Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals.

Two-dimensional electron spin resonance (2D ESR) spectroscopy is a unique experimental technique for probing protein structure and dynamics, including processes that occur at the microsecond time scale. While it provides significant resolution enhancement over the one-dimensional experimental setup, spectral broadening and noise make extraction of spectral information highly challenging. Traditionally, two-dimensional Fourier transform (2D FT) is applied for the analysis of 2D ESR signals, although its efficiency is limited to stationary signals. In addition, it often fails to resolve overlapping peaks in 2D ESR. In this work, we propose a time-frequency analysis of 2D time-domain signals, which identifies all frequency peaks by decoupling a signal into its distinct constituent components via projection on the time-frequency plane. The method utilizes 2D undecimated discrete wavelet transform (2D UDWT) as an intermediate step in the analysis, followed by signal reconstruction and 2D FT. We have applied the method to a simulated 2D double quantum coherence (DQC) signal for validation and a set of experimental 2D ESR signals, demonstrating its efficiency in resolving overlapping peaks in the frequency domain, while displaying frequency evolution with time in case of non-stationary data.

Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR.

Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.

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.

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.

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.

Cofactors are essential constituents of stable and seeding-active tau fibrils.

Amyloid fibrils are cross-ß-rich aggregates that are exceptionally stable forms of protein assembly. Accumulation of tau amyloid fibrils is involved in many neurodegenerative diseases, including Alzheimer's disease (AD). Heparin-induced aggregates have been widely used and assumed to be a good tau amyloid fibril model for most biophysical studies. Here we show that mature fibrils made of 4R tau variants, prepared with heparin or RNA, spontaneously depolymerize and release monomers when their cofactors are removed. We demonstrate that the cross-ß-sheet assembly formed in vitro with polyanion addition is unstable at room temperature. We furthermore demonstrate high seeding capacity with transgenic AD mouse brain-extracted tau fibrils in vitro that, however, is exhausted after one generation, while supplementation with RNA cofactors resulted in sustained seeding over multiple generations. We suggest that tau fibrils formed in brains are supported by unknown cofactors and inhere higher-quality packing, as reflected in a more distinct conformational arrangement in the mouse fibril-seeded, compared with heparin-induced, tau fibrils. Our study suggests that the role of cofactors in tauopathies is a worthy focus of future studies, as they may be viable targets for diagnosis and therapeutics.

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).

A New Wavelet Denoising Method for Experimental Time-Domain Signals: Pulsed Dipolar Electron Spin Resonance.

We adapt a new wavelet-transform-based method of denoising experimental signals to pulse-dipolar electron-spin resonance spectroscopy (PDS). We show that signal averaging times of the time-domain signals can be reduced by as much as 2 orders of magnitude, while retaining the fidelity of the underlying signals, in comparison with noiseless reference signals. We have achieved excellent signal recovery when the initial noisy signal has an SNR ≳ 3. This approach is robust and is expected to be applicable to other time-domain spectroscopies. In PDS, these time-domain signals representing the dipolar interaction between two electron spin labels are converted into their distance distribution functions P(r), usually by regularization methods such as Tikhonov regularization. The significant improvements achieved by using denoised signals for this regularization are described. We show that they yield P(r)'s with more accurate detail and yield clearer separations of respective distances, which is especially important when the P(r)'s are complex. Also, longer distance P(r)'s, requiring longer dipolar evolution times, become accessible after denoising. In comparison to standard wavelet denoising approaches, it is clearly shown that the new method (WavPDS) is superior.

Fluorine-18 FDG PET/CT and New NIMS Grading System for Chemotherapy Response in Breast Cancer.

