Deconvolution Method Research Articles

ITGA5+ synovial fibroblasts orchestrate proinflammatory niche formation by remodelling the local immune microenvironment in rheumatoid arthritis.

To investigate the phenotypic heterogeneity of tissue-resident synovial fibroblasts and their role in inflammatory response in rheumatoid arthritis (RA). We used single-cell and spatial transcriptomics to profile synovial cells and spatial gene expressions of synovial tissues to identify phenotypic changes in patients with osteoarthritis, RA in sustained remission and active state. Immunohistology, multiplex immunofluorescence and flow cytometry were used to identify synovial fibroblasts subsets. Deconvolution methods further validated our findings in two cohorts (PEAC and R4RA) with treatment response. Cell coculture was used to access the potential cell-cell interactions. Adoptive transfer of synovial cells in collagen-induced arthritis (CIA) mice and bulk RNA sequencing of synovial joints further validate the cellular functions. We identified a novel tissue-remodelling CD45-CD31-PDPN+ITGA5+ synovial fibroblast population with unique transcriptome of POSTN, COL3A1, CCL5 and TGFB1, and enriched in immunoregulatory pathways. This subset was upregulated in active and lympho-myeloid type of RA, associated with an increased risk of multidrug resistance. Transforming growth factor (TGF)-β1 might participate in the differentiation of this subset. Moreover, ITGA5+ synovial fibroblasts might occur in early stage of inflammation and induce the differentiation of CXCL13hiPD-1hi peripheral helper T cells (TPHs) from naïve CD4+ T cells, by secreting TGF-β1. Intra-articular injection of ITGA5+ synovial fibroblasts exacerbates RA development and upregulates TPHs in CIA mice. We demonstrate that ITGA5+ synovial fibroblasts might regulate the RA progression by inducing the differentiation of CXCL13hiPD-1hi TPHs and remodelling the proinflammatory microenvironments. Therapeutic modulation of this subpopulation could therefore be a potential treatment strategy for RA.

Thermogravimetric Assessment of Biomass: Unravelling Kinetic, Chemical Composition and Combustion Profiles

Thermogravimetric analysis (TGA) was performed on six samples of pine wood, poplar sawdust and olive residue, and the kinetic parameters were evaluated by using isoconversional models. The hemicellulose, cellulose and lignin contents were also estimated using the Fraser–Suzuki deconvolution method. In addition, a range of thermodynamic parameters and combustion indices was calculated. Significant correlations were found between the kinetic, thermodynamic and combustion parameters. The ignition index showed an inverse relationship with the activation energy, whereas the burnout index correlated with enthalpy values for most samples. Higher heating rates during TGA increased ignition and combustion efficiencies but decreased combustion stability. Differences in behaviour were detected between the olive residues, which had a much higher lignin content (51.2–56.9%), and the woody biomass samples (24.2–29.2%). Moreover, the sample with the highest ash content also exhibited some distinctive characteristics, including the lowest high heating value and ignition index, coupled with the highest activation energy, indicating a less favourable combustion behaviour than the other samples. The particle size of the samples was also found to be critical for both combustion efficiency and safety.

A new approach to improve the Earth's polar motion prediction: on the deconvolution and convolution methods

A new approach to improve the Earth's polar motion prediction: on the deconvolution and convolution methods

Enhanced multi-layer Ramanujan decomposition: an effective rolling bearing condition monitoring method

ABSTRACT Due to the influence of strong noise, it is difficult to extract and identify the fault features of rolling bearing when it is slightly damaged, which greatly increases the risk of fault miss detection. In this paper, the enhanced multi-layer Ramanujan decomposition (EMRD) method is proposed. On the one hand, EMRD method adopts accurate spectrum segmentation technology (ASST) to realise adaptive frequency band division. ASST firstly minimises the frequency erosion caused by noise through prior-unknown blind deconvolution method based on SOSO (SOSO-PUBD), then obtains relatively pure frequency distribution through power spectral density function, and finally realises frequency band segmentation by minimum segmentation method. On the other hand, EMRD method obtains the optimal number of decomposition modes in the form of multi-layer decomposition, and selects the optimal mode by Ramanujan spectrum signal-to-noise ratio index. The comparative experimental results show that EMRD method has excellent accuracy of frequency band segmentation and meets the requirements of rolling bearing fault diagnosis.

Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers

Abstract Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. It is challenging to effectively eliminate background noise interference from time-frequency domain transformer defect detection techniques such as wavelet transformations, modal decomposition, etc. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum Fourier spectrum cyclostationarity blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.

Quantitative assessment of kidney split function and mean transit time in healthy patients using dynamic 18F-FDG PET/MRI studies with denoising and deconvolution methods making use of Legendre polynomials

PurposeOur objective was to assess a deconvolution and denoising technique based on Legendre polynomials compared to matrix deconvolution on dynamic 18F-FDG renography of healthy patients.MethodThe study was carried out and compared to the data of 24 healthy patients from a published study who underwent examinations with 99mTc-MAG3 planar scintigraphy and 18F-FDG PET/MRI. Due to corruption issues in some data used in the published article, post-publication measurements were provided. We have been warned that post-publication data were treated differently. The smoothing method switched from Bezier to Savitzky–Golay and the deconvolution from matrix-based (with Tikhonov Regularization) to Richardson–Lucy. A comparison of the split function and mean transit times of the published and post-publication data against our method based on Legendre polynomials was performed.ResultsFor split function, we only observed a good agreement between the processing methods for the 99mTc-MAG3 and the post-published data. No correlation was found between the split functions obtained on the 99mTc-MAG3 and the 18F-FDG, contrary to the published study. However, all calculated split function values for 18F-FDG and 99mTc-MAG3 were within the established normal range. For the mean transit time, the correlation was moderate with published data and very good with the post-publication measurements for both 99mTc-MAG3 and 18F-FDG. Bias of the Bland–Altman analysis of the mean transit times for 99mTc-MAG3 versus 18F-FDG was 1.1 min (SD 1.7 min) for the published data, − 0.11 min (SD 1.9 min) for the post-publication results and .05 min (SD 1.9 min) for our method.ConclusionsThe processing methods used in the original publication and in the post-publication work were quite complex and required adaptation of the fitting parameters for each individual and each type of examination. Our method did not require any specific adjustment; the same unmodified and fully automated algorithm was successfully applied to all data.

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER’s superior accuracy and robustness over existing methods.

Regional study of site effects on the high-frequency spectral-decay parameter

The high-frequency spectral-decay parameter κ has been used in the field of engineering seismology for several decades. The zero-distance κ value (κ0) is a site parameter in ground motion models and strong motion synthesis. Using surface-borehole pairs of strong motion records from the strong-motion seismograph network KiK-net, we explored the effect of site soil on κ0, specifically the contribution from ground motion directionality. In the target area (138°E−143°E, 36°N–40°N), and records from earthquakes at 50 pairs of stations were collected. All horizontal orthogonal records were rotated from 0° to 180° with an increment of 10° to capture the difference between the average κ0 on two horizontal components and the median value of the rotated κ0 at surface and borehole stations. The same process was applied to Δκ0, the difference between surface station κ0 and borehole station κ0, to measure the contribution by soil column. The nonlinear behavior of soils led to large shear strains, reduced stiffness, and decreased resonance frequency. Thus, the effect of soil nonlinearity on κ is investigated. In the same area, strong ground motion records with peak ground acceleration >100 cm/s2 were selected to identify the soil nonlinearity by the moving time window deconvolution method, and the temporal variation of time-average shear wave velocity and κ at the 50 surface stations were assessed.

