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Cellulose crystallinity can be described according to the crystal size and the crystallinity index (CI). In this research, using Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) methods, we studied the crystallinity of three different types of cellulose: banana rachis (BR), commercial cellulose (CS), and bacterial cellulose (BC). For each type of cellulose, we analyzed three different crystallization grades. These variations were obtained using three milling conditions: 6.5 h, 10 min, and unmilled (films). We developed a code in MATLAB software to perform deconvolution of the XRD data to estimate CI and full width at half-maximum (FWHM). For deconvolution, crystalline peaks were represented with Voigt functions, and a Fourier series fitted to the amorphous profile was used as the amorphous contribution, which allowed the contribution of the amorphous profile to be more effectively modeled. Comparisons based on the FTIR spectra and XRD results showed there were no compositional differences between the amorphous samples. However, changes associated with crystallinity were observed when the milling time was 10 min. The obtained CI (%) values show agreement with values reported in the literature and confirm the effectiveness of the method used in this work in predicting the crystallization aspects of cellulose samples.

This paper addresses two fundamental issues in the peak deconvolution method of cellulose XRD data analysis: there is no standard model for amorphous cellulose and common peak functions such as Gauss, Lorentz and Voigt functions do not fit the amorphous profile well. It first examines the effects of ball milling on three types of cellulose and results show that ball milling transforms all samples into a highly amorphous phase exhibiting nearly identical powder X-ray diffraction (XRD) profiles. It is hypothesized that short range order within a glucose unit and between adjacent units survives ball milling and generates the characteristic amorphous XRD profiles. This agrees well with cellulose I d-spacing measurements and oligosaccharide XRD analysis. The amorphous XRD profile is modeled using a Fourier series equation where the coefficients are determined using the nonlinear least squares method. A new peak deconvolution method then is proposed to analyze cellulose XRD data with the amorphous Fourier model function in conjunction with standard Voigt functions representing the crystalline peaks. The impact of background subtraction method has also been assessed. Analysis of several cellulose samples was then performed and compared to the conventional peak deconvolution methods with common peak fitting functions and background subtraction approach. Results suggest that prior peak deconvolution methods overestimate cellulose crystallinity.Graphic abstract

We present a hybrid method for reflector design with finite light sources, combining a neural-network-based solver with a deconvolution-inspired iterative correction scheme. Our approach addresses the limitations of classical techniques, which often assume idealized point or parallel sources, by solving a simplified problem using a neural network and refining the solution via feedback from ray-traced simulations of the full finite-source system. We demonstrate the effectiveness of our method on a representative example, showing improved convergence toward a prescribed far-field intensity distribution compared to the approximate problem’s solution.

Blind image deconvolution is a challenging issue in image processing. In blind image deconvolution, the typical approach involves iteratively estimating both the blur kernel and latent image until convergence to the blur kernel of the observed image is achieved. Recently, several approaches have been attempted to develop a sophisticated regularization to obtain the clean image. However, existing methods often struggle to effectively handle ringing artifacts and local blur. To overcome these limitations, we introduce a fractional-order variational model. This model alleviates the ringing artifacts through the selection of an optimal derivative. Subsequently, to refine the latent image further, we leverage the local prior, namely patch-wise minimal pixels (PMP) prior. Since the PMP prior of clean images blocks is much sparser than that of blurred ones, it is capable of discriminating between clean and blurred image blocks. We illustrate the effective integration of the fractional-order operations and the PMP prior within our proposed approach. Moreover, the convergence of our algorithm has been proved as the values of the objective function monotonically decrease. Extensive experiments on different datasets demonstrate the superiority of the proposed method compared with other methods in terms of reconstruction quality for blind deconvolution.

