This dataset contains 2850 photographs of the seafloor in coral communities from Venezuela that were taken during 2017 and 2018. We used a hierarchical experimental design with four random factors representing four different spatial scales: (1) region (hundreds of kilometers), (2) localities (tens of kilometers), (2) reef sites (hundreds of meters) and (3) transects (a couple meters) across the Venezuelan coast. At each site, four 30-m transects were deployed parallel to the coastline, and 15 pictures were taken every other meter at each transect, containing an area of at least 80 × 90cm with enough resolution to identify benthic groups. This dataset covers spatial scales from a few meters to hundreds of kilometers; marine protected areas, and non-protected areas; coastal zones, continental and oceanic islands. These images have the potential to be further used for training researchers in benthic organisms identification, and training artificial intelligence classification algorithms. Also, they represent and updated baseline to perform spatial and temporal comparisons in Venezuela or further studies involving multiple spatial scales in the region.
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http://dx.doi.org/10.1016/j.dib.2021.107235 | DOI Listing |
Sci Rep
January 2025
Bodega Marine Laboratory, University of California, Davis, Bodega Bay, CA, 94923, USA.
Marine foundation species are increasingly impacted by anthropogenic stressors, driving a loss of diversity within these critical habitats. Prior studies suggest that species diversity within mussel beds has declined precipitously in southern California, USA, but it is unclear whether a similar loss has occurred farther north. Here, we resurvey a mussel bed community in northern California first sampled in 1941 to evaluate changes in diversity after 78 years.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, 11829, Badr City, Egypt. Electronic address:
Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately delineating boundaries due to overfitting to weak annotations such as bounding boxes. This issue is particularly pronounced in thyroid ultrasound images, where low contrast and noisy backgrounds hinder precise segmentation. In this paper, we propose a novel weakly-supervised segmentation framework that addresses these challenges.
View Article and Find Full Text PDFEarly Hum Dev
January 2025
Department of Neonatology, Máxima Medical Center, Veldhoven, Noord-Brabant, the Netherlands.
Background: Although preterm birth is associated with deficits in both motor and cognitive functioning, the association between early motor skills and cognitive outcomes at a later age remains underexplored.
Aim: To evaluate associations between motor skills at age 5.5 and cognitive functioning at age 8.
PLoS One
January 2025
Real Estate Research Center, Nanjing Agricultural University, Nanjing, China.
This paper aims to reveal the changing characteristics of the contribution rates of different production factors in China since the reform and opening up from two dimensions: stage and space. The study used national data from 1978 to 2021 and provincial data from 2000 to 2020, combined with methods such as C-D production function and spatial econometrics for analysis. Research has found that: (1) In terms of stage characteristics, during the structural adjustment stage (1978-1998), economic growth mainly relies on capital and labor input, and the contribution rate of land factors gradually decreases.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method.
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