140 results about "Affinity matrix" patented technology

A method for identifying nucleic acid ligands to target molecules using the SELEX procedure wherein the candidate nucleic acids contain photoreactive groups and nucleic acid ligands identified thereby are claimed. The complexes of increased affinity nucleic acids and target molecules formed in the procedure are crosslinked by irradiation to facilitate separation from unbound nucleic acids. In other methods partitioning of high and low affinity nucleic acids is facilitated by primer extension steps as shown in the figure in which chain termination nucleotides, digestion resistant nucleotides or nucleotides that allow retention of the cDNA product on an affinity matrix are differentially incorporated into the cDNA products of either the high or low affinity nucleic acids and the cDNA products are treated accordingly to amplification, enzymatic or chemical digestion or by contact with an affinity matrix.

Oligomeric compounds including oligoribonucleotides and oligoribonucleosides are provided that have subsequences of 2′-pentoribofuranosyl nucleosides that activate dsRNase. The oligoribonucleotides and oligoribonucleosides can include substituent groups for increasing binding affinity to complementary nucleic acid strand as well as substituent groups for increasing nuclease resistance. The oligomeric compounds are useful for diagnostics and other research purposes, for modulating the expression of a protein in organisms, and for the diagnosis, detection and treatment of other conditions susceptible to oligonucleotide therapeutics. Also included in the invention are mammalian ribonucleases, i.e., enzymes that degrade RNA, and substrates for such ribonucleases. Such a ribonuclease is referred to herein as a dsRNase, wherein “ds” indicates the RNase's specificity for certain double-stranded RNA substrates. The artificial substrates for the dsRNases described herein are useful in preparing affinity matrices for purifying mammalian ribonuclease as well as non-degradative RNA-binding proteins.

Oligomeric compounds including oligoribonucleotides and oligoribonucleosides are provided that have subsequences of 2′-pentoribofuranosyl nucleosides that activate dsRNase. The oligoribonucleotides and oligoribonucleosides can include substituent groups for increasing binding affinity to complementary nucleic acid strand as well as substituent groups for increasing nuclease resistance. The oligomeric compounds are useful for diagnostics and other research purposes, for modulating the expression of a protein in organisms, and for the diagnosis, detection and treatment of other conditions susceptible to oligonucleotide therapeutics. Also included in the invention are mammalian ribonucleases, i.e., enzymes that degrade RNA, and substrates for such ribonucleases. Such a ribonuclease is referred to herein as a dsRNase, wherein “ds” indicates the RNase's specificity for certain double-stranded RNA substrates. The artificial substrates for the dsRNases described herein are useful in preparing affinity matrices for purifying mammalian ribonuclease as well as non-degradative RNA-binding proteins.

A method detects events in multimedia. Features are extracted from the multimedia. The features are sampled using a sliding window to obtain samples. A context model is constructed for each sample. The context models form a time series. An affinity matrix is determined from the time series models and a commutative distance metric between each pair of context models. A second generalized eigenvector is determined for the affinity matrix, and the samples are then clustered into events according to the second generalized eigenvector.

A system and method for dynamic zoning are provided. Travel demand data is received for a network which includes a set of points. The travel demand data includes values representing demand from each point to each of other point. Destination-distance values are computed which reflect the similarity between points in a respective pair, based on the travel demand data. For each pair of the points, a geo-distance value is generated which reflects the distance between locations of the points in the pair. An aggregated affinity matrix is formed by aggregating the computed geo-distance values and destination-distance values. The aggregated affinity matrix is used by a clustering algorithm to assign each of the points in the set to a respective one of a set of clusters. A representation of the clusters can be generated in which each of a set of zones encompasses the points assigned to its respective cluster.

The invention provides a method for detecting usual events in a video. The events are detected by first constructing an aggregate affinity matrix from features of associated items extracted from the video. The affinity matrix is decomposed into eigenvectors, and the eigenvectors are used to reconstruct approximate estimates of the aggregate affinity matrix. Each matrix is clustered and scored, and the clustering that yields the highest scores is used to detect usual events.

A method detects events in multimedia. Features are extracted from the multimedia. The features are sampled using a sliding window to obtain samples. A context model is constructed for each sample. An affinity matrix is determined from the models and a commutative distance metric between each pair of context models. A second generation eigenvector is determined for the affinity matrix, and the samples are then clustered into events according to the second generation eigenvector.

The methods, apparatus and compositions disclosed herein concern the detection, identification and / or quantification of target cells and / or microorganisms in samples. The assays are based on light emission detected from a bioluminescence regenerative cycle (BRC). Light emission may be related to cell and / or microorganism number through the number of ATP and PPi molecules per cell or microorganism. In certain embodiments of the invention, specific target cells and / or microorganisms may be separated from samples using one or more capture molecules, such as antibodies. The cells and / or microorganisms may be lysed, the contents purified in whole or in part and the ATP and PPi contents determined by BRC. Other embodiments of the invention concern apparatus comprising a series of chambers connected by a monodirectional flow channel, each chamber comprising an affinity matrix with one or more binding moieties attached. In certain embodiments, a multiplex assay may be performed using both antibodies and oligonucleotide probes specific for a pathogen of interest.

