Ultrafast 2D NMR replaces the time-domain parametrization usually employed to monitor the indirect-domain spin evolution, with an equivalent encoding along a spatial geometry. When coupled to a gradient-assisted decoding during the acquisition, this enables the collection of complete 2D spectra within a single transient. We have presented elsewhere two strategies for carrying out the spatial encoding underlying ultrafast NMR: a discrete excitation protocol capable of imparting a phase-modulated encoding of the interactions, and a continuous protocol yielding amplitude-modulated signals. The former is general but has associated with it a number of practical complications; the latter is easier to implement but unsuitable for certain 2D NMR acquisitions. The present communication discusses a new protocol that incorporates attractive attributes from both alternatives, imparting a continuous spatial encoding of the interactions yet yielding a phase modulation of the signal. This in turn enables a number of basic experiments that have shown particularly useful in the context of in vivo 2D NMR, including 2D J-resolved and 2D H,H-COSY spectroscopies. It also provides a route to achieving sensitivity-enhanced acquisitions for other homonuclear correlation experiments, such as ultrafast 2D TOCSY. The main features underlying this new spatial encoding protocol are derived, and its potential demonstrated with a series of phase-modulated homonuclear single-scan 2D NMR examples.
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J Exp Psychol Hum Percept Perform
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Faculte de Psychologie et des Sciences de l'Education, Universite de Geneve.
Visual working memory (VWM) is a core cognitive system enabling us to select and briefly store relevant visual information. We recently observed that more salient items were recalled more precisely from VWM and demonstrated that these effects of salience resisted manipulations of reward, probability, and selection history. Here, we investigated whether and how salience interacts with shifts of attention induced by pre- and retrocueing.
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January 2025
School of Psychology and Neuroscience, University of St. Andrews. KY16 9JP, United Kingdom.
The encoding of episodic memories depends on segmentation; memory performance improves when segmentation is available and performance is impaired when segmentation is absent. Indeed, for episodic memories to be created, the encoding of information into long-term memory requires the experience of event boundaries (i.e.
View Article and Find Full Text PDFSci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
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January 2025
Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Electronic address:
Background: The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.
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December 2024
Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore. Electronic address:
Manual annotation of ultrasound images relies on expert knowledge and requires significant time and financial resources. Semi-supervised learning (SSL) exploits large amounts of unlabeled data to improve model performance under limited labeled data. However, it faces two challenges: fusion of contextual information at multiple scales and bias of spatial information between multiple objects.
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