Background: Positron emission tomography with computed tomography (PET-CT) using fluorine 18-fluorodeoxyglucose (F-18 FDG) is increasingly used to stage patients with locally advanced breast cancer and for assessing treatment response after neoadjuvant chemotherapy (NACT). Aims and Objectives: The aim of the study was to assess the correlation between PET-CT parameters and pathologic response of breast primary after NACT in breast cancer patients and to devise a grading system called NIMS grading system for response assessment using PET quantitative parameters. Materials and Methods: 55 patients who underwent F-18 FDG PET-CT before starting the therapy and again after completion of therapy were identified and included in the study. The clinical data and the histopathologic findings were recorded. All the patients received chemotherapy followed by surgery with axillary lymph node dissection. The PET-CT results were interpreted both qualitatively by visual analysis and quantitatively by estimating maximum Standardized uptake values(SUVmax) and other parameters - SUVmean, SUL, SUVBSA, Metabolic tumor volume (MTV) and Total lesion glycolysis (TLG). Results: The sensitivity and specificity of F-18 FDG PET-CT to detect the residual disease after neoadjuvant chemotherapy was 75.6% & 92.8% respectively. Differences between complete response and residual disease were significant for ΔSUVmax(p=0.005), ΔSUVmean(p=0.006), ΔSUL (0.005) and ΔSUVBSA(0.004), while ΔMTV and ΔTLG were not significantly different between the two groups. The new NIMS grading system included scoring of ΔSUVmax, ΔSUVBSA, ΔTLG and ΔMTV on scale of 1 to 4 and correlated well with PERCIST criteria. Conclusion: F-18 FDG PET-CT had a good accuracy in the detection of residual disease after completion of NACT. Pre chemotherapy PET-CT is not adequate to predict the response of primary tumour to chemotherapy. However, changes in the values of various PET-CT parameters are a sensitive tool to assess the response to chemotherapy. The new grading system is easy to use and showed good correlation to PERCIST.

Optimal Wavelet Selection for Signal Denoising.

Wavelet denoising plays a key role in removing noise from signals and is widely used in many applications. In denoising, selection of the mother wavelet is desirable for maximizing the separation of noise and signal coefficients in the wavelet domain for effective noise thresholding. At present, wavelet selection is carried out in a heuristic manner or using a trial-and-error that is time consuming and prone to error, including human bias. This paper introduces a universal method to select optimal wavelets based on the sparsity of Detail components in the wavelet domain, an empirical approach. A mean of sparsity change ( µ s c ) parameter is defined that captures the mean variation of noisy Detail components. The efficacy of the presented method is tested on simulated and experimental signals from Electron Spin Resonance spectroscopy at various SNRs. The results reveal that the µ s c values of signal vary abruptly between wavelets, whereas for noise it displays similar values for all wavelets. For low Signal-to-Noise Ratio (SNR) data, the change in µ s c between highest and second highest value is ≈ 8 - 10% and for high SNR data it is around 5%. The mean of sparsity change increases with the SNR of the signal, which implies that multiple wavelets can be used for denoising a signal, whereas, the signal with low SNR can only be efficiently denoised with a few wavelets. Either a single wavelet or a collection of optimal wavelets (i.e., top five wavelets) should be selected from the highest µ s c values. The code is available on GitHub and the signalsciencelab.com website.

Lymphoscintigraphy - Beyond Lymphedema.

Lymphoscintigraphy is an established modality for imaging the lymphatic system using radiocolloids and is routinely indicated to find the cause of limb lymphedema. However, in this case series, we are highlighting other less-known indications of lymphoscintigraphy like chylothorax and chyluria which present as lymphatic leaks in the thorax and abdomen, respectively. Once the site of the lymphatic leak is established by lymphoscintigraphy, definitive management like thoracic duct ligation or sclerotherapy can be done. The other indication discussed is postrenal transplant perinephric fluid collection which can be challenging to confirm whether it is urinoma, lymphocele, or any other collection. And finally, sentinel lymph node localization is another, now, well-established indication of lymphoscintigraphy.

Monoarticular carcinomatous knee joint arthritis as an initial presentation of cervical carcinoma.

