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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

Spatial transcriptomics (ST) technologies offer unprecedented insights into the spatial organization of gene expression, allowing for the study of tissue architecture, domain boundaries, and cell–cell interactions. However, most ST data generated so far are at multicellular resolution, where each spot captures transcripts from a mixture of diverse cells of different cell types. While reference‐based deconvolution approaches offer robust solutions, they rely heavily on the availability and quality of external single‐cell reference data, which may be incomplete, unavailable, or poorly matched to the spatial data. Moreover, even when such references are available, they often represent only broad cell types, potentially obscuring finer subpopulation structures and masking intra‐type heterogeneity. To overcome these limitations, we introduced gnSPADE, a reference‐free spatial deconvolution method that incorporates gene network structures via a Markov random field within a latent Dirichlet allocation modeling framework. gnSPADE jointly infers cell type‐specific transcriptional profiles and spatial compositions without external references. When applied to synthetic and real ST datasets, gnSPADE achieves improved accuracy, spatial resolution, and biological interpretability compared to other methods, highlighting the power of reference‐free deconvolution in resolving complex tissues. gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization without external single‐cell references.

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.

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.

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.

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.

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.

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.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment. Several bottlenecks exist in metabolomics data analysis. Here, the authors present MetaboAnalystR 4.0 as a unified workflow for LC-MS untargeted metabolomics. It highlights significant improvements in LC-MS2 spectral processing and functional analysis, providing an end-to-end computational pipeline.