Abstract: This system presents a comprehensive approach for detecting protein-protein interactions and exploring protein structures. It encompasses various components, including a receptacle for receiving cross-linked precursor peptides, a mass spectrometer for generating MS1 and MS2 spectra, and modules for precursor mass refinement, MS2 spectrum scoring, protein score database construction, and feedback mechanism processing. Through the integration of these elements, the system facilitates the identification of peptide interactions and protein structures, leveraging a cross-linking dataset obtained from proteins cross-linked with cleavable or non-cleavable crosslinking reagents.

Abstract: Systems and methods that facilitate image motion analysis are described herein. According to a first image motion analysis technique a first image is warped according to a locally affine model. The first warped image and a second image are compared and a match between the first warped image and the second image is discovered. A value for a motion parameter is estimated based on the match. According to a second image motion analysis technique, an image sequence is converted into an input matrix. A column of the input matrix corresponds to a vectorized image related to the image sequence. The input matrix is approximated with a low rank matrix having a lower rank than the input matrix. One or more outliers of the input matrix are detected.

Abstract: The subject disclosure relates to motion estimation and compensation of feature-motion decorrelation. In an aspect, coupled filtering can be employed to solve feature-motion decorrelation problems. In further aspects, a linear convolution model can be employed to model image variation caused by motion, and an image before motion and a warped image after motion can be filtered with a pair of filters having motion parameters shared with a tissue motion model. Compensation of feature-motion decorrelation in ultrasound images demonstrates various aspects of the subject disclosure.

Abstract: Systems and methods that facilitate image motion analysis are described herein. According to a first image motion analysis technique a first image is warped according to a locally affine model. The first warped image and a second image are compared and a pattern that matches between the first warped image and the second image is discovered. A value for a motion parameter is estimated based on the pattern. According to a second image motion analysis technique, an image sequence is converted into an input matrix. A column of the input matrix corresponds to a vectorized image related to the image sequence. The input matrix is approximated with a low rank matrix having a lower rank than the input matrix. One or more outliers of the low rank matrix are detected.

Abstract: The subject disclosure relates to motion estimation and compensation of feature-motion decorrelation. In an aspect, coupled filtering can be employed to solve feature-motion decorrelation problems. In further aspects, a linear convolution model can be employed to model image variation caused by motion, and an image before motion and a warped image after motion can be filtered with a pair of filters having motion parameters shared with a tissue motion model. Compensation of feature-motion decorrelation in ultrasound images demonstrates various aspects of the subject disclosure.