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The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.

Raman microscopy is a vibrational imaging technology that can detect molecular chemical bond vibrational signals. Since this signal is originated from almost every vibrational mode of molecules with different vibrational energy levels, it provides spatiotemporal distribution of various molecules in living organisms without the need for any labeling. The limitations of low signal strength in Raman microscopy have been effectively addressed by incorporating a stimulated emission process, leading to the development of stimulated Raman scattering (SRS) microscopy. Furthermore, the issue of low spatial resolution has been resolved through the application of computational techniques, specifically image deconvolution. In this article, we present a comprehensive guide to super-resolution SRS microscopy using an Adam-based pointillism deconvolution (A-PoD) algorithm, complemented by a user-friendly graphical user interface (GUI). We delve into the crucial parameters and conditions necessary for achieving super-resolved images through SRS imaging. Additionally, we provide a step-by-step walkthrough of the preprocessing steps and the use of GUI-supported A-PoD. This complete package offers a user-friendly platform for super-resolution SRS microscopy, enhancing the versatility and applicability of this advanced microscopy technique to reveal nanoscopic multimolecular nature. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Super-resolution stimulated Raman scattering microscopy with graphical user interface-supported A-PoD Support Protocol: Deuterium labeling on cells with heavy water for metabolic imaging.

We analyze nonparametric estimators for the distribution function of the incubation time in the singly and doubly interval censoring model. The classical approach is to use parametric families like Weibull, log‐normal or gamma distributions in the estimation procedure. We propose nonparametric estimates for functions of the observations, which stay closer to the data than the classical parametric methods. We also give explicit limit distributions for discrete versions of the models and apply this to compute confidence intervals. The methods complement the analysis of the continuous model in Groeneboom (2021, 2023). R scripts for computation of the estimates are provided in Groeneboom (2020). [ABSTRACT FROM AUTHOR]

Background: Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD‐based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. Purpose: Building on the identified limitations in QA, the study aims to adapt SVD‐based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two‐dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics. Materials and methods: The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time‐density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block‐circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient‐specific internal carotid artery phantom. The DSA showcases factors like x‐ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data. Results: The different SVD algorithm variants showed strong correlations between flow and deconvolution‐adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. Conclusion: Implementing SVD‐based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters. [ABSTRACT FROM AUTHOR]

Let X, Y be continuous random variables with unknown distributions. The aim of this paper is to study the problem of estimating the probability θ : = P (X < Y) based on independent random samples from the distributions of X ′ , Y ′ , ζ and η , where X ′ = X + ζ , Y ′ = Y + η and X, Y, ζ , η are mutually independent random variables. In this context, ζ , η are referred to as measurement errors. We apply the ridge-parameter regularization method to derive a nonparametric estimator for θ depending on two parameters. Our estimator is shown to be consistent with respect to mean squared error if the characteristic functions of ζ , η only vanish on Lebesgue measure zero sets. Under some further assumptions on the densities of X, Y, ζ and η , we obtain some upper and lower bounds on the convergence rate of the estimator. A numerical example is also given to illustrate the efficiency of our method. [ABSTRACT FROM AUTHOR]

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma. [ABSTRACT FROM AUTHOR]

Purpose: We present a probabilistic approach to medical image analysis that requires, and makes use of, explicit prior information provided by a medical expert. Depending on the choice of prior model the method can be used for image enhancement, analysis, and segmentation. Methods: The methodology is based on a probabilistic approach to medical image analysis, that allows integration of 1) arbitrarily complex prior information (for which realizations can be generated), 2) information about a convolution operator of the imaging system, and 3) information about the noise in the reconstructed image into a posterior probability density. The method was demonstrated on positron emission tomography (PET) images obtained from a phantom and a patient with lung cancer. The likelihood model (multivariate log-normal) and the convolution operator were derived from phantom data. Two examples of prior information were used to show the potential of the method. The extended Metropolis-Hastings algorithm, a Markov chain Monte Carlo method, was used to generate realizations of the posterior distribution of the tracer activity concentration. Results: A set of realizations from the posterior was used as the base of a quantitative PET image analysis. The mean and variance of activity concentrations were computed, as well as the probability of high tracer uptake and statistics on the size and activity concentration of high uptake regions. For both phantom and in vivo images, the estimated images of mean activity concentrations appeared to have reduced noise levels, and a sharper outline of high activity regions, as compared to the original PET. The estimated variance of activity concentrations was high at the edges of high activity regions. Conclusions: The methodology provides a probabilistic approach for medical image analysis that explicitly takes into account medical expert knowledge as prior information. The presented first results indicate the potential of the method to improve the detection of small lesions. The methodology allows for a probabilistic measure of the size and activity level of high uptake regions, with possible long-term perspectives for early detection of cancer, aswell as treatment, planning, and follow-up. [ABSTRACT FROM AUTHOR]

