This paper presents spatial maps of the arsenic, lead, and polycyclic aromatic hydrocarbon (PAH) soil contamination in Sydney, Nova Scotia, Canada. The spatial maps were designed to create exposure cohorts to help understand the observed increase in health effects. To assess whether contamination can be a proxy for exposures, the following hypothesis was tested: residential soils were impacted by the coke oven and steel plant industrial complex. The spatial map showed contaminants are centered on the industrial facility, significantly correlated, and exceed Canadian health risk-based soil quality guidelines. Core samples taken at 5-cm intervals suggest a consistent deposition over time. The concentrations in Sydney significantly exceed background Sydney soil concentrations, and are significantly elevated compared with North Sydney, an adjacent industrial community. The contaminant spatial maps will also be useful for developing cohorts of exposure and guiding risk management decisions.
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http://dx.doi.org/10.1080/19338244.2010.516780 | DOI Listing |
Magn Reson Med
January 2025
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Purpose: To develop a deep subspace learning network that can function across different pulse sequences.
Methods: A contrast-invariant component-by-component (CBC) network structure was developed and compared against previously reported spatiotemporal multicomponent (MC) structure for reconstructing MR Multitasking images. A total of 130, 167, and 16 subjects were imaged using T, T-T, and T-T- -fat fraction (FF) mapping sequences, respectively.
Sci Rep
January 2025
School of Computer Science, Hunan University of Technology, Tianyuan District, Zhuzhou, 412007, China.
The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. In response to these problems, this paper presents a lightweight algorithm DA-DeepLabv3 + based on densely connected and attention mechanisms. Firstly, the lightweight MobileNetV2 network is employed to replace the Xception feature extraction network, thereby reducing the number of model parameters.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer and Information Technology, Xinyang Normal University, Xinyang, Henan Province, 464000, P. R. China.
Accurate detection of surface defects on strip steel is essential for ensuring strip steel product quality. Existing deep learning based detectors for strip steel surface defects typically strive to iteratively refine and integrate the coarse outputs of the backbone network, enhancing the models' ability to express defect characteristics. Attention mechanisms including spatial attention, channel attention and self-attention are among the most prevalent techniques for feature extraction and fusion.
View Article and Find Full Text PDFEcol Appl
January 2025
Parks Victoria, Marine and Coastal Science and Programs, Melbourne, Victoria, Australia.
Kelp forests serve as the foundation for shallow marine ecosystems in many temperate areas of the world but are under threat from various stressors, including climate change. To better manage these ecosystems now and into the future, understanding the impacts of climate change and identifying potential refuges will help to prioritize management actions. In this study, we use a long-term dataset of observations of kelp percentage cover for two dominant canopy-forming species off the coast of Victoria, Australia: Ecklonia radiata and Phyllospora comosa.
View Article and Find Full Text PDFComputational theories posit that attention is guided by a combination of spatial maps for individual features that can be dynamically weighted according to task goals. Consistent with this framework, when a stimulus contains several features, attending to one or another feature results in stronger fMRI responses in regions preferring the attended feature. We hypothesized that multivariate activation patterns across feature-responsive cortical regions form spatial 'feature dimension maps', which combine to guide attentional priority.
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