On a nonparametric estimation of ℙ(X

Let θ = P ( X < Y < Z ) , where X ∼ N ( μ X , σ 2 ) , Z ∼ N ( μ Z , σ 2 ) and Y are independent random variables, with known constants μ X , μ Z ∈ R , σ > 0 . Suppose we observe a random sample Y 1 ′ , … , Y n ′ from the distribution of Y ′ = Y + ε . Here ε is a random error and distributed with a known continuous distribution. Our aim is to estimate θ based on the sample as well as on the complete knowledge about the distributions of X, Z and ε . We propose a nonparametric estimator of θ by applying the Fourier deconvolution method. Our estimator is shown to be mean consistent with respect to the mean squared error and strongly consistent. Under some regularity assumptions on the densities of Y and ε , some error estimates of the proposed estimator are derived. A simulation study is also conducted to illustrate the effectiveness of our method.

SpatialDeX is a Reference-Free Method for Cell Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors.

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multi-cell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell type-specific spatial organization. To this end, we developed SpatialDeX, a regression model-based method for estimating cell type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring scRNA-seq references.

Changes in diffusion MRI and clinical motor function after physical/occupational therapies in toddler-aged children with spastic unilateral cerebral palsy.

Diffusion-weighted magnetic resonance imaging (DMRI) is a potential tool to assess changes in brain connectivity and microstructure resulting from physical and occupational therapy in young children with cerebral palsy. This works was carried out to assess whether DMRI can detect changes after 36 weeks of physical and occupational therapy in the microstructure and connectivity of the brains of children with cerebral palsy and determine whether imaging findings correlate with changes in clinical measures of motor function. Five children underwent anatomical MRI and DMRI and evaluations of motor function skills at baseline and after 36 weeks of intensive or once-weekly physical and occupational Perception-Action Approach therapies. Diffusion tensor imaging and constrained spherical deconvolution methods were used to calculate fractional anisotropy (FA) and fiber orientation distribution functions (fODFs), respectively. The fODFs were used to generate tractograms of the cerebrospinal tract (CST). After 36 weeks of physical and occupational therapy, all children showed increases in motor function. No changes were observed in anatomical MRI before and after therapy but CST tractography did show small differences indicating possible altered microstructure and connectivity in the brain. FA values along the CSTs, however, showed no significant changes. Reliable longitudinal DMRI can be employed in toddler-aged children with CP and DMRI has the potential to monitor neuroplastic changes in white matter microstructure. However, there is a high variability between subjects and clinical improvements were not always correlated with measures of FA along the CST.

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations

Abstract An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and m-mode analysis. In this paper, we propose an alternative Bayesian method to infer the coefficients of a full-sky spherical harmonic basis for a drift-scan telescope with potentially thousands of baselines, that can precisely encode the uncertainties and correlations between the parameters used to build the recovered image. We use Gaussian Constrained Realisations (GCR) to efficiently draw samples of the spherical harmonic coefficients, despite the very large parameter space and extensive sky-regions of missing data. Each GCR solution provides a complete, statistically-consistent gap-free realisation of a full-sky map conditioned on the available data, even when the interferometer’s field of view is small. Many realisations can be generated and used for further analysis and robust propagation of statistical uncertainties. In this paper, we present the mathematical formalism of the spherical harmonic GCR-method for radio interferometers. We focus on the recovery of diffuse emission as a use case, along with validation of the method against simulations with a known diffuse emission component.

Rotating and stationary sound sources separation method based on MCB

Traditional beamforming methods do not effectively separate the mixed field of sound sources for different rotational speeds. The coexistence of these sources often manifests out-of-focus sources (for instance, stationary sources appearing with a rotational focus), thereby blurring the beamforming outcomes. Most existing algorithms for segregating rotational different speed sources employ a virtual rotating array utilizing ring arrays. However, due to the constant microphone spacing, the acoustic imaging of the ring array suffers from the interference of high-level side lobes. In this work, a hybrid deconvolution method based on modal composition beamforming is used to explore the ability of a multi-arm spiral array to separate different speed sources. Considering the even energy distribution from the out-of-focus source along the circular trajectory, the beamforming result of out-of-focus sources exhibits a weaker amplitude than the source amplitude. Therefore, out-of-focus sources can be considered noise. First, the point spread function is derived based on the modal transfer function. Then, a system of linear equations is constructed based on an incomplete cross-point spread function matrix. Finally, the source separation is solved through sparse-constrained deconvolution. Simulation and experimental results demonstrate that this method can accurately locate and separate different rotational speed sound sources.