AbstractLet θ=P(XY are independent random variables, with known constants μX,μZ∈R, σ>0. Suppose we observe a random sample Y1′,…,Yn′ 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. [ABSTRACT FROM AUTHOR]

In survival analysis, it is possible to have some data which are both positively dependent and corrupted by additive noise. This occurs due to the presence of measurement errors. The main focus of this paper is the nonparametric estimation of the hazard rate function in terms of the deconvolution kernel density estimator f n (x) and the distribution function estimator obtained by integrating f n (x). We aim at establishing a strong uniform consistency with rates. The performance quality of the hazard function estimator is evaluated numerically. Finally, the approach is implemented to estimate the length of pregnancy from a real data set. [ABSTRACT FROM AUTHOR]

Measurement of laboratory atomic pair distribution function data has improved with contemporary X‐ray sources, optics and detectors, with acquisition times of the order of minutes for ideal samples. This paper examines resolution effects in pair distribution function data obtained using a convergent‐beam configuration and an Ag X‐ray tube from standard silicon powder and from 10 nm BaTiO3 nanocubes. The elliptical multilayer X‐ray mirror reflects a non‐trivial X‐ray spectrum and introduces resolution effects not commonly treated in ordinary parafocusing divergent‐beam laboratory diffraction. These resolution effects are modeled using the fundamental parameters approach, and the influence this has on interpretation and modeling of the resulting reduced atomic pair distribution function data is demonstrated. [ABSTRACT FROM AUTHOR]

This paper focuses on nonparametrically estimating the probability $ \mathbb {P} \left ( X_t \lt Y_t \right ) $ P(Xt

This paper considers the quantitative comparison of several alternative models to perform deconvolution in situations where there is no ground truth data available. With applications to very large data sets in mind, we focus on linear deconvolution models based on a Wiener filter. Although comparatively simple, such models are widely prevalent in large scale setting such as high-resolution image restoration because they provide an excellent trade-off between accuracy and computational effort. However, in order to deliver accurate solutions, the models need to be properly calibrated in order to capture the covariance structure of the unknown quantity of interest and of the measurement error. This calibration often requires onerous controlled experiments and extensive expert supervision, as well as regular recalibration procedures. This paper adopts an unsupervised Bayesian statistical approach to model assessment that allows comparing alternative models by using only the observed data, without the need for ground truth data or controlled experiments. Accordingly, the models are quantitatively compared based on their posterior probabilities given the data, which are derived from the marginal likelihoods or evidences of the models. The computation of these evidences is highly non-trivial and this paper consider three different strategies to address this difficulty—a Chib approach, Laplace approximations, and a truncated harmonic expectation—all of which efficiently implemented by using a Gibbs sampling algorithm specialised for this class of models. In addition to enabling unsupervised model selection, the output of the Gibbs sampler can also be used to automatically estimate unknown model parameters such as the variance of the measurement error and the power of the unknown quantity of interest. The proposed strategies are demonstrated on a range of image deconvolution problems, where they are used to compare different modelling choices for the instrument's point spread function and covariance matrices for the unknown image and for the measurement error. [ABSTRACT FROM AUTHOR]

Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data. [ABSTRACT FROM AUTHOR]

