The authors of the above article drew to our attention that, in the above paper, they had identified three instances of data overlapping between data panels, suggesting that data purportedly showing results obtained under different experimental conditions had been derived from the same original source. Comparing among the data panels, two pairs of panels in Fig. 4B were shown to be overlapping, and a further pair of panels showed overlapping data in Fig. 6B. The authors were presented with an opportunity to correct their figures in a Corrigendum, although it has subsequently come to light that the replacement figures themselves featured problems with overlapping data. Given the errors that have been identified in the compilation of the figures in this article, the Editor of Oncology Reports has decided that this article should be retracted from the publication owing to a lack of overall confidence in the presented data. The authors all agree to the retraction of this article, and the Editor and the authors apologize for any inconvenience that might result from this retraction. [the original article was published in Oncology Reports 39: 1825-1834, 2018; DOI: 10.3892/or.2018.6261].
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http://dx.doi.org/10.3892/or.2020.7785 | DOI Listing |
Sci Rep
December 2024
British Antarctic Survey, High Cross, Madingley Road, Cambridge, UK.
Marine microplastic is pervasive, polluting the remotest ecosystems including the Southern Ocean. Since this region is already undergoing climatic changes, the additional stress of microplastic pollution on the ecosystem should not be considered in isolation. We identify potential hotspot areas of ecological impact from a spatial overlap analysis of multiple data sets to understand where marine biota are likely to interact with local microplastic emissions (from ship traffic and human populations associated with scientific research and tourism).
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December 2024
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China.
Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. The model takes as inputs both one-dimensional data and two-dimensional Continuous Wavelet Transform images.
View Article and Find Full Text PDFMagn Reson Med
December 2024
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
Purpose: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal.
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December 2024
Department of Radiology, Stanford University School of Medicine, Stanford, California, USA.
Purpose: To measure and validate elevated succinate in brain during circulatory arrest in a piglet model of cardiopulmonary bypass.
Methods: Using data from an archive of 3T H MR spectra acquired in previous in-magnet studies, dynamic plots of succinate, spectral simulations and difference spectra were generated for analysis and validation.
Results: Elevation of succinate during circulatory arrest was observed and validated.
Hum Brain Mapp
January 2025
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject's brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different areas of the brain, and currently there is no commercial or research neuronavigation system that can highlight in real time the brain's structural connections during TMS. This lack of real-time visualization may overlook critical inter-individual differences in brain connectivity and does not provide the opportunity to target brain networks.
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