Methods, systems and articles of manufacture for identifying semantic nearest neighbors in a feature space are described herein. A method embodiment includes generating an affinity matrix for objects in a given feature space, wherein the affinity matrix identifies the semantic similarity between each pair of objects in the feature space, training a multi-bit hash function using a greedy algorithm that increases the Hamming distance between dissimilar objects in the feature space while minimizing the Hamming distance between similar objects, and identifying semantic nearest neighbors for an object in a second feature space using the multi-bit hash function. A system embodiment includes a hash generator configured to generate the affinity matrix and train the multi-bit hash function, and a similarity determiner configured to identify semantic nearest neighbors for an object in a second feature space using the multi-bit hash function.

The invention discloses a data subspace clustering method based on multiple view angles, which comprises the steps of extracting multi-view-angle characteristics in a multi-view-angle database; for the multi-view-angle database, selecting a specific linear reconstruction expression method and determining a regularization constraint method corresponding to the linear reconstruction expression method; determining reconstruction error weight of each view angle characteristic in multi-view-angle characteristics; according to the selected reconstruction expression method and the obtained reconstruction error weights of different view angle characteristics, learning to obtain a linear expression matrix for reconstructing all samples in the multi-view-angle database, wherein the linear expression matrices are used for expressing a relationship among the samples in the database and element values are used for expressing reconstruction coefficients for corresponding samples in the line to reconstruct corresponding samples in the row; correspondingly processing the linear expression matrix to obtain an affinity matrix for measuring the similarity of the samples in the multi-view-angle database; and using a spectral clustering algorithm to partition the affinity matrix to obtain multi-view-angle data subspaces.

Endotoxin, also known as lipopolysaccharides (LPS), is the major mediator of septic shock due to Gram-negative bacterial infection. Chemically synthesized S3 peptide, derived from Sushi3 domain of Factor C, which is the endotoxin-sensitive serine protease of the limulus coagulation cascade, binds and neutralizes LPS activity. Fluorescent tagged-S3 is shown to detect LPS-containing bacteria. For large-scale production of S3 and to mimic other pathogen-recognizing molecules, tandem multimers of the S3 gene were constructed and expressed in E. coli. Tetramer of S3 for example is shown to display an enhanced inhibitory effect on LPS-induced activities. An affinity matrix based on tetramer of S3 is also shown to be particularly efficient at removing LPS.

A polymer affinity matrix for the binding of one or more substances in a fluid for removing said substance(s) from the fluid and / or decreasing the amount or concentration thereof in said fluid with a view to preventing, eliminating, or reducing undesired activation of components in said fluid is described, as well as a method for removing said substance(s) from the fluid and / or decreasing the amount or concentration thereof in said fluid, a method for the production of said matrix, use of said matrix and a kit comprising said matrix. The polymer affinity matrix comprises a solid support, a space and a ligand, containing arginine as a binding unit.

The invention discloses a synthetic aperture radar image target identification method based on multi-parameter spectrum feature, aiming at solving the low SAR image target identification rate problem of the current method. The method comprises the steps of carrying out pretreatment on the selected image of the known category information and the image to be tested to obtain a training set and a testing set; respectively calculating the scale parameters of all the training sample points and the testing sample points; respectively calculating the multi-parameter affinity matrix of the training set and the testing set by using the obtained scale parameters; respectively constructing Laplacian matrixes of the training set and the testing set with the multi-parameter affinity matrix; carrying out feature decomposition on the Laplacian matrix of the training set to obtain a corresponding projection matrix; respectively projecting the training sample and the testing sample to the space stretched by the projection matrix to obtain a new training set and a new testing set; inputting the new training set and the testing set into a support vector machine for category identification to obtain the category information of the tested image. The invention has the advantage of high identification rate and can be used for identifying SAR images.

A method for the separation of a target molecule from a mixture is described. The method employs oil bodies and their associated proteins as affinity matrices for the selective, non-covalent binding of desired target molecules. The oil body proteins may be genetically fused to a ligand having specificity for the desired target molecule. Native oil body proteins can also be used in conjunction with an oil body protein specific ligand such as an antibody or an oil body binding protein. The method allows the separation and recovery of the desired target molecules due to the difference in densities between oil bodies and aqueous solutions.

The invention discloses a search method for relevance feedback images based on an ant colony algorithm and a probability hypergraph, which comprises a training stage and a search stage. The training stage comprises the following steps of extracting low-level features of images, studying a dictionary and performing the high level representation of the images in a library image. The search stage comprises the following steps of extracting low-level features of sample images; performing the high level representation of the sample images; constructing an affinity matrix; initializing or updating a pheromone matrix; labeling positive correlation images and negative collation images for search results of all sample images in the image library; calculating a semantic pheromone matrix; calculating an affinity enhancing probability; enhancing the affinity matrix by using the ant colony algorithm; constructing a hypergraph; and returning the search result and finishing search, or updating the pheromone matrix for the next search. According to the method, an efficient and accurate image search technology is provided for image search and a higher use value is realized.

