Abstract: The present disclosure provides systems and methods for radio-luminescent imaging dosimetry of variable dose rate radiation beams at high spatial resolution. Exemplary embodiments include time-gating one or more camera shutters such that one image frame from a plastic scintillator detector (PSD) is captured per camera shutter for each radiation beam pulse produced from a pulsed beam accelerator directed toward the PSD.

Abstract: A computer implemented method of treatment targeting includes receiving magnetic resonance (MR) images of a subject including a target region, generating at least one contour of at least one surrogate element apart from the target region in the MR images, and determining a location of the target region in each of the MR images based on a location of the at least one contour in the MR images.

Abstract: A computer implemented method of treatment targeting includes receiving magnetic resonance (MR) images of a subject including a target region, generating at least one contour of at least one surrogate element apart from the target region in the MR images, and determining a location of the target region in each of the MR images based on a location of the at least one contour in the MR images.

Abstract: The present invention discloses a construction method for heuristic metabolic co-expression network and the system thereof. Based on the max-dependent criteria, the present invention treats the characterized multivariate mutual information of a plurality of metabolites as mutual function value, and applies an optimization searching for the best feature subset, with a heuristics computational intelligence multimodal optimization algorithm. And by running the optimization process in a plurality of times, combining and studying the results in each time running, a co-expression network structure is built. Finally, a threshold for segmentations is calculated through probability models, and an exact and stable metabolic co-expression network is obtained.

Abstract: The present invention discloses a metabolic mass spectrometry screening method for diseases based on deep learning and the system thereof. The present invention is based on the prior metabolic mass spectrometry database, and by extracting and integrating specific types of metabolic mass spectrometry samples (such as a disease), which are applied to train a deep learning network, and make it be able to determinate a plurality of types or states simultaneously. Then applying the specific network into screening a real input metabolic mass spectrometry.

Abstract: The present invention discloses a data compression system for DNA sequence, which is a lossless compression system for DNA sequence data, based on the MA-ARV codebook, which is able to search the approximate repeat fragment of the MA-ARV code vector in the whole sequence, and use a heuristic optimization algorithm of memetic algorithm to optimize the construction process of the compressed codebook, so as to fully use the repeat nature of DNA sequence data, and eliminate the redundancy effectively.