Quest Diagnostics Investments Incorporated - Wilmington DE
International Classification:
G01N 33/00
US Classification:
436129, 436127, 436173
Abstract:
Provided are methods and compositions for quantitatively measuring the amount of an unlabeled organic acid in a sample. Oxygen-18 labeled organic acids are used as internal standards to adjust for the loss of a structurally similar or identical unlabeled organic acid through processing required for its detection, such as by mass spectrometry. The methods of the invention are useful for diagnosing inborn errors of metabolism in an individual by quantitating signature organic acids in body fluids such as urine or plasma.
Preparation Of Highly Polyunsaturated Fatty Acid-Containing Phosphatidylserine And Phosphatidic Acid
Su Chen - Aliso Viejo CA, US Hung Kwong - Aliso Viejo CA, US
International Classification:
A61K031/685 C12P007/64
US Classification:
435134000, 514078000, 554078000
Abstract:
Preparation of highly polyunsaturated fatty acid-containing phosphatidylserine and phosphatidic acid by phospholipase D-catalyzed transphosphatidylation of fish liver phosphatidylcholine is disclosed.
Method For Preparation Of Polyunsaturated Fatty Acid-Containing Phosphatidylserine
Su Chen - Aliso Viejo CA, US Hung Kwong - Aliso Viejo CA, US
International Classification:
C07F 9/02 A61K 31/685 C12P 13/00
US Classification:
435128, 514 78, 554 78
Abstract:
A method for the preparation of highly polyunsaturated fatty acid-containing phosphatidylserine and phosphatidic acid by phospholipase D-catalyzed transphosphatidylation of fish liver phosphatidylcholine and L-serine is disclosed.
- San Jose CA, US Yifan Jiang - Austin TX, US Yilin Wang - San Jose CA, US Jianming Zhang - Campbell CA, US Kalyan Sunkavalli - San Jose CA, US Sarah Kong - Cupertino CA, US Su Chen - San Jose CA, US Sohrab Amirghodsi - Seattle WA, US Zhe Lin - Fremont CA, US
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.