This article deals with the problem of estimating the service time distribution of the M t / G / ∞ queue from observation of the departure epochs. We develop minimax optimal estimators of G and study behavior of the minimax pointwise risk over a suitable family of service time distribution functions. In addition, we address the problem of adaptive estimation and propose a data–driven estimation procedure that adapts to unknown smoothness of the service time distribution function G. Lastly, a numerical study is presented to illustrate practical performance of the developed adaptive procedure. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The molecular basis of cognitive resilience (CR) among pathologically confirmed Alzheimer's disease (AD) cases is not well understood. METHODS: Abundance of 13 cell types and neuronal subtypes in brain bulk RNA‐seq data from the anterior caudate, dorsolateral prefrontal cortex (DLPFC), and posterior cingulate cortex (PCC) obtained from 434 AD cases, 318 cognitively resilient AD cases, and 188 controls in the Religious Orders Study and Rush Memory and Aging Project was estimated by deconvolution. RESULTS: PVALB+ neuron abundance was negatively associated with cognitive status and tau pathology in the DLPFC and PCC (Padj < 0.001) and the most reduced neuronal subtype in AD cases compared to controls in DLPFC (Padj = 8.4 × 10−7) and PCC (Padj = 0.0015). We identified genome‐wide significant association of neuron abundance with TMEM106B single nucleotide polymorphism rs13237518 in PCC (p = 6.08 × 10−12). rs13237518 was also associated with amyloid beta (p = 0.0085) and tangles (p = 0.0073). DISCUSSION: High abundance of PVALB+ neurons may be a marker of CR. TMEM106B variants may influence CR independent of AD pathology. Highlights: Neuron retention and a lack of astrocytosis are highly predictive of Alzheimer's disease (AD) resilience.PVALB+ GABAergic and RORB+ glutamatergic neurons are associated with cognitive status.A TMEM106B single nucleotide polymorphism is related to lower AD risk, higher neuron count, and increased AD pathology. [ABSTRACT FROM AUTHOR]

The current work was going to study the dosimetric properties of Egyptian muscovite minerals irradiated with β‐rays using the thermoluminescence technique (TL). The analysis of the TL glow curves was done using the (Tm − Tstop) and the initial rise methods. The deconvolution of the muscovite glow curves was analyzed to have eight superimposed trapping peaks. These trapes were located at 0.72, 0.80, 0.98, 1.09, 1.19, 1.30, 1.50, and 1.59 eV. Muscovite samples were studied at doses up to 330 Gy and exhibited good linearity in relation to beta radiation. The kinetic parameters, sensitivity, and dosimetric characteristics were evaluated. High accuracy was achieved through the repeatability of TL measurements. [ABSTRACT FROM AUTHOR]

Two-dimensional transition metal dichalcogenides, particularly MoS2, are interesting materials for many applications in aerospace research, radiation therapy and bioscience more in general. Since in many of these applications MoS2-based nanomaterials can be placed in an aqueous environment while exposed to ionizing radiation, both experimental and theoretical studies of their behaviour under these conditions is particularly interesting. Here, we study the effects of tiny imparted doses of 511 keV photons to MoS2 nanoflakes in water solution. To the best of our knowledge, this is the first study in which ionizing radiation on 2D-MoS2 occurs in water. Interestingly, we find that, in addition to the direct interaction between high-energy photons and nanoflakes, reactive chemical species, generated by γ-photons induced radiolysis of water, come into play a relevant role. A radiation transport Monte Carlo simulation allowed determining the elements driving the morphological and spectroscopical changes of 2D-MoS2, experimentally monitored by SEM microscopy, DLS, Raman and UV–vis spectroscopy, AFM, and X-ray photoelectron techniques. Our study demonstrates that radiolysis products affect the Molybdenum oxidation state, which is massively changed from the stable + 4 and + 6 states into the rarer and more unstable + 5. These findings will be relevant for radiation-based therapies and diagnostics in patients that are assuming drugs or contrast agents containing 2D-MoS2 and for aerospace biomedical applications of 2DMs investigating their actions into living organisms on space station or satellites. [ABSTRACT FROM AUTHOR]

Background: The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential. Results: We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies. Conclusions: DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor inversion ability of multiple magnetic field sources inside small-scale objects, a multi-magnetic source inversion technology for small-scale objects was proposed. The atomic magnetometer was used to collect the magnetic source signal, the distribution of the magnetic source was inverted based on the theory of weak field, the distribution source model and Gauss-Seidel iterative algorithm were introduced to optimize the solution process, and the forward and inverse model was verified. At the same time, the point source model deconvolution operation was used to improve the spatial resolution of the magnetic source, and the spatial resolution of the system is about 10 mm before deconvolution and less than 4 mm after deconvolution. The results show that the spatial position distribution of the magnetic source inversion is roughly close to the real one, which verifies the correctness of the inversion technology and can be used for the identification of magnetic sources in precision instruments. This research has opened up a new way for the detection of multiple magnetic sources inside some precision instruments. [ABSTRACT FROM AUTHOR]

