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An unsupervised automatic texture classification method for ultrasound images of thyroid nodules.
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Beijing institute of control and electronic technology, 51 Beilijia, Muxidi, Xicheng District, Beijing 100038, Beijing, 100038, CHINA.
Objective Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules.
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Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Humans exploit motor synergies for motor control; however, how they emerge during motor learning is not clearly understood. Few studies have dealt with the computational mechanism for generating synergies. Previously, optimal control generated synergistic motion for the upper limb; however, it has not yet been applied to the high-dimensional whole-body system.
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Background: The J20 mouse is an established model of amyloid pathology, exhibiting neuropathological and behavioural symptoms reflective of human Alzheimer's disease (AD). Previous work, conducted by Castanho et al (2020), revealed transcriptomic change in the hippocampus of J20 mice to be associated with the accumulation of amyloid pathology. Here, we investigated the spatial distribution of such transcriptomic changes using novel spatial transcriptomic technology.
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