Yuan Chen

US Patent:

6797265, Sep 28, 2004

Filed:

Oct 5, 2001

Appl. No.:

09/972794

Inventors:

Andrea Amalfitano - Durham NC
Yuan Tsong Chen - Chapel Hill NC
Huimin Hu - Memphis TN
Bradley Lowell Hodges - Milford MA

Assignee:

Duke University - Durham NC

International Classification:

A61K 4800

US Classification:

424 932, 435 691, 4353201, 435325

Abstract:

The present invention provides deleted adenovirus vectors. The inventive adenovirus vectors carry one or more deletions in the IVa2, 100K, polymerase and/or preterminal protein sequences of the adenovirus genome. The adenoviruses may additionally contain other deletions, mutations or other modifications as well. In particular preferred embodiments, the adenovirus genome is multiply deleted, i. e. , carries two or more deletions therein. The deleted adenoviruses of the invention are âpropagation-defectiveâ in that the virus cannot replicate and produce new virions in the absence of complementing function(s). Preferred adenovirus vectors of the invention carry a heterologous nucleotide sequence encoding a protein or peptide associated with a metabolic disorder, more preferably a protein or peptide associated with a lysosomal or glycogen storage disease, most preferably, a lysosomal acid -glucosidase. Further provided are methods for producing the inventive deleted adenovirus vectors. Further provided are methods of administering the deleted adenovirus vectors to a cell in vitro or in vivo.

US Patent:

7287014, Oct 23, 2007

Filed:

Nov 15, 2002

Appl. No.:

10/294773

Inventors:

Yuan Yan Chen - Woodbridge VA, US
Joseph Chen - Woodbridge VA, US

International Classification:

G06E 1/00
G06E 3/00
G06F 15/18
G06G 7/00

US Classification:

706 16

Abstract:

A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.

US Patent:

7666405, Feb 23, 2010

Filed:

Sep 7, 2004

Appl. No.:

10/935576

Inventors:

Andrea Amalfitano - Durham NC, US
Yuan Tsong Chen - Chapel Hill NC, US
Huimin Hu - Memphis TN, US
Bradley Lowell Hodges - Milford MA, US

Assignee:

Duke University - Durham NC

International Classification:

A61K 48/00
C12N 15/00
A01N 43/04

US Classification:

424 932, 4353201, 514 44 R

Abstract:

The present invention provides deleted adenovirus vectors. The inventive adenovirus vectors carry one or more deletions in the IVa2, 100K, polymerase and/or preterminal protein sequences of the adenovirus genome. The adenoviruses may additionally contain other deletions, mutations or other modifications as well. In particular preferred embodiments, the adenovirus genome is multiply deleted, i. e. , carries two or more deletions therein. The deleted adenoviruses of the invention are “propagation-defective” in that the virus cannot replicate and produce new virions in the absence of complementing function(s). Preferred adenovirus vectors of the invention carry a heterologous nucleotide sequence encoding a protein or peptide associated with a metabolic disorder, more preferably a protein or peptide associated with a lysosomal or glycogen storage disease, most preferably, a lysosomal acid α-glucosidase. Further provided are methods for producing the inventive deleted adenovirus vectors. Further provided are methods of administering the deleted adenovirus vectors to a cell in vitro or in vivo.

US Patent:

20020010691, Jan 24, 2002

Filed:

Mar 15, 2001

Appl. No.:

09/808101

Inventors:

Yuan Chen - Woodbridge VA, US

International Classification:

G06F015/18

US Classification:

706/020000

Abstract:

An apparatus and method for performing parallel distributed processing are disclosed. A plurality of nodes are connected with weight connections. The weight connections are updated based on a likelihood function of the associated nodes. Also, inputs to nodes are aggregated using t-norm or t-conorm functions, with outputs representing the possibility and belief measures. The aggregation methods presented offer an improvement over many other classification methods. Because of the form of the output, additional data evidence, including additional attributes, may be taken into account to improve classification without retraining the original data.

US Patent:

20020102737, Aug 1, 2002

Filed:

Jun 6, 2001

Appl. No.:

09/875327

Inventors:

David Millington - Chapel Hill NC, US
Yan An - Chapel Hill NC, US
Yuan Chen - Chapel Hill NC, US
Robert Stevens - Durham NC, US
Sarah Young - Durham NC, US
Johan Van Hove - Mol, BE

International Classification:

G01N033/487

US Classification:

436/094000, 436/173000

Abstract:

Provided are methods of screening subjects for lysosomal storage diseases, preferably glycogen storage diseases, using a tetrasaccharide as a biomarker. In a more preferred embodiment, subjects are screened for Pompe disease (i.e., glycogen storage disease type II). Also provided are neonatal screening assays. The present invention further provides methods of monitoring the clinical condition and efficacy of therapeutic treatment in affected subjects. Further provided are methods of measuring a tetrasaccharide biomarker by tandem mass spectrometry, preferably, as part of a neonatal screening assay for Pompe disease.

US Patent:

6328958, Dec 11, 2001

Filed:

Aug 27, 1999

Appl. No.:

9/384749

Inventors:

Andrea Amalfitano - Durham NC
Yuan Tsong Chen - Chapel Hill NC
Huimin Hu - Memphis TN

Assignee:

Duke University - Durham NC

International Classification:

A61K 4800
C12N 1588

US Classification:

424 932

Abstract:

The present invention provides deleted adenovirus vectors. The inventive adenovirus vectors carry one or more deletions in the IVa2, 100K, polymerase and/or preterminal protein sequences of the adenovirus genome. The adenoviruses may additionally contain other deletions, mutations or other modifications as well. In particular preferred embodiments, the adenovirus genome is multiply deleted, i. e. , carries two or more deletions therein. The deleted adenoviruses of the invention are "propagation-defective" in that the virus cannot replicate and produce new virions in the absence of complementing function(s). Preferred adenovirus vectors of the invention carry a heterologous nucleotide sequence encoding a protein or peptide associated with a metabolic disorder, more preferably a protein or peptide associated with a lysosomal or glycogen storage disease, most preferably, a lysosomal acid. alpha. -glucosidase. Further provided are methods for producing the inventive deleted adenovirus vectors.

US Patent:

20220365509, Nov 17, 2022

Filed:

May 23, 2022

Appl. No.:

17/750549

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06F 30/00
G06N 20/20
G06N 5/04

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

US Patent:

20200348646, Nov 5, 2020

Filed:

Jul 14, 2020

Appl. No.:

16/928499

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06F 30/00
G06N 5/04

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.