The choroid plays an essential role in the pathogenesis of various posterior segment diseases. However, traditional imaging methods still have limited cross-sectional observation of choroid. Enhanced depth imaging in spectral-domain optical coherence tomography (EDI SD-OCT) uses a closer scanning position to the eye to create an inverted SD-OCT image with the advantage of better depth sensitivity, which can observe choroidal structure and measure choroidal thickness (CT) accurately. At present, more and more choroidal thickness measurements have been made in normal and pathologic states, in order to understand the pathogenesis and differential diagnosis and prognosis of various diseases, especially for macular lesions. This paper would review relevant original literatures published from January 1, 2008, to February 1, 2020, to evaluate the relationship between the changes of CT with EDI SD-OCT and macular diseases.
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http://dx.doi.org/10.1155/2020/9503795 | DOI Listing |
Cell Death Discov
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Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
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The connection between the respiratory capacity of skeletal muscle mitochondria and athletic performance is widely acknowledged in contemporary research. Building on a solid foundation of prior studies, current research has fostered an environment where scientists can effectively demonstrate how a tailored regimen of exercise intensity, duration, and frequency significantly boosts mitochondrial function within skeletal muscles. The range of exercise modalities is broad, spanning from endurance and high-intensity interval training to resistance-based exercises, allowing for an in-depth exploration of effective strategies to enhance mitochondrial respiratory capacity-a key factor in improving exercise performance, in other words offering a better skeletal muscle capacity to cope with exercise demands.
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Department of English Language, College of Arts, King Faisal University, Al Ahsa, Saudi Arabia.
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Institute of Automation, Chinese Academy of Sciences, MAIS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explores alternatives to the standard convolution, with the objective of augmenting its feature extraction prowess while maintaining a similar parameter count. We propose innovative solutions targeting depthwise separable convolution and standard convolution, culminating in our Multi-scale Progressive Inference Convolution (MPIC).
View Article and Find Full Text PDFJ Autism Dev Disord
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Department of Public Health Sciences, Clemson University, Clemson, SC, USA.
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