Grating-based X-ray phase contrast imaging technology is one of the most potential imaging methods in real applications. It can be classified into two categories: interferometry and non-interferometric imaging. The non-interferometric grating-based X-ray phase contrast imaging (NIGPCI) instrument has a great advantage in the forthcoming commercial applications for the flexible system design and the use of large periodic gratings. The performance of the NIGPCI instrument depends on its angular sensitivity to a great extent. Therefore, good angular sensitivity is mandatory in order to obtain high quality phase-contrast images. Several parameters, such as the X-ray spectrum, the inter-grating distances, and the parameters of the three gratings, influence the angular sensitivity of the imaging system. However, the quantitative relationship between the angular sensitivity and grating duty cycle is unclear. Therefore, this paper is devoted to revealing their internal relation by theoretical deduction and emulation of the imaging process with the theories of linear system and Fourier optics. Furthermore, a quantitative analysis method to optimize the duty cycles of gratings is proposed and its applicability to a general NIGPCI system is verified.
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Front Psychiatry
January 2025
Department of Psychiatry, Faculty of Medicine, Masaryk University, Brno, Czechia.
Introduction: Virtual reality (VR) holds significant promise for psychiatric research, treatment, and assessment. Its unique ability to elicit immersion and presence is important for effective interventions. Immersion and presence are influenced by matching-the alignment between provided sensory information and user feedback, and self-presentation-the depiction of a user's virtual body or limbs.
View Article and Find Full Text PDFChemistry
January 2025
National Chi Nan University, Department of Applied Chemistry, TAIWAN.
Three fluorescent Zn coordaintion polymers (CPs) have been synthesized from the reactions of Zn(NO3)2∙6H2O, benzene-1,4-dicarboxylic acid (1,4-H2bdc), and angular carbazole-derived bispyridyl ligands (Cz-3,6-bpy or Cz-Pr-3,6-bpy). CPs 1-3 all adopt similar two-dimensional (2D) ring-and-rod layer structures, described as topologically 4-connected 2∙65 nets where the Zn(II) centers act as 4-connected nodes. CPs 1 and 2 are a pair of solvent-mediated supramolecular isomers where the former shows a two-fold interlocked 2D → 2D polyrotaxane-like entangled net and the latter reveals a four-fold interpenetrated 2D → 3D polyrotaxane entanglement.
View Article and Find Full Text PDFBiomed Opt Express
January 2025
Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, 200093 Shanghai, China.
Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis.
View Article and Find Full Text PDFJ Bone Joint Surg Am
November 2024
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed.
J Phys Chem Lett
January 2025
Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.
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