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Searching to extrapolate embedding for out-of-graph node representation learning.
Neural Netw
January 2025
Department of Electronic Engineering, Tsinghua University, Beijing, China. Electronic address:
Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from community detection, recommendation system to malware detection. Although existing methods can be adapted for out-of-graph node representation learning, real-world challenges such as fixed in-graph node embedding and data diversity essentially limit the performance of these methods.
View Article and Find Full Text PDFBRCA1 and BRCA2 mutations testing in prostate cancer: Detection in formalin fixed paraffin embedded (FFPE) and blood samples.
Pathol Res Pract
January 2025
Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", Via L. Armanni 5, Naples 80138, Italy.
Prostate cancer (PC) represents one of the leading causes of cancer-related morbidity and mortality in men, requiring further understanding to improve diagnosis and treatment. Germline BRCA1/2 mutations, primarily identified in other hereditary cancers, confer an increased risk of developing PC; thus, testing is essential to assess cancer risk, guiding preventive strategies and screening. Recently, somatic BRCA1/2 mutations have emerged as pivotal predictive biomarkers of responsiveness to the poly ADP-ribose polymerase (PARP) inhibitors.
View Article and Find Full Text PDFHigh Sensitivity of Fine-Needle Aspiration Calcitonin in Detecting Medullary Thyroid Carcinoma Is Independent of Predefined Decisional Thresholds.
Diagn Cytopathol
January 2025
Servizio di Endocrinologia e Diabetologia, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland.
The measurement of Calcitonin (Ctn) in fine-needle aspiration (FNA) washout fluids (FNA-Ctn) has demonstrated excellent sensitivity, significantly higher than FNA cytology, in detecting medullary thyroid carcinoma (MTC). However, the absence of a fixed cutoff value for FNA-Ctn poses a limitation. This study aimed to investigate whether the sensitivity of FNA-Ctn in detecting MTC varies with different cutoffs reported in the literature.
View Article and Find Full Text PDFDetection Method for Bolt Loosening Based on Summation Coefficient of Absolute Spectrum Ratio.
Sensors (Basel)
January 2025
Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
A bolt loosening detection method based on the summation coefficient of the absolute spectrum ratio technique is proposed to address the prevalent issue of bolt loosening in mechanical connections. This proposed method involves initially collecting vibration and rotation speed signals of the motor bolt connection structure, acquiring the baseline spectrum curve of a healthy structure and the spectrum curves of non-healthy structures under different degrees of bolt looseness through chirp Fourier transform (CFT). Subsequently, the spectrum ratio curves between healthy and non-healthy structures are calculated for different degrees of bolt loosening, and then the Summation Coefficient of Absolute Spectrum Ratio (SCASR) is defined to indicate the looseness.
View Article and Find Full Text PDFEBR-YOLO: A Lightweight Detection Method for Non-Motorized Vehicles Based on Drone Aerial Images.
Sensors (Basel)
January 2025
College of Information Science and Technology, Donghua University, Shanghai 201620, China.
Modern city construction focuses on developing smart transportation, but the recognition of the large number of non-motorized vehicles in the city is still not sufficient. Compared to fixed recognition equipment, drones have advantages in image acquisition due to their flexibility and maneuverability. With the dataset collected from aerial images taken by drones, this study proposed a novel lightweight architecture for small objection detection based on YOLO framework, named EBR-YOLO.
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