Effects of Electron Microscope Parameters and Sample Thickness on High Angle Annular Dark Field Imaging.

Scanning transmission electron microscopy (STEM) developed into a very important characterization tool for atomic analysis of crystalline specimens. High-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) has become one of the most powerful tools to visualize material structures at atomic resolution. However, the parameter of electron microscope and sample thickness is the important influence factors on HAADF-STEM imaging. The effect of convergence angle, spherical aberration, and defocus to HAADF imaging process has been analyzed through simulation. The applicability of two HAADF simulation software has been compared, and suggestions for their usage have been given.

Proteogenomic characterization identifies clinically relevant subgroups of intrahepatic cholangiocarcinoma.

We performed proteogenomic characterization of intrahepatic cholangiocarcinoma (iCCA) using paired tumor and adjacent liver tissues from 262 patients. Integrated proteogenomic analyses prioritized genetic aberrations and revealed hallmarks of iCCA pathogenesis. Aflatoxin signature was associated with tumor initiation, proliferation, and immune suppression. Mutation-associated signaling profiles revealed that TP53 and KRAS co-mutations may contribute to iCCA metastasis via the integrin-FAK-SRC pathway. FGFR2 fusions activated the Rho GTPase pathway and could be a potential source of neoantigens. Proteomic profiling identified four patient subgroups (S1-S4) with subgroup-specific biomarkers. These proteomic subgroups had distinct features in prognosis, genetic alterations, microenvironment dysregulation, tumor microbiota composition, and potential therapeutics. SLC16A3 and HKDC1 were further identified as potential prognostic biomarkers associated with metabolic reprogramming of iCCA cells. This study provides a valuable resource for researchers and clinicians to further identify molecular pathogenesis and therapeutic opportunities in iCCA.

Illumination modulation for reflective and fluorescent separation.

In this Letter, we present, to the best of our knowledge, a novel illumination modulation method for reflective and fluorescent separation by using only one spectral image. Specifically, we present an optical system using off-the-shelf devices to generate high frequency illumination, which is desirable in reflective-fluorescent separation tasks. In addition, we employ the total variation regularization scheme to account for spectral-spatial correlation, which makes our method robust to noise. Experiments on both simulated and real data verify the effectiveness and practicality of our method.

Spectral reflectance recovery using optimal illuminations.

The spectral reflectance of objects provides intrinsic information on material properties that have been proven beneficial in a diverse range of applications, e.g., remote sensing, agriculture and diagnostic medicine, to name a few. Existing methods for the spectral reflectance recovery from RGB or monochromatic images either ignore the effect from the illumination or implement/optimize the illumination under the linear representation assumption of the spectral reflectance. In this paper, we present a simple and efficient convolutional neural network (CNN)-based spectral reflectance recovery method with optimal illuminations. Specifically, we design illumination optimization layer to optimally multiplex illumination spectra in a given dataset or to design the optimal one under physical restrictions. Meanwhile, we develop the nonlinear representation for spectral reflectance in a data-driven way and jointly optimize illuminations under this representation in a CNN-based end-to-end architecture. Experimental results on both synthetic and real data show that our method outperforms the state-of-the-arts and verifies the advantages of deeply optimal illumination and nonlinear representation of the spectral reflectance.