In this study, we introduce Robust estimation of Cell type proportions by Integrating single-reference-based DEconvolutions (ReCIDE), an innovative framework for robust estimation of cell type proportions by integrating single-reference-based deconvolutions. ReCIDE outperforms existing approaches in benchmark and real datasets, particularly excelling in estimating rare cell type proportions. Through exploratory analysis on public bulk data of triple-negative breast cancer (TNBC) patients using ReCIDE, we demonstrate a significant correlation between the prognosis of TNBC patients and the proportions of both T cell and perivascular-like cell subtypes. Built upon this discovery, we develop a prognostic assessment model for TNBC patients. Our contribution presents a novel framework for enhancing deconvolution accuracy, showcasing its effectiveness in medical research. [ABSTRACT FROM AUTHOR]

Purpose: To propose the simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods. Methods: The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC‐MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data. Results: The proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods. Conclusion: These results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground‐truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning. [ABSTRACT FROM AUTHOR]

When a wheel passes over a rail surface irregularity, the resulting vehicle excitations lead to the application of additional system forces to both the track and the vehicle. These forces contribute to an accelerated track geometry deterioration, which in turn results in increased costs. In a recent paper, a clear correlation between the presence of rail irregularities and poor track geometry quality was demonstrated. Rail surface irregularities thereby were quantified by raw data of a chord-based optical measurement system mounted on the regular track recording vehicle in Austria. This paper deals with deconvolution of the recorded data in order to guarantee irregularity quantification without any distortion. Two different deconvolution approaches are developed and validated by additional measurements. Using the deconvoluted data, previously published evaluations were repeated, and the impact of using deconvoluted data instead of chord values was analysed. The correlation between short-wave effects and track geometry quality can not only be confirmed; it is even stronger than predicted by the chord data. The results of the analysis demonstrate that irregularities with amplitudes exceeding 0.08 mm contribute to an accelerated deterioration in track geometry. Amplitudes of a greater severity result in track geometry levels that are up to 120% inferior to the average. [ABSTRACT FROM AUTHOR]

Abstract A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

Abstract Background The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential. Results We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies. Conclusions DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.

Abstract Two-dimensional transition metal dichalcogenides, particularly MoS2, are interesting materials for many applications in aerospace research, radiation therapy and bioscience more in general. Since in many of these applications MoS2-based nanomaterials can be placed in an aqueous environment while exposed to ionizing radiation, both experimental and theoretical studies of their behaviour under these conditions is particularly interesting. Here, we study the effects of tiny imparted doses of 511 keV photons to MoS2 nanoflakes in water solution. To the best of our knowledge, this is the first study in which ionizing radiation on 2D-MoS2 occurs in water. Interestingly, we find that, in addition to the direct interaction between high-energy photons and nanoflakes, reactive chemical species, generated by γ-photons induced radiolysis of water, come into play a relevant role. A radiation transport Monte Carlo simulation allowed determining the elements driving the morphological and spectroscopical changes of 2D-MoS2, experimentally monitored by SEM microscopy, DLS, Raman and UV–vis spectroscopy, AFM, and X-ray photoelectron techniques. Our study demonstrates that radiolysis products affect the Molybdenum oxidation state, which is massively changed from the stable + 4 and + 6 states into the rarer and more unstable + 5. These findings will be relevant for radiation-based therapies and diagnostics in patients that are assuming drugs or contrast agents containing 2D-MoS2 and for aerospace biomedical applications of 2DMs investigating their actions into living organisms on space station or satellites.

