We designed and implemented algorithms for three-dimensional (3-D) reconstruction of brain images from serial sections using two important supercomputer architectures, vector and parallel. These architectures were represented by the Cray YMP and Connection Machine CM-2, respectively. The programs operated on linear octree representations of the brain data sets, and achieved 500-800 times acceleration when compared with a conventional laboratory workstation. As the need for higher resolution data sets increases, supercomputer algorithms may offer a means of performing 3-D reconstruction well above current experimental limits.
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http://dx.doi.org/10.1016/0169-2607(95)01619-5 | DOI Listing |
Sci Adv
January 2025
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA.
Predicting the dynamics of turbulent fluids has been an elusive goal for centuries. Even with modern computers, anything beyond the simplest turbulent flows is too chaotic and multiscaled to be directly simulatable. An alternative is to treat turbulence probabilistically, viewing flow properties as random variables distributed according to joint probability density functions (PDFs).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China.
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan.
This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was on enhancing simplicity, reproducibility, and convenience, and assessing the accuracy and speed of AI compared with conventional methods. A cohort of 3096 cases undergoing CT imaging up to the third lumbar (L3) level between 2011 and 2021 were included.
View Article and Find Full Text PDFMetab Brain Dis
January 2025
San Diego Supercomputer Center, UC San Diego, San Diego, CA, 92093, USA.
The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a robust machine-learning model for a reliable diagnosis of neurological diseases. We used miRNA sequence descriptors and gene target data to create machine-learning models that can be used as diagnostic tools.
View Article and Find Full Text PDFCommun Biol
December 2024
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
Proteins derived from microorganisms that survive in the harshest environments on Earth have stable activity under extreme conditions, providing rich resources for industrial applications and enzyme engineering. Due to the time-consuming nature of experimental determinations, it is imperative to develop computational models for fast and accurate prediction of protein optimal conditions. Previous studies were limited by the scarcity of data and the neglect of protein structures.
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