free radical probe systems that sense and report molecular oxygen levels in vivo. We report on the encapsulation of existing free radical probes in lipophilic gel implants: an in-situ-oleogel and an emulgel, based only on well-known, safe excipients for the incorporation of lipophilic and hydrophilic radicals, respectively. The EPR signals of encapsulated radicals were not altered compared to dissolved radicals. The high solubility of oxygen in lipophilic solvents enhanced oxygen sensitivity. The gels extended the lifetime of the radicals in tissues from tens of minutes to many days, simplifying studies with extended series of measurements. The encapsulated radicals showed a good in vivo response to changes in oxygen supply and seem to circumvent concerns from toxicity of the radical probes. These gels simplify the development of new oxygen-sensitive free radical probes for EPR oximetry by making their in vivo stability, persistence and toxicity a function of the encapsulating gel and not a set of additional requirements for the free radical probe.

Singular Value Decomposition Method To Determine Distance Distributions in Pulsed Dipolar Electron Spin Resonance: II. Estimating Uncertainty

This paper is a continuation of the method introduced by Srivastava and Freed (2017) that is a new method based on truncated singular value decomposition (TSVD) for obtaining physical results from experimental signals without any need for Tikhonov regularization or other similar methods that require a regularization parameter. We show here how to estimate the uncertainty in the SVD-generated solutions. The uncertainty in the solution may be obtained by finding the minimum and maximum values over which the solution remains converged. These are obtained from the optimum range of singular value contributions, where the width of this region depends on the solution point location (e.g., distance) and the signal-to-noise ratio (SNR) of the signal. The uncertainty levels typically found are very small with substantial SNR of the (denoised) signal, emphasizing the reliability of the method. With poorer SNR, the method is still satisfactory but with greater uncertainty, as expected. Pulsed dipolar electron spin resonance spectroscopy experiments are used as an example, but this TSVD approach is general and thus applicable to any similar experimental method wherein singular matrix inversion is needed to obtain the physically relevant result. We show that the Srivastava-Freed TSVD method along with the estimate of uncertainty can be effectively applied to pulsed dipolar electron spin resonance signals with SNR > 30, and even for a weak signal (e.g., SNR ≈ 3) reliable results are obtained by this method, provided the signal is first denoised using wavelet transforms (WavPDS).

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters (Rc2 = 0.834, RMSEC = 0.262, RPDc = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) (Rc2 = 0.816, RMSEC = 0.276, RPDc = 2.331) and raw spectra (Rc2 = 0.822, RMSEC = 0.271, RPDc = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.

Nucleotide Spin Labeling for ESR Spectroscopy of ATP-Binding Proteins

Site-directed spin labeling of proteins by chemical modification of engineered cysteine residues with the molecule MTSSL (1-Oxyl-2,2,5,5-tetramethylpyrroline-3-methyl methanethiosulfonate) has been an invaluable tool for conducting double electron electron resonance (DEER) spectroscopy experiments. However, this method is generally limited to recombinant proteins with a limited number of reactive Cys residues that when modified will not impair protein function. Here we present a method that allows for spin-labeling of protein nucleotide binding sites by adenosine diphosphate (ADP) modified with a nitroxide moiety on the β-phosphate (ADP-β-S-SL). The synthesis of this ADP analog is straightforward and isolation of pure product is readily achieved on a standard reverse-phase high-performance liquid chromatography (HPLC) system. Furthermore, analyses of isolated ADP-β-S-SL by LC-mass spectrometry confirm that the molecule is extremely stable under ambient conditions. The crystal structure of ADP-β-S-SL bound to the ATP pocket of the histidine kinase CheA reveals specific targeting of the probe, whose nitroxide moiety is mobile on the protein surface. Continuous wave and pulsed ESR measurements demonstrate the capability of ADP-β-S-SL to report on active site environment and provide reliable DEER distance constraints.

Site-Specific Incorporation of a Cu2+ Spin Label into Proteins for Measuring Distances by Pulsed Dipolar Electron Spin Resonance Spectroscopy

Pulsed dipolar electron spin resonance spectroscopy (PDS) is a powerful tool for measuring distances in solution-state macromolecules. Paramagnetic metal ions, such as Cu²⁺, are used as spin probes because they can report on metalloprotein features and can be spectroscopically distinguished from traditional nitroxide (NO)-based labels. Here, we demonstrate site-specific incorporation of Cu²⁺ into non-metalloproteins through the use of a genetically encodable non-natural amino acid, 3-pyrazolyltyrosine (PyTyr). We first incorporate PyTyr in cyan fluorescent protein to measure Cu²⁺-to-NO distances and examine the effects of solvent conditions on Cu²⁺ binding and protein aggregation. We then apply the method to characterize the complex formed by the histidine kinase CheA and its target response regulator CheY. The X-ray structure of CheY-PyTyr confirms Cu labeling at PyTyr but also reveals a secondary Cu site. Cu²⁺-to-NO and Cu²⁺-to-Cu²⁺ PDS measurements of CheY-PyTyr with nitroxide-labeled CheA provide new insights into the conformational landscape of the phosphotransfer complex and have implications for kinase regulation.