Accelerating the clean-SC and CMF beamforming deconvolution methods using neural gridcompression

Deconvolution algorithms can considerably improve the spatial resolution of sound sources when applied to beamforming techniques. However, they are often associated with additional computation time, which can be prohibitive for real-time applications, especially when using high-resolution scanning grids. Additionally, depending on the size of the problem, full deconvolution may not even be possible due to memory constraints. Recently, it has been shown that a neural network approach to compress the possible source directions can speed up the results of the DAMAS deconvolution algorithm by orders of magnitude. In this paper, this neural grid compression preprocessing is evaluated for the CLEAN-SC and CMF deconvolution techniques, and it is shown that it is possible to achieve dramatic speedups without significant loss of accuracy and even improve the results in some examples, especially for the CLEAN-SC technique.

Evaluation of the reactivity of co-combustion of wheat straw and waste rubber thermolysis char

The co-combustion of wheat straw (WS) and waste rubber thermolysis char (WRTC) was examined with the perspective of utilizing WRTC as a high-calorific value fuel addition to agricultural biomass. The study investigated the effect of different proportions of WS-WRTC blends (10/15/25/49 wt% WRTC) and a maximum of 4 wt% binders, including potato starch and calcium oxide, on the reactivity of co-combustion and the distribution of products during the process. Thermogravimetric analysis with Fourier Transform Infrared Spectroscopy (TGA-FTIR) analyses was employed at a temperature range of 25–1000 ° C with a heating rate of 10 ° C/min. The kinetics of co-combustion were analyzed using the Fraser-Suzuki (FS) deconvolution method and a model-based kinetic modeling. The results indicate that a 10% addition of WRTC char reduces the intensity and rate of combustion and prolongs the combustion time. It was observed that the optimal addition to the blend is 25% WRCT, and it resulting in a reduction in activation energy and combustion indexes. However, it remains with no significant impact on process stability and efficiency. Kinetics of the decomposition of the WRTC25 mixture modeled by the deconvolution method indicates a 5-step reaction course, which is a combination of the characteristic combustion stages of both fuels. The main denoted functional group observed in the FTIR absorption spectra were C=O, C-O, O-H, and C-H related to the release of CO2, CO, H2O, CH4, aromatic compounds, and hydrocarbons.

Synthesis and Thermoluminescence Characterization of Ba₆Y₂W₃O₁₈ Perovskite Nanosensors for Dosimetry

Through the citrate-assisted sol-gel method, barium gadolinium tungstate (Ba6Y2W3O18) perovskite samples were synthesized, and their luminescence properties were investigated. Through X-ray diffraction (XRD) the crystal structure of the prepared samples was examined. The tunnelling electron microscopy (TEM) analysis has confirmed the agglomeration of the nanoparticles into rods with an average diameter and length of 71 and 214 nm, respectively. The thermoluminescence (TL) properties were investigated by; first, estimating the number of composing TL components using both experimental and computational methods; second, studying the dose-response of the glow curves in response to the irradiated beta dose. The third step in the study of the TL properties was to assess the reusability of the prepared samples in TL dosimetry and the fourth step was to estimate the minimum detectable dose of the phosphor samples. The Computerized Glow Curve Deconvolution (CGCD) method was then used to extract the kinetic parameters of the composing TL peaks.