This article is devoted to the non parametric deconvolution problem of the cumulative distribution function from repeated observations with unknown noise distribution. The noise distribution is assumed to be symmetric around zero and can be consistently estimated from observed data without any additional data from that distribution. We suggest an estimator of the target function depending on a smoothing parameter and then study some asymptotic properties of the proposed estimator with respect to the pointwise mean squared error by assuming some regularity conditions on the target distribution as well as on the noise distribution. We also illustrate the effectiveness of our estimation via a simulation study. [ABSTRACT FROM AUTHOR]

Muscle damage resulting from physical activities such as exercise triggers an immune response crucial for tissue repair and recovery. This study investigates the immune cell profiles in muscle biopsies of individuals engaged in resistance exercise (RE) and explores the impact of age and sex on the immune response following exercise-induced muscle damage. Microarray datasets from muscle biopsies of young and old subjects were analyzed, focusing on the gene expression patterns associated with immune cell activation. Genes were compared with immune cell signatures to reveal the cellular landscape during exercise. Results show that the most significant modulated gene after RE was Folliculin Interacting Protein 2 (FNIP2) a crucial regulator in cellular homeostasis. Moreover, the transcriptome was stratified based on the expression of FNIP2 and the 203 genes common to the groups obtained based on sex and age. Gene ontology analysis highlighted the FLCN-FNIP1-FNIP2 complex, which exerts as a negative feedback loop to Pi3k-Akt-mTORC1 pathway. Furthermore, we highlighted that the young females exhibit a distinct innate immune cell activation signature compared to males after a RE session. Specifically, young females demonstrate a notable overlap with dendritic cells (DCs), M1 macrophages, M2 macrophages, and neutrophils, while young males overlap with M1 macrophages, M2 macrophages, and motor neurons. Interestingly, in elderly subjects, both sexes display M1 macrophage activation signatures. Comparison of young and elderly signatures reveals an increased M1 macrophage percentage in young subjects. Additionally, common genes were identified in both sexes across different age groups, elucidating biological functions related to cell remodeling and immune activation. This study underscores the intricate interplay between sex, age, and the immune response in muscle tissue following RE, offering potential directions for future research. Nevertheless, there is a need for further studies to delve deeper and confirm the dynamics of immune cells in response to exercise-induced muscle damage. [ABSTRACT FROM AUTHOR]

Among existing methods for independence test, mutual information (MI) has great popularity as it is invariant to monotone transformations and enjoys higher power in detecting nonlinear associations. In this paper, we propose a novel MI-based independence test in the presence of measurement errors. The conditional density functions involved in MI are estimated using a novel deconvolution double kernel method. The convergence rates of these estimates are derived under the assumption that the measurement errors are either ordinary or super smooth. In addition, the asymptotic behaviors of the resultant estimate of MI are established under both the null and alternative hypotheses. Extensive simulation studies and an application to the low-resolution observations of source stars dataset confirm the superior numerical performances of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract Atomic Force Microscopy (AFM) has intrinsic tip-sample convolution artifacts. Commercially available tip-check samples are used to obtain only the tip radius, which can be used to deconvolute surface profiles or to quantify tip wear by relying on AFM alone. When the sample height is of the order of 100 nm or more, not only the tip radius but also the overall tip shape plays a key role in imaging. Therefore, it is necessary to know the overall tip shape, which requires a structured sample that is much larger than tip-check samples. Here, we propose to use deep reactive ion-etched holes of 1 µ diameter and 5 µ height to reconstruct the overall tip shape of three different AFM probes, namely conical, pyramidal and tetrahedral. The proposed cylindrical hole structure seems promising, as simple inversion of AFM images can provide sufficient collective features to be used for deconvolution and image enhancement.

Abstract Background Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children (n = 8) before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modeling with Cibersortx or IDOL, respectively. Results Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60–0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.

Abstract We report the numerical and experimental studies of the two-dimensional Brillouin gain spectrum (BGS) distribution deformation induced by the self-phase modulation in the Brillouin optical time domain reflectometry (BOTDR) with a 20.6 km sensing distance. The BGS distribution deformation is investigated by analyzing the evolution of the point spread function along the fiber in the two-dimensional model of the BOTDR. In the simulation and experimental results, the specific deformation degree of the BGS distribution induced by the self-phase modulation is related to the pump pulse profile, pump pulse peak power, BGS demodulation method, and detected scattered light component. By comprehensively analyzing the evolution of the point spread function induced by the self-phase modulation and using the image deconvolution, a typical BOTDR sensor with a 25 ns pump pulse reaches the 20 cm spatial resolution over the 20.6 km sensing fiber.

Abstract Background Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. Results In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. Conclusions Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

Abstract Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and of tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.