Indonesia is located at the confluence of three plates, namely the Eurasian, Indo-Australian, and Pacific plates which causes the formation of an active fault line. One of these active faults is the Palu-Koro fault, Sulawesi, with a shift rate of 30-44 mm/year. A geophysical method such as gravity determines the fault’s characteristics. Gravity data is processed by a derivative analysis process in the form of First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD), which is carried out to represent the type of fault, and the Euler Deconvolution method is used to determine the location and depth of the fault anomaly source. The results show that the Palu-Koro fault has a high anomaly value in the west of 0.00557-0.01087 mGal in the north-south direction on the FHD map and an anomaly value of 0 in the north-south direction on the SVD map. The source of the anomaly can be seen more clearly in the results of the Euler Deconvolution modelling at a depth of 50-400 m from the topography of the study area. The Palu-Koro fault is proven to have many structures at shallow depths with very active movements that can trigger earthquakes.

Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets. This work presents a comprehensive benchmarking analysis of computational methods that integrates spatial and single-cell transcriptomics data for transcript distribution prediction and cell type deconvolution.

A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy. The resolution of fluorescence microscopy is increased by incorporating prior information into deconvolution algorithms.

Single-cell RNA-seq is a vital tool for deciphering patterns of gene expression within the multitude of cell lineages comprising any tissue. However, price and technical complexities limit the use of this technology. Deconvolution of bulk RNA-seq data from whole tissues offers an alternative approach to derive some of these insights and gain valuable contextual information from existing data.Cardiovascular diseases and their risk factors alter myocardial and skeletal muscle biology. We aimed to develop and validate approaches to deconvolute bulk RNAseq data from human right atrium (RA), left ventricle (LV) and skeletal muscle biopsies, allowing inference of the cellular composition of these samples.MethodsWe used single-cell RNA-seq data from the Heart Cell Atlas project to construct signature matrices encompassing all major cell lineages using the CIBERSORTx deconvolution package. Randomly selecting 200 cells from each cell lineage, we generated a signature matrix for each tissue, including fibroblasts, endothelial cells, lymphoid cells, myeloid cells, neuronal cells, pericytes, smooth muscle cells, and myocytes (defined as atrial cardiomyocytes and ventricular cardiomyocytes in the RA and LV matrices, respectively). Cardiac signature matrices also included adipocytes and neuronal cells, while the skeletal muscle signature matrix also incorporated satellite cells. These matrices can be applied in CIBERSORTx by any scientist wishing to infer cell lineage proportions in their bulk RNAseq data.To assess the accuracy of these signature matrices, we generated 100 synthetic RA, LV and skeletal muscle datasets with varying proportions of each cell lineage and compared their known composition with the composition predicted by CIBERSORTx. To illustrate their value in deconvolution, we applied our signature matrices to publicly available bulk RNA-seq data from the Genotype Tissue Expression (GTEx) project to explore the association of age and sex with cellular composition of these tissues.ResultsLinear regression analyses revealed excellent agreement between known and predicted composition of synthetic tissues, with R2>0.95 for all cell lineages, and good calibration. Deconvolution of skeletal muscle data from GTEx revealed, amongst other differences, a lower proportion of satellite cells and a higher proportion of fibroblasts with age, both of which have been demonstrated previously using alternative methods, corroborating the accuracy of our approach. Wide-ranging age- and/or sex- differences were noted in all tissues, which warrant further assessment using complementary approaches.ConclusionsOur validated CIBERSORTx signature matrices represent user-friendly tools for elucidating how disease processes influence cell lineage composition of human myocardium and skeletal muscle. These allow additional insights to be gained from existing and new bulk RNAseq data, which may help to define important cell lineages for further characterisation using complementary approaches.Conflict of InterestNone

In this paper, we study the problem of density deconvolution under general assumptions on the measurement error distribution. Typically, deconvolution estimators are constructed using Fourier transform techniques, and it is assumed that the characteristic function of the measurement errors does not have zeros on the real line. This assumption is rather strong and is not fulfilled in many cases of interest. In this paper, we develop a methodology for constructing optimal density deconvolution estimators in the general setting that covers vanishing and nonvanishing characteristic functions of the measurement errors. We derive upper bounds on the risk of the proposed estimators and provide sufficient conditions under which zeros of the corresponding characteristic function have no effect on estimation accuracy. Moreover, we show that the derived conditions are also necessary in some specific problem instances.

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue. Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and discriminative spot representations from spatial transcriptomics data.