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Abstract—Accurate cell counting is essential in various biomedical research and clinical applications, including cancer diagnosis, stem cell research, and immunology. Manual counting is labor-intensive and error-prone, motivating automation through deep learning techniques. However, training reliable deep learning models requires large amounts of high-quality annotated data, which is difficult and time-consuming to produce manually. Consequently, existing cell-counting datasets are often limited, frequently containing fewer than 500 images. In this work, we introduce a large-scale annotated dataset comprising 3,023 images from immunocytochemistry experiments related to cellular differentiation, containing over 430,000 manually annotated cell locations. The dataset presents significant challenges: high cell density, overlapping and morphologically diverse cells, a long-tailed distribution of cell count per image, and variation in staining protocols. We benchmark three categories of existing methods: regression-based, crowd-counting, and cell-counting techniques on a test set with cell counts ranging from 10 to 2,126 cells per image. We also evaluate how the Segment Anything Model (SAM) can be adapted for microscopy cell counting using only dot-annotated datasets. As a case study, we implement a density-map-based adaptation of SAM (SAM-Counter) and report a mean absolute error (MAE) of 22.12, which outperforms existing approaches (second-best MAE of 27.46). Our results underscore the value of the dataset and the benchmarking framework for driving progress in automated cell counting and provide a robust foundation for future research and development. 

This study presents an analysis of the fatigue damage experienced by mooring systems under extreme and operational wave conditions, with a discussion on the Reference Model 3 (RM3), a widely recognized point absorber wave energy converter (WEC), and the Reference Model 5 (RM5), a floating oscillating surge wave energy converter (FOSWEC). Utilizing the combined strengths of WEC-Sim and MoorDyn, both open-source simulation tools, the study investigates the dynamic behavior of mooring lines over the operational wave condition and a 100-year return period extreme wave condition. This study highlights the relationship between tension force and fatigue damage in mooring lines. The tension forces at various nodes of the mooring lines are calculated, revealing that the complex mooring design is causing a complex trend on the fatigue damage. Instead, variations in tension force show a more significant impact on cumulative fatigue damage, as evidenced by the higher damage observed in nodes experiencing greater tension variation. The findings contribute to a better understanding of the factors influencing fatigue damage in mooring lines of WECs and fatigue damage of different types of WECs, offering insights for more effective monitoring and strategies for WEC design optimization.