The invention relates, at least in part, to an affinity matrix library and the construction and use thereof. The library may be used, for example, for the enrichment of low-abundance proteins and depletion of abundant proteins in the search for biologically important proteins. The present invention also relates to a synthetic affinity matrix library comprising one or more ligand compounds with groups selected from amino, sulfhydryl, hydroxyl, carbonyl, and / or active hydrogen. The ligand compound may be attached to a base matrix.

The invention discloses a de-convolutional neural network-based scene semantic segmentation method. The method comprises the following steps of: S1, extracting intensive feature expression for a scene picture by using a full-convolutional neural network; and S2, carrying out up-sampling learning and object edge optimization on the intensive feature expression obtained in the step S1 through utilizing a locally sensitive de-convolutional neural network by means of a local affinity matrix of the picture, so as to obtain a score map of the picture and then realize refined scene semantic segmentation. Through the locally sensitive de-convolutional neural network, the sensitivity, to the local edge, of the full-convolutional neural network is strengthened by utilizing local bottom layer information, so that scene segmentation with higher precision is obtained.

A method of object recognition trains a convolutional neural network (CNN) with a set of training images, then classifies an image of an object using the trained CNN. A first layer of the CNN is trained by generating a set of first convolutional filters from eigenvectors produced from linear principal component analysis of patches of the training images. The training of each of multiple hidden layers CNN includes generating a set of convolutional filters from a selected subset of eigenvectors produced from linear principal component analysis of patches of an affinity matrix constructed using a set of prior convolutional filters from a prior layer of the CNN, where the affinity matrix represents correlations of feature vectors associated with the prior layer. The last layer of the CNN is trained with a regular classifier by error back-propagation using the training images and labels associated with the training images.

A method for acquiring and migrating website data, used for vertical e-commerce websites, migrating historical data information of existing e-commerce websites to other e-commerce websites through transfer learning, and collecting basic user information of existing e-commerce websites through scripts As well as user behavior, the user feature database is obtained from the basic user information, the product feature database is obtained from the product information of the existing e-commerce website, and the correlation between user features and product features is analyzed according to user behavior to obtain a correlation matrix. Other e-commerce According to the basic information of the user who enters the website, combined with the correlation matrix, the website calculates the correlation between the user and the products of the e-commerce website, and recommends them. The present invention learns the relevant patterns of user characteristics and product characteristics in the original system with dense historical data, and migrates them to other similar e-commerce websites that cannot be effectively recommended due to insufficient historical data based on the transfer learning method.

The invention discloses a method for purifying recombinant proteins with intein-mediated elastin like proteins (ELPs). In the method, a method which does not need any affinity matrix or label excision enzyme is developed by combining the temperature-dependent phase transfer characteristics of the ELPs and the label self-excision function of intein. The method is easy to operate, and has low cost and extremely good application prospect.

A method for the separation of a target molecule from a mixture is described. The method employs oil bodies and their associated proteins as affinity matrices for the selective, non-covalent binding of desired target molecules. The oil body proteins may be genetically fused to a ligand having specificity for the desired target molecule. Native oil body proteins can also be used in conjunction with an oil body protein specific ligand such as an antibody or an oil body binding protein. The method allows the separation and recovery of the desired target molecules due to the difference in densities between oil bodies and aqueous solutions.

The invention discloses a multi-channel spectral clustering method based on local density estimation and neighbor relation spreading. The multi-channel spectrum clustering method based on local density estimation and neighbor relation spreading mainly solves the problem that an existing clustering method cannot carry out clustering on data distributed unevenly in density. The multi-channel spectrum clustering method based on local density estimation and neighbor relation spreading comprises the steps that local density of a sample is estimated and is used as data characteristics and dimension lifting is carried out on original data; a distance matrix, a threshold value and a similarity matrix are calculated, and a neighbor relation matrix is initialized; the neighbor relation matrix and the similarity matrix are updated, and similarity of samples of a subset is updated by the adoption of a local maximum similar value, and an accurate affinity matrix is obtained; a similarity matrix and a normalized Laplacian matrix are calculated; a spectrum matrix is normalized, and a clustering result is obtained through the K-means algorithm. Compared with an existing clustering technology, the multi-channel spectrum method based on local density estimation and neighbor relation spreading enables a more real similarity matrix to be obtained, the clustering result is more accurate and the robustness is better.

Methods are provided for purification of Factor VIII polypeptides by affinity chromatography and ion exchange chromatography, in which the eluate from the affinity column is diluted with a solution comprising higher salt concentration, or lower non-polar agent concentration than that of the elution solution, prior to passing the diluted solution through the ion exchange column. The affinity matrix may comprise a monoclonal antibody or a peptide ligand. The methods result in improved purification without significant yield loss.

A method for hierarchical clustering comprises multi-level partitioning of data points into non-overlapping LDFs and formation of sets of such LDFs, wherein original data points may reside in different LDFs. The sets of LDFs are then hierarchically processed into sets of super-LDFs, which are further processed up the hierarchy. In some embodiments, the further processing ends with the construction of a single super-LDF at the root of the hierarchy. The partitioning of the data points and the hierarchical processing of the sets of LDFs and super-LDFs uses respective local affinity matrices.