Abstract Background Single-cell RNA sequencing (sc-RNASeq) data illuminate transcriptomic heterogeneity but also possess a high level of noise, abundant missing entries and sometimes inadequate or no cell type annotations at all. Bulk-level gene expression data lack direct information of cell population composition but are more robust and complete and often better annotated. We propose a modeling framework to integrate bulk-level and single-cell RNASeq data to address the deficiencies and leverage the mutual strengths of each type of data and enable a more comprehensive inference of their transcriptomic heterogeneity. Contrary to the standard approaches of factorizing the bulk-level data with one algorithm and (for some methods) treating single-cell RNASeq data as references to decompose bulk-level data, we employed multiple deconvolution algorithms to factorize the bulk-level data, constructed the probabilistic graphical models of cell-level gene expressions from the decomposition outcomes, and compared the log-likelihood scores of these models in single-cell data. We term this framework backward deconvolution as inference operates from coarse-grained bulk-level data to fine-grained single-cell data. As the abundant missing entries in sc-RNASeq data have a significant effect on log-likelihood scores, we also developed a criterion for inclusion or exclusion of zero entries in log-likelihood score computation. Results We selected nine deconvolution algorithms and validated backward deconvolution in five datasets. In the in-silico mixtures of mouse sc-RNASeq data, the log-likelihood scores of the deconvolution algorithms were strongly anticorrelated with their errors of mixture coefficients and cell type specific gene expression signatures. In the true bulk-level mouse data, the sample mixture coefficients were unknown but the log-likelihood scores were strongly correlated with accuracy rates of inferred cell types. In the data of autism spectrum disorder (ASD) and normal controls, we found that ASD brains possessed higher fractions of astrocytes and lower fractions of NRGN-expressing neurons than normal controls. In datasets of breast cancer and low-grade gliomas (LGG), we compared the log-likelihood scores of three simple hypotheses about the gene expression patterns of the cell types underlying the tumor subtypes. The model that tumors of each subtype were dominated by one cell type persistently outperformed an alternative model that each cell type had elevated expression in one gene group and tumors were mixtures of those cell types. Superiority of the former model is also supported by comparing the real breast cancer sc-RNASeq clusters with those generated by simulated sc-RNASeq data. Conclusions The results indicate that backward deconvolution serves as a sensible model selection tool for deconvolution algorithms and facilitates discerning hypotheses about cell type compositions underlying heterogeneous specimens such as tumors.

Atomic Force Microscopy (AFM) has intrinsic tip-sample convolution artifacts. Commercially available tip-check samples are used to obtain only the tip radius, which can be used to deconvolute surface profiles or to quantify tip wear by relying on AFM alone. When the sample height is of the order of 100 nm or more, not only the tip radius but also the overall tip shape plays a key role in imaging. Therefore, it is necessary to know the overall tip shape, which requires a structured sample that is much larger than tip-check samples. Here, we propose to use deep reactive ion-etched holes of 1 µ diameter and 5 µ height to reconstruct the overall tip shape of three different AFM probes, namely conical, pyramidal and tetrahedral. The proposed cylindrical hole structure seems promising, as simple inversion of AFM images can provide sufficient collective features to be used for deconvolution and image enhancement. [ABSTRACT FROM AUTHOR]

In this study, we investigate the usefulness of the spectral whitening procedure, devised by one of the authors as a preprocessing stage of envelope-detected single-look synthetic aperture radar (SAR) images, in application contexts where phase information is relevant. In the first experiment, each of the raw datasets of an interferometric pair of COSMO-SkyMed images, representing industrial buildings amidst vegetated areas, was individually (1) synthesized by the SAR processor without Fourier-domain Hamming windowing; (2) synthesized with Hamming windowing, used to improve the focalization of targets, with the drawback of spatially correlating speckle; and (3) processed for the whitening of complex speckle, using the data obtained in (2). The interferograms were produced in the three cases, and interferometric coherence and phase maps were calculated through 3 × 3 boxcar filtering. In (1), coherence is low on vegetation; the presence of high sidelobes in the system's point-spread function (PSF) causes the spread of areas featuring high backscattering. In (2), point targets and buildings are better defined, thanks to the sidelobe suppression achieved by the frequency windowing, but the background coherence is abnormally increased because of the spatial correlation introduced by the Hamming window. Case (3) is the most favorable because the whitening operation results in low coherence in vegetation and high coherence in buildings, where the effects of windowing are preserved. An analysis of the phase map reveals that (3) is likely to be facilitated also in terms of unwrapping. Results are presented on a TerraSAR-X/TanDEM-X (TSX-TDX) image pair by processing the interferograms of original and whitened data using a non-local filter. The main results are as follows: (1) with autocorrelated speckle, the estimation error of coherence may attain 16% and inversely depends on the heterogeneity of the scene; and (2) the cleanness and accuracy of the phase are increased by the preliminary whitening stage, as witnessed by the number of residues, reduced by 24%. Benefits are also expected not only for differential InSAR (DInSAR) but also for any coherent analysis and processing carried out performed on SLC data. [ABSTRACT FROM AUTHOR]

