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Government-Owned Inventions; Availability for Licensing

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National Institutes of Health, Public Health Service, DHHS.




The inventions listed below are owned by an agency of the U.S. Government and are available for licensing in the U.S. in accordance with 35 U.S.C. 207 to achieve expeditious commercialization of results of federally-funded research and development. Foreign patent applications are filed on selected inventions to extend market coverage for companies and may also be available for licensing.


Licensing information and copies of the U.S. patent applications listed below may be obtained by writing to the indicated licensing contact at the Office of Technology Transfer, National Institutes of Health, 6011 Executive Boulevard, Suite 325, Rockville, Maryland 20852-3804; telephone: (301) 496-7057; fax: (301) 402-0220. A signed Confidential Disclosure Agreement will be required to receive copies of the patent applications.

Proteomic Toolkit for Protein Identification and Quantitation

David A. Lucas, Thomas P. Conrads, Timothy D. Veenstra (NCI/SAIC) DHHS Reference No. E-255-2004/0—Research Tool

Licensing Contact: Michael Shmilovich; (301) 435-5019;

A popular software package for the analysis of raw tandem mass spectrometry proteomic data is SEQUEST (from ThermoFinnigan, San Jose, CA), which coverts raw mass spectral data into peptide identifications (Peptide IDs). The large number of Peptide IDs generated by SEQUEST are contained in a single file and require further analysis using other software to identify relevant peptides. The SEQUEST software, however, cannot combine multiple Peptide ID files nor perform data mining.

The present software developed at the NIH and available for licensing, allows multiple Peptide ID files to be collated into a single file for analysis. Thus, one can analyze and mine the data from multiple proteomic experiments. The software provides tools that are not currently available in the management of mass spectrometry proteomic data. This software can be used to query the data asking relevant questions and provide a statistical component. The NIH software also interfaces directly with SEQUEST.

Software for Determining Features of an Anatomical Boundary Within a Digital Representation of Tissue

Jianhua Yao and Ronald Summers (NIHCC), U.S. Patent Application No. 10/779,210 filed 13 Feb 2004 (DHHS Reference No. E-351-2003/0-US-01), claiming priority to U.S. Provisional Application No. 60/510,640 filed 10 Oct 2003 (DHHS Reference No. E-174-2003/0-US-01).

Licensing Contact: Michael Shmilovich; (301) 435-5019; Available for licensing and commercial use and/or distribution is software for analyzing virtual anatomical structures and computing the enclosing three-dimensional boundaries. Various techniques can be used to determine tissue types in the virtual anatomical structure. For example, tissue types can be determined via an iso-boundary between lumen and air in the virtual anatomical structure and a fuzzy clustering approach. Based on the tissue type determination, a deformable model approach can be used to determine an enclosing three-dimensional boundary of a feature in the virtual anatomical structure (e.g., a colonic polyp). The software can be applied in a two-dimensional scenario, in which an enclosing two-dimensional boundary is first determined in a two-dimensional digital representation (for example, a slice of a three-dimensional representation) and then propagated to neighboring slices to result in an enclosing three-dimensional boundary of a feature. The software can also be applied in a three-dimensional scenario, in which an enclosing three-Start Printed Page 42752dimensional boundary of a feature is determined using three-dimensional techniques for tissue classification and converging via a deformable surface to avoid propagation.

Abciximab Pharmacodynamic Pattern Recognition

Mirna Urquidi-MacDonald (Penn State), Darrell Abernethy (NIA), U.S. Patent Application No. 10/810,809 filed 29 Mar 2004 (DHHS Reference No. E-319-2003/0-US-01).

Licensing Contact: Michael Shmilovich; (301) 435-5019,

Available for licensing and rapid implementation is a computerized neural network for predicting drug dosage and clinical outcome based on the use of data from drug dosage, drug effect and patient clinical characteristics. This network is especially suited to predict dosage and outcome for Abciximab. By establishing associated mapping, the neural network can predict a drug effect for a given patient characteristic and conversely predict drug dosing for a given drug effect and patient characteristic. The associative mapping is established and can be modulated by setting and adjusting weights of the connections between nodes in the neural network. The invention uses a feed-forward back-propagation neural network to model pharmacodynamic behavior and to predict drug dosage.

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Dated: July 6, 2004.

Steven M. Ferguson,

Director, Division of Technology Development and Transfer, Office of Technology Transfer, National Institutes of Health.

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[FR Doc. 04-16126 Filed 7-15-04; 8:45 am]