A scanning transmission electron microscope (STEM) produces a convergent beam electron diffraction pattern at each position of a raster scan with a focused electron beam, but recording this information poses major challenges for gathering and storing such large data sets in a timely manner and with sufficient dynamic range. To investigate the crystalline structure of materials, a 16x16 analog pixel array detector (PAD) is used to replace the traditional detectors and retain the diffraction information at every STEM raster position. The PAD, unlike a charge-coupled device (CCD) or photomultiplier tube (PMT), directly images 120-200keV electrons with relatively little radiation damage, exhibits no afterglow and limits crosstalk between adjacent pixels. Traditional STEM imaging modes can still be performed by the PAD with a 1.1kHz frame rate, which allows post-acquisition control over imaging conditions and enables novel imaging techniques based on the retained crystalline information. Techniques for rapid, semi-automatic crystal grain segmentation with sub-nanometer resolution are described using cross-correlation, sub-region integration, and other post-processing methods.
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http://dx.doi.org/10.1016/j.ultramic.2008.11.023 | DOI Listing |
J Hematol Oncol
January 2025
Department of Gynecology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
N7-methylguanosine (m7G) is an important RNA modification involved in epigenetic regulation that is commonly observed in both prokaryotic and eukaryotic organisms. Their influence on the synthesis and processing of messenger RNA, ribosomal RNA, and transfer RNA allows m7G modifications to affect diverse cellular, physiological, and pathological processes. m7G modifications are pivotal in human diseases, particularly cancer progression.
View Article and Find Full Text PDFInsights Imaging
January 2025
Diagnostic and Interventional Radiology, University Hospital of Zurich, University Zurich, Zurich, Switzerland.
Objectives: To compare and correlate bone edema volume detected by 3D-short-tau-inversion-recovery (STIR) sequence to osseous decay detected by a T1-based sequence and conventional panoramic radiography (OPT).
Materials And Methods: Patients with clinical evidence of apical periodontitis were included retrospectively and received OPT as well as MRI of the viscerocranium including a 3D-STIR and a 3D-T1 gradient echo sequence. Bone edema was visualized using the 3D-STIR sequence and periapical hard tissue changes were evaluated using the 3D-T1 sequence.
J Imaging Inform Med
January 2025
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Continuous and consistent access to quality medical imaging data stimulates innovations in artificial intelligence (AI) technologies for patient care. Breakthrough innovations in data-driven AI technologies are founded on seamless communication between data providers, data managers, data users and regulators or other evaluators to determine the standards for quality data. However, the complexity in imaging data quality and heterogeneous nature of AI-enabled medical devices and their intended uses presents several challenges limiting the clinical translation of novel AI technologies.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, and ranks among the most lethal malignancies globally, primarily due to its high rates of recurrence and metastasis. Despite the urgency, no reliable biomarkers currently exist for predicting tumor recurrence in HCC. Telomerase reverse transcriptase (TERT) promoter mutations (TERTpm) and cellular tumor antigen p53 mutations (TP53m) have been frequently documented in HCC, but their combined clinical significance remains undefined.
View Article and Find Full Text PDFSci Rep
January 2025
College of Electrical and Information Engineering, Beihua University, Jilin, 132013, China.
Remote sensing images often suffer from the degradation effects of atmospheric haze, which can significantly impair the quality and utility of the acquired data. A novel dehazing method leveraging generative adversarial networks is proposed to address this challenge. It integrates a generator network, designed to enhance the clarity and detail of hazy images, with a discriminator network that distinguishes between dehazed and real clear images.
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