HONG ZHAO
Nursing at Southwest Fwy, Sugar Land, TX

8560092, Oct 15, 2013

May 24, 2010

12/786052

Qingsheng Quinn Zheng - Sugar Land TX, US
Hong Zhao - Sugar Land TX, US

Aspen Technology, Inc. - Burlington MA

G06F 19/00

700 28, 700 52, 700128

Apparatuses and methods for model quality estimation and model adaptation in multivariable process control are disclosed. A method for updating a multiple input multiple output (MIMO) dynamical model of a process includes perturbing the process, auditing the controller model, identifying poor performing submodels and re-testing the relevant process variables, re-identifying submodels and adapting the model online while the process continues to operate within normal operating parameters. An apparatus comprises an online multivariable controller, a tester, a database to store data corresponding to manipulated variables and controlled variables, and a performance diagnosis module configured to identify problematic submodels and adapt a model used by the controller.

2012018, Jul 19, 2012

Sep 26, 2011

13/245706

Stephen T.C. Wong - Missouri City TX, US
Hong Zhao - Houston TX, US

The Methodist Hospital Research Institute - Houston TX

C40B 30/04, C40B 40/10, G01N 33/566, A61N 5/00, A61K 31/496, A61K 31/404, A61P 35/00, C40B 40/08, G01N 33/573

51425219, 506 17, 506 18, 435 71, 506 9, 514414, 600 1, 600 3

Disclosed are molecular diagnostic compositions and methods for predicting brain metastasis of breast cancer, as well as methods for drug repositioning to identify existing and new therapeutics for use in developing individualized, patient-specific treatment regimens for improving diagnoses and patient outcomes in individuals at risk for brain metastasis of breast cancer.

7065511, Jun 20, 2006

Oct 24, 2001

10/045668

Hong Zhao - Sugar Land TX, US
Guillermo Sentoni - Buenos Aires, AR
John P. Guiver - Saffron Walden, GB

Aspen Technology, Inc. - Cambridge MA

G06N 5/00

706 21, 700 29, 703 13

A non-linear dynamic predictive device () is disclosed which operates either in a configuration mode or in one of three runtime modes: prediction mode, horizon mode, or reverse horizon mode. An external device controller () sets the mode and determines the data source and the frequency of data. In the forward modes (prediction and horizon), the data are passed to a series of preprocessing units () which convert each input variable () from engineering units to normalized units. Each preprocessing unit feeds a delay unit () that time-aligns the input to take into account dead time effects. The output of each delay unit is passed to a dynamic filter unit (). Each dynamic filter unit internally utilizes one or more feedback paths that provide representations of the dynamic information in the process. The outputs () of the dynamic filter units are passed to a non-linear approximator () which outputs a value in normalized units. The output of the approximator is passed to a post-processing unit () that converts the output to engineering units.

2012029, Nov 22, 2012

Apr 4, 2012

13/439626

Stephen TC Wong - Houston TX, US
Hong Zhao - Houston TX, US
Guangxu Jin - Houston TX, US

The Methodist Hospital Research Institute - Houston TX

G06F 19/00, C07D 305/14, C07C 233/15, C07D 413/12

544119, 549510, 564218, 702 19

Disclosed are systems biology-based methods for repositioning known pharmaceutical compounds to new indications, through the identification of network-based signatures. In particular, the invention provides new and useful methods for selecting drugs or combinations of drugs (and preferably previously-approved drugs) for use in new therapeutic indications. Also disclosed are methods for identifying anti-breast tumor initiating cell (TIC)-based therapeutics from within populations of target compounds. In illustrative embodiments, the invention provides methods and computer programs for the repositioning of FDA-approved pharmaceutical compounds to new indications using network-based signature analysis coupled with conventional in vitro and in vivo testing of identified drug candidates. The invention also allows identification of drugs or drug combinations for treating unmet medical needs including, for example, “orphan” diseases.