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We demonstrate a novel technique to measure spatially resolved birefringence structures in an all-digital fashion with a digital micro-mirror device (DMD). The technique exploits the polarization independence of DMDs to apply holographic phase control to orthogonal polarization components and requires only a static linear polarizer as an analyzer for the resulting phase shift polarization measurements. We show the efficacy of this approach by spatially resolving complex polarization structures, including nano-structured metasurfaces, customized liquid crystal devices, as well as chiral L-Alanine and N-Acetyl-L-cystein crystals. Concentration dependent measurements of optical rotation in glucose and fructose solutions are also presented, demonstrating the technique's versatility. Unlike conventional approaches, our technique is calibration free and has no moving parts, offers high frame rates and wavelength independence, and is low cost, making it highly suitable to a range of applications, including pharmaceutical manufacturing, saccharimetry and stress imaging.
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http://dx.doi.org/10.1364/OE.437722 | DOI Listing |
PLoS One
September 2025
School of Computer Science and Technology, Huaiyin Normal University, Huai'an, Jiangsu, China.
Previous studies have demonstrated that metric learning approaches yield remarkable performance in the field of kinship verification. Nevertheless, a prevalent limitation of most existing methods lies in their over-reliance on learning exclusively from specified types of given kin data, which frequently results in information isolation. Although generative-based metric learning methods present potential solutions to this problem, they are hindered by substantial computational costs.
View Article and Find Full Text PDFPLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFBioinformatics
September 2025
Department of Mathematical Sciences, The University of Texas at Dallas, TX United States.
Motivation: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes that elucidate the clustering result, referred to as spatial domain-specific discriminating genes (DGs).
View Article and Find Full Text PDFUnlabelled: Passive Acoustic Mapping (PAM) is rapidly emerging as a ubiquitous tool for real-time localization and monitoring of therapeutic ultrasound treatments involving cavitation in the context of safety or efficacy. The ability of PAM to spatially quantify and resolve cavitation activity offers a unique opportunity to correlate the energy of cavitation phenomena with locally observed bioeffects.
Objective: We aim to develop methods of measuring and reporting spatio-temporally varying cavitation energies that are energy-preserving, device-independent, and adequately normalized to the volume of tissue being affected by the reported cavitation activity.
IEEE Trans Biomed Eng
September 2025
Objective: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features.
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