Unveiling the Exquisite Microstructural Details in Zebrafish Brain Non-Invasively Using Magnetic Resonance Imaging at 28.2 T

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T1 and T2 relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy, at a very high spatial resolution. Visualisation of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction in false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organisation from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases.

Image deconvolution using hybrid threshold based on modified L1-clipped penalty in EM framework

Image deconvolution remains a challenging task due to its inherent ill-posedness. While existing algorithms show strong numerical performance, their complexity often complicates analysis and implementation. This paper introduces a computationally efficient image deconvolution method within the expectation maximization (EM) framework. The proposed algorithm alternates between an E-step leveraging the fast Fourier transform (FFT) and an M-step utilizing the discrete wavelet transform (DWT). In the M-step, we introduce a novel L1-clipped penalty to compute the maximum a posteriori (MAP) estimate, resulting in a hybrid threshold that combines the strengths of soft and hard thresholding. This hybrid threshold is mathematically derived, overcoming the high variance of hard-thresholding and the high bias of soft-thresholding, thus optimizing the trade-off between variance and bias. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art techniques in terms of improved signal-to-noise ratio (ISNR) and peak signal-to-noise ratio (PSNR), as well as visual quality. Notably, the proposed method shows average PSNR improvements of 3.49 dB, 4.23 dB, and 1.44 dB for uniform blur and 0.76 dB, 3.57 dB, and 0.66 dB for Gaussian blur on the Set12, BSD68, and Set14 datasets, respectively.

Deep learning for enhanced spectral analysis of MA-XRF datasets of paintings.

Recent advancements of noninvasive imaging techniques applied for the study and conservation of paintings have driven a rapid development of cutting-edge computational methods. Macro x-ray fluorescence (MA-XRF), a well-established tool in this domain, generates complex and voluminous datasets that pose analytical challenges. To address this, we have incorporated machine learning strategies specifically designed for the analysis as they allow for identification of nontrivial dependencies and classification within these high-dimensional data, thereby promising comprehensive interrogation. We introduce a deep learning algorithm trained on a synthetic dataset that allows for fast and accurate analysis of the XRF spectra in MA-XRF datasets. This approach successfully overcomes the limitations commonly associated with traditional deconvolution methods. Applying this methodology to a painting by Raphael, we demonstrate that our model not only achieves superior accuracy in quantifying the fluorescence line intensities but also effectively eliminates the artifacts typically observed in elemental maps generated through conventional analysis methods.

Novel theoretical studies based on CIELab and deconvolution methods to characterize AuNPs aggregation processes

The study of the interaction of spherical gold nanoparticles when they undergo aggregation phenomena due to the presence of a ligand in solution is presented, based on both colorimetric parameters (using CIELab space) and traditional deconvolution analysis. The feasibility of using CIELab space for this purpose can be an important method to simplify the required instrumentation (colorimeter versus UV spectrophotometer) as well as being less time consuming compared with the traditional computational analysis. For these reasons, in this work we present the first experimental approach of the proposed methodology for its application in this field of research. The synthetized AuNPs employed in this work have been previously characterized by means of the maxima of their LSPR bands, zeta potential and electron microscopy images. The aggregation of spherical AuNPs (with a diameter of 12 nm) by an inert electrolyte (NaCl) is investigated in a first step with successfully results, allowing to obtain the characteristic CIELab parameters (a*, b* and L*). At a later stage, the color changes induced in the optical properties of the solution due to the interaction of AuNPs (with increasing diameters of 12, 25 and 33 nm) with an amino acid such as lysine at neutral pH, have been also described using the CIELab spatial framework. The obtained a*, b* and L* parameters of the CIELab space allow to characterize satisfactorily the color change from red to blue which describes the aggregation phenomena, relying on deconvolution analysis of the LSPR bands. The results obtained support the validity of the CIELab space as a valid tool for the investigation of the aggregation process of colloidal solutions. It will allow simplifying the whole process using simpler and cheaper instrumentation as well as a decrease in the required analysis time.