Xiao Xia Chen

7160856, Jan 9, 2007

Jul 25, 2002

10/205021

Samuel J. Danishefsky - Englewood NJ, US
Dalibor Sames - New York NY, US
Samuel Hintermann - Basel, CH
Xiao Tao Chen - Newark DE, US
Jacob B. Schwarz - Ann Arbor MI, US
Peter Glunz - Wilmington DE, US
Philip O. Livingston - New York NY, US
Scott Kuduk - Harleyville PA, US
Lawrence Williams - New York NY, US

Sloan-Kettering Institute for Cancer Research - New York NY

A61K 38/14
A61K 39/385
C07K 9/00

514 8, 4241851, 4241941, 4242771, 530322

The present invention provides novel α-O-linked glycoconjugates such as α-O-linked glycopeptides, as well convergent methods for synthesis thereof. The general preparative approach is exemplified by the synthesis of the mucin motif commonly found on epithelial tumor cell surfaces. The present invention further provides compositions and methods of treating cancer using the α-O-linked glycoconjugates.

20040102607, May 27, 2004

Jun 19, 2003

10/600012

Samuel Danishefsky - Englewood NJ, US
Dalibor Sames - New York NY, US
Samuel Hintermann - Basel, CH
Xiao Chen - Newark DE, US
Jacob Schwarz - Ann Arbor MI, US
Peter Glunz - Wilmington DE, US
Philip Livingston - New York NY, US
Scott Kuduk - Harleyville PA, US
Kenneth Lloyd - New York NY, US
Lawrence Williams - New York NY, US
Valery Kudryashov - Brooklyn NY, US

C07K009/00
A61K038/14

530/322000, 514/008000

The present invention provides novel -O-linked glycoconjugates such as -O-linked glycopeptides, as well as convergent methods for the synthesis thereof. The general preparative approach is exemplified by the synthesis of the mucin motif commonly found on epithelial tumor cell surfaces. The present invention further provides compositions and methods of treating cancer using the -O-linked glycoconjugates.

20200400769, Dec 24, 2020

Jun 19, 2019

16/445784

- Erlangen, DE
Benjamin L. Odry - West New York NJ, US
Xiao Chen - Princeton NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

G01R 33/565
G01R 33/56
G06T 11/00
G06T 7/246

For detecting motion in MR imaging, a regression model, such as a convolutional neural network, is machine trained. To generalize to MR imagers, MR contrasts, or other differences in MR image generation, the regression model is trained adversarially. The discriminator for adversarial training discriminates between classes of the variation source (e.g., type of MR imager or type of contrast) based on values of features learned in the regression model for detecting motion. By adversarial training, the regression model learns features that are less susceptible or invariant to variation in image source.

20190172230, Jun 6, 2019

Dec 6, 2017

15/832967

- Erlangen, DE
Benjamin L. Odry - West New York NJ, US
Xiao Chen - Princeton NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

G06T 11/00
G06N 3/08
G06N 3/04
G06T 5/00
G06T 5/20

Deep reinforcement machine learning is used to control denoising (e.g., image regularizer) in iterative reconstruction for MRI compressed sensing. Rather than requiring different machine-learnt networks for different scan settings (e.g., acceleration of the MR compressed sensing), reinforcement learning creates a policy of actions to provide denoising and data fitting through iterations of the reconstruction given a range of different scan settings. This allows a user to scan as appropriate for the patient, the MR system, the application, and/or preferences while still providing an optimized reconstruction under sampling resulting from the MR compressed sensing.