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Kidney disease, the ninth leading cause of death in the United States, has one of the poorest diagnostic efficiencies of only 10%. Conventional diagnostic methods often rely on light microscopy analysis of 2D fixed tissue sections with limited molecular insight compared to omics studies. Targeting multiple features in a biopsy using molecular or chemical reagents can enhance molecular phenotyping but are limited by overlap of their spatial and chromatic properties, variations in quality of the products, limited multimodal nature and need additional tissue processing.

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  • This study explores the third-order fractional nonlinear Schrödinger equation (FNLSE) using a new analytical method (NEDAM) to identify optical soliton solutions in various forms.
  • The researchers focus on understanding nonlinear wave behavior relevant to plasma physics and optics, validating their results with stability assessments and analysis techniques like chaos and bifurcation.
  • The findings contribute to advancements in fields related to nonlinear wave propagation, including Bose-Einstein condensates and ultrafast fibre optics, by providing visual data through 2D, 3D, and contour plots.
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Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported.

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  • Optical equalization helps compensate for chromatic dispersion in optical communication, improving system performance.
  • A photonic integrated circuit (PIC) filter is used for both static and adaptive optical equalization techniques: static relies on known fiber specs, while adaptive adjusts to transmission impairments with a least-mean squares algorithm.
  • Experimental results show both methods achieve an 18-dB Q-factor for a 14-Gbd QPSK signal over 30 km, with simulations indicating increased performance using additional taps.
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This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs.

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