When it comes to cancer, one can expect the unexpected. The clinical presentations can be very bizarre. One of these uncommon presentations is monoarticular arthritis. The age group affected by cancer and arthritis are similar. However, the possibility of joint pain being secondary to metastatic involvement does not come to mind easily. In this report, a 65-year-old postmenopausal woman presented with complaint of pain and restricted movement of the right knee joint, in whom the clinical and magnetic resonance imaging features were suggestive of infective monoarthritis. However, synovial fluid aspirate showed presence of malignant cells. Hence, patient was evaluated with whole-body (18)F-fluorodeoxyglucose positron emission tomography computed tomography which detected primary malignancy of the cervix with regional nodal and right knee joint metastasis. To our knowledge, this is the first reported case of cervical malignancy with solitary skeletal metastasis involving the knee joint. The report also discusses overall incidence of malignant arthritis.

Differentiating Unimodal and Multimodal Distributions in Pulsed Dipolar Spectroscopy Using Wavelet Transforms.

Site directed spin labeling has enabled protein structure determination using electron spin resonance (ESR) pulsed dipolar spectroscopy (PDS). Small details in a distance distribution can be key to understanding important protein structure-function relationships. A major challenge has been to differentiate unimodal and overlapped multimodal distance distributions. They often yield similar distributions and dipolar signals. Current model-free distance reconstruction techniques such as Srivastava-Freed Singular Value Decomposition (SF-SVD) and Tikhonov regularization can suppress these small features in uncertainty and/or error bounds, despite being present. In this work, we demonstrate that continuous wavelet transform (CWT) can distinguish PDS signals from unimodal and multimodal distance distributions. We show that periodicity in CWT representation reflects unimodal distributions, which is masked for multimodal cases. This work is meant as a precursor to a cross-validation technique, which could indicate the modality of the distance distribution.

Bone Scan: Indications Revisited.

Skeletal scintigraphy is one of the most widely performed investigations in any nuclear medicine department. However, there has been a paradigm shift in the indications for which bone scan was performed in the past 3 decades, mainly due to advancement in other imaging modalities, better disease understanding, and the development of newer disease-specific guidelines. The metastatic indications for bone scans accounted for 60.3% of cases in 1998 which reduced to 15.5% in 2021 and nonmetastatic indications rose from 39.7% in 1998 to 84.5% in 2021. Fewer bone scans are being performed for the metastatic survey, and more scans are being performed for nononcological orthopedic and rheumatological indications. This article captures the journey of skeletal scintigraphy in the past three decades.

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.

Role of magnetic resonance imaging and 18-fluorodeoxyglucose positron emission tomography-computed tomography in identifying pain generators in patients with chronic low back pain.

Objective: Low back pain (LBP) is a major cause of pain and disability. Identification of the pathology accurately or the pain generators is sometimes difficult with the conventional modalities such as magnetic resonance imaging (MRI), computed tomography (CT), or X-ray. Nuclear medicine investigations such as single-photon emission CT (SPECT/CT) or 18-fluorodeoxyglucose positron emission tomography-CT (18-FDG PET-CT) have emerged as an adjuvant tool in these cases. In this study, we evaluated and analyzed the role of 18-FDG PET-CT in identifying active pain generators and the outcomes of interventions based on that compared to MRI. Methodology: This study included all patients who fell under inclusion criteria presented with chronic LBP with or without radiculopathy. History and clinical examination were done as well as Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores were calculated. All the patients underwent MRI lumbosacral spine with sacroiliac (SI) joint and 18-FDG PET-CT whole spine. Patients in whom PET-CT was positive and active pain generator was identified were managed for the specific level or pain generator responsible by appropriate modalities, i.e. surgery, interfacetal injections, transforaminal epidural injections, and SI joint injections. Patients in whom PET-CT was negative were managed according to the pain generator identified on the basis of MRI and clinical correlation. Patients were told to follow-up after 1 week and 1 month, and subsequent improvement was evaluated on the basis of VAS after 1 week and 1 month and ODI score after 1 month. Results: A total of 20 patients were included in the study, with a mean age of 41.9 ± 13.53 years. Twelve patients had multiple level pathology without the indication of significant pain generator and eight patients' symptoms did not correlate with the MRI findings. 18-FDG PET-CT was done in all patients. 10% (2/20) patients were identified with active pain generators on PET-CT which were not identified on MRI. Eleven out of twenty patients underwent intervention in the form of surgery or pain injections. The mean VAS and ODI score in the patients intervened on the basis of 18-FDG PET-CT improved by 70.59% and 50%, respectively, whereas in patients who underwent intervention on the basis of MRI had improvement in mean VAS and ODI score by 58.57% and 30.81%, respectively after 1 month. Conclusion: Inflammation and associated degenerative process in the spine is a continuous process and affects multiple levels and might not be easily picked up on MRI or other conventional modalities. Thus, 18-FDG PET-CT is useful in identifying these active inflammatory processes and thereby helping in the localization of active pain generators. Treating these active pain generators has a better outcome in patients after intervention in terms of better pain relief and quality of life and also reduces the levels being treated.