Fluorescence microscopic imaging is essentially a convolution process distorted by random noise, limiting critical parameters such as imaging speed, duration, and resolution. Though algorithmic compensation has shown great potential to enhance these pivotal aspects, its fidelity remains questioned. Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity. Our approach employs a multi-resolution analysis (MRA) framework to extract the two main characteristics of fluorescence images against noise: across-edge contrast, and along-edge continuity. By constraining the two features in a model-solution framework using framelet and curvelet, we develop MRA deconvolution algorithms, which improve the signal-to-noise ratio (SNR) up to 10 dB higher than spatial derivative based penalties, and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference. We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity, enabling accomplishments of more challenging imaging tasks. [ABSTRACT FROM AUTHOR]

In many areas, such as speech processing, communication, and measurement systems, where the actual signal is not directly available, Wiener deconvolution is a widely used signal restoration technique. The accuracy of the estimated signal in Wiener deconvolution is determined by the unknown filter parameter. To compute this unknown parameter, complex and time-consuming numerical as well as iterative computation approaches were proposed in the literature, which also often compute an unoptimized unknown filter parameter. To address the computational problem and estimate a more accurate unknown filter parameter, the authors reported an analytical solution in a prior work for a given signal to noise ratio. However, it was revealed that the actual optimum parameter value also varies with noise also. In this paper, the authors propose a new analytical solution for optimum parameter estimation that includes noise as well. The authors compared the modified analytical solution to existing numerical computation methods for different values of Signal to Noise Ratio and demonstrated that the proposed methods' optimum parameter estimation is always close to the actual optimum value, which resulted in more accurate signal deconvolution. Proposed mathematical solutions are also verified with experimental signals, acquired in a laboratory. [ABSTRACT FROM AUTHOR]

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering, this study proposes a robust production data analysis method based on deconvolution, which is used for multi-well inter-well interference research. In this study, a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established, and its Laplace solutions under two different outer boundary conditions were obtained. Then, an improved pressure deconvolution algorithm was used to normalize the scattered production data. Furthermore, the typical curve fitting was carried out using the production data and the seepage model solution. Finally, some reservoir parameters and fracturing parameters were interpreted, and the intensity of inter-well interference was compared. The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area. The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging. Besides, by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions, the strength of the interference between wells on the same well platform was well judged. This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells. [ABSTRACT FROM AUTHOR]

Background: Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized. Methods: We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database. Results: We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor− subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups. Conclusion: Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients. [ABSTRACT FROM AUTHOR]

Reflectivity inversion plays a pivotal role in reservoir prediction. Conventional sparse-spike deconvolution assumes that the reflectivity (reflection coefficient) is sparse, which is solved based on the l 1 norm. However, the restricted isometry property (RIP) of wavelet matrix and seismic effective bandwidth limits the accuracy of the sparse-spike reflectivity inversion. Consequently, we investigate the connection between seismic amplitude shape and reflectivity. When the reflectivity contains more non-zero values, the wavelet bandwidth within the effective seismic data bandwidth approaches a limit corresponding to the Sinc wavelet, where the main-lobe amplitude closely approximates the reflectivity. Conversely, when the reflectivity has fewer non-zero values, a wavelet with a smaller sidelobe provides a more accurate approximation of the reflectivity. In this paper, we propose a high-resolution inversion optimization method based on joint l 2 norm and l 1 norm constraints. By parameter tuning, we construct the Sinc wavelet or the wavelet with a weak-sidelobe corresponding to the seismic spectrum. Subsequently, we determine the extremum to approximate the reflectivity. To mitigate the RIP condition's constraints, we employ the l 2 norm to balance the l 1 norm (joint constraint) by introducing l 2 norm with low-pass filtering characteristics. This approach yields more accurate reflectivity estimates. By taking the extremum, this approach yields more accurate reflectivity estimates. The synthetic test demonstrates that our method achieves better reflectivity inversion accuracy compared to sparse-spike inversion with l 1– l 2 norm constraint. Furthermore, field tests indicate that the proposed reflectivity inversion method not only can better match the well curve, but also exhibits excellent resolution. [ABSTRACT FROM AUTHOR]

