Abstract: A method for encoding computer processes for malicious program detection.

Abstract: A method for encoding computer processes for malicious program detection.

Abstract: A system and method for using business services within a customer relationship management (CRM) application. Business services are objects that encapsulate and simplify the use of some set of functionality. The CRM application includes an object manager that manages business objects as well as business services. Business objects are associated with data stored in the application database. Business services operate or act upon business objects to achieve a particular goal. Business services include methods that can be invoked and properties that maintain information about the state of a process during execution.

Abstract: Systems and methods for software development in which the development of a base product proceeds concurrently with the internationalization and localization of the base product to produce multiple language versions of the product for polylingual simultaneous shipment to customers wherein one or more of the multiple languages can be uploaded by the customer in a single installation process.

Abstract: A method, system, and computer-readable medium is described for creating a prototype GUI for conversion into an actual GUI whose appearance when displayed matches the appearance of the prototype GUI. In some situations, multiple personnel may participate in the process, such as a non-technical GUI designer that specifies the prototype GUI appearance information and a technical GUI developer that specifies various non-appearance information for a corresponding actual GUI. A prototype GUI is first created based on layout and other appearance information specified for various GUI elements, and corresponding proxy GUI objects are generated that store appearance information but not various non-appearance information used as part of an actual GUI. Proxy GUI objects for a prototype GUI can then be converted into actual GUI objects for use with an actual GUI, and various non-appearance information can be specified for the actual GUI objects in various ways.

Abstract: High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

Abstract: An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Abstract: Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

Abstract: High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

Abstract: An automatic peak selection method for multidimensional data can efficiently select peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Abstract: High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

Abstract: Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

Abstract: High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

Abstract: An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Abstract: Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

Abstract: High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

Abstract: A method, system, and computer-readable medium is described for creating a prototype GUI for conversion into an actual GUI whose appearance when displayed matches the appearance of the prototype GUI. In some situations, multiple personnel may participate in the process, such as a non-technical GUI designer that specifies the prototype GUI appearance information and a technical GUI developer that specifies various non-appearance information for a corresponding actual GUI. A prototype GUI is first created based on layout and other appearance information specified for various GUI elements, and corresponding proxy GUI objects are generated that store appearance information but not various non-appearance information used as part of an actual GUI. Proxy GUI objects for a prototype GUI can then be converted into actual GUI objects for use with an actual GUI, and various non-appearance information can be specified for the actual GUI objects in various ways.

Abstract: Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

Abstract: A component list extraction method improves the quality of data extracted from a series of spectra, images, or other data sets, resulting in more accurate analysis and data mining. A series of spectra, such as mass spectra, are obtained and thresholded to distinguish peaks from noise. Conventionally, all data below the noise threshold are recorded as having zero intensity, which introduces an artificial discontinuity in the data. Instead, a composite peak list is constructed containing peaks occurring in at least a minimum number of spectra, and intensity values are recorded for corresponding peak locations in all spectra, even those having intensities below the noise threshold. The resulting intensities serve as inputs to a data mining or analysis method. The method can also be used as a peak detection method to determine components characterizing a sample type or patient population. The method is particularly useful for biological marker discovery and image processing.

Abstract: An automatic peak selection method for multidimensional data can efficiently select peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.