Neurocognitive Profile and 18 F-Fluorodeoxyglucose Positron Emission Tomography Brain Imaging Correlation in Children with Electrical Status Epilepticus during Sleep.

Objective Electrical status epilepticus in sleep (ESES) is defined by near-continuous epileptiform discharges during sleep along with cognitive, behavioral, and/or imaging abnormalities. We studied the neurocognitive profile and their correlation with 18 F fluorodeoxyglucose positron emission tomography (FDG PET) brain abnormalities in children with ESES. Methods Fourteen children with ESES with normal magnetic resonance imaging (MRI) from March to December 2019 were included. The intelligence quotient (IQ) and child behavior checklist (CBCL) scores were estimated using validated scales, and FDG PET brain was done at the same point of time to look for cerebral metabolic defects which was compared with a control group. Results Fourteen patients with a mean age of 8.2 ± 2.7 years were analyzed. The average duration of epilepsy was 6 ± 2.8 years. The mean IQ was 72.4 ± 18.2 and mean CBCL score was 37.3 ± 11.8. There was negative correlation between IQ and CBCL ( r = -0.55, p < 0.001). The duration of epilepsy also showed negative correlation with IQ ( r = -4.75, p < 0.001). FDG PET scan showed predominant thalamic hypometabolism in 12 of 14 patients (85.7%) on visual analysis with multiple other hypometabolic cortical and subcortical regions in the brain. The quantitative analysis showed significant difference in metabolism of basal ganglion when compared with control group. The total number of hypometabolic regions seen in the brain showed moderate positive correlation with CBCL score but no significant correlation with the IQ of cases. Conclusion This study demonstrates functional impairment of cerebral cortical, basal ganglia, and thalamic hypometabolism in a cohort of ESES patients with normal structural MRI brain study. There was a moderate correlation of extent and pattern of cerebral hypometabolism with the neuropsychological status of the child and duration of epilepsy.

Revisiting signal analysis in the big data era.

A fast and accurate time-frequency analysis is challenging for many applications, especially in the current big data era. A recent work introduces a fast continuous wavelet transform that effectively boosts the analysis speed without sacrificing the resolution of the result.

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.

A Rare Case of Systemic Cystic Angiomatosis Involving the Bones, Spleen, Liver, and Lungs.

Systemic cystic angiomatosis (SCA) is a rare disorder, usually involving the visceral organs with incidental detection during its insidious course. On radiography, it can present as multiple cystic lesions. In rare instances, it can present as mixed lesions (lytic, sclerotic) as was the case with our patient. The disease has a better prognosis than most vascular neoplasms involving the bones. We present a rare case of this disease, involving multiple organs, and presenting with an insidious onset.