Optical‐resolution photoacoustic microscopy (OR‐PAM) has been increasingly utilized for in vivo imaging of biological tissues, offering structural, functional, and molecular information. In OR‐PAM, it is often necessary to make a trade‐off between imaging depth, lateral resolution, field of view, and imaging speed. To improve the lateral resolution without sacrificing other performance metrics, we developed a virtual‐point‐based deconvolution algorithm for OR‐PAM (VP‐PAM). VP‐PAM has achieved a resolution improvement ranging from 43% to 62.5% on a single‐line target. In addition, it has outperformed Richardson‐Lucy deconvolution with 15 iterations in both structural similarity index and peak signal‐to‐noise ratio on an OR‐PAM image of mouse brain vasculature. When applied to an in vivo glass frog image obtained by a deep‐penetrating OR‐PAM system with compromised lateral resolution, VP‐PAM yielded enhanced resolution and contrast with better‐resolved microvessels. [ABSTRACT FROM AUTHOR]

Background: Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs). Methods: Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children (n = 8) before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modeling with Cibersortx or IDOL, respectively. Results: Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60–0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils. Conclusion: Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types. [ABSTRACT FROM AUTHOR]

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec. [ABSTRACT FROM AUTHOR]

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS). [ABSTRACT FROM AUTHOR]

The slightly altered samples of dioxan lignin were extracted from coniferous (spruce and juniper) and deciduous wood (aspen) by the Pepper's method. Lignins were characterized by data of elemental, functional composition, macromolecular and reactive (acid–base and redox) properties. IR spectra of lignins were recorded by the attenuated total refection method. The absorption band of lignins in the 3000–3700 cm−1 region corresponds to the valence vibrations of hydroxyl groups involved in hydrogen bonds. This band was divided into separate Gaussian contours by deconvolution method. The characteristic parameters (energy, length, and relative content) were calculated on the basis of correlation dependencies for each type of H-bonds. The relationship between reactivity (acid–base and redox properties) and the involvement of hydroxyl groups of a natural polymer in H-bonds was shown. The highest reactivity of aspen lignins is caused by the presence of syringyl structures and lower H-bond energies involving reaction centers of macromolecules. [ABSTRACT FROM AUTHOR]

Deconvolution algorithms have been proved to be effective tools for fault feature extraction and diagnosis of rotating machinery. As a non-iterative algorithm, multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) shows its advantage in computation efficiency. However, MOMEDA focuses only on the extraction of impulses with precise periodicity while the fault feature excited under time-varying speed may not meet this requirement. To solve this problem, this paper proposes an improved method named as MOMEDAang to extend the application scenarios of MOMEDA under time-varying speed condition. Instead of using a periodic target vector, the new method calculates the time-domain intervals of fault impulses according to their nature of isometric distribution in angle domain, which can be free from the variation of speed. Moreover, the harmonic modulation intensity and average Gini index are further defined in this paper to highlight the inherent cyclic modulation order of filtered signal and conduct the health condition identification of monitoring equipment. The results of simulation and experimental cases reveal effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we explore a new idea of using deep learning representations as a principle for regularization in inverse problems for digital signal processing. Specifically, we consider the standard variational formulation, where a composite function encodes a fidelity term that quantifies the proximity of the candidate solution to the observations (under a physical process), and a second regularization term that constrains the space of solutions according to some prior knowledge. In this work, we investigate deep learning representations as a means of fulfilling the role of this second (regularization) term. Several numerical examples are presented for signal restoration under different degradation processes, showing successful recovery under the proposed methodology. Moreover, one of these examples uses real data on energy usage by households in London from 2012 to 2014. [ABSTRACT FROM AUTHOR]