176 results about "Sub populations" patented technology

The invention provides methods for determining genome-wide sequence variations associated with a phenotype of a species in a hypothesis-free manner. In the methods of the invention, a set of restriction fragments for each of a sub-population of individuals having the phenotype are generated by digesting nucleic acids from the individual using one or more different restriction enzymes. A set of restriction sequence tags for the individual is then determined from the set of restriction fragments. The restriction sequence tags for the sub-population of organisms are compared and grouped into one or more groups, each of which comprising restriction sequence tags that comprise homologous sequences. The obtained one or more groups of restriction sequence tags identify the sequence variations associated with the phenotype. The methods of the invention can be used for, e.g., analysis of large numbers of sequence variants in many patient samples to identify subtle genetic risk factors.

The invention provides a method and system for aggregating and pooling information with feedback in a computer communication system. A communication system includes a server device and a set of client devices. Each client device collects information from an associated individual (whether by asking questions of those individuals, or accepting data input from peripheral devices), and transmits that data to a server device. The server device, or some other device at its behest, determines statistical information with regard to that data (such as aggregate, correlation, dispersion, or other measures), and provides that information to a communication channel for distribution to the individuals. The communication channel can include either (1) a broadcast communication channel that members of an affinity group can display, or (2) redistributing the determined statistical measures to associated individuals using the client devices. The statistical measure (such as an aggregate or sum) can be computed and distributed for the entire population, or can be computed and compared for selected sub-populations as a contest.

A microfluidic platform for single-cell capture, stimulation, and imaging. It passively traps 4,000 single cells on a 4.5 mm² footprint in 30 seconds, with a single-cell loading efficiency of 95%. The array format and optimized geometry allows for easy, robust and efficient single-cell loading, while maintaining captured cells in a low shear stress environment for long-term studies. Because cells are captured sequentially, the system is adequate for rare cell samples. Trapped cells can be exposed to various environmental conditions and chemical stimulus and their dynamic response can be monitored over time. The information gained from high-throughput, single-cell time lapsed imaging presents new opportunities in quantifying cellular responses, as averaged information by other measurement methods eliminates sub-population phenotypes.

A method for identifying a sub-population within a mixed population of cells is disclosed. The method involves contacting the mixed population of cells with at least one unique binding agent, wherein the at least one unique binding agent is designed to bind to a target molecule present in the sub-population, and wherein the at least one unique binding agent is attached to an epitope specific barcode that represents the identity of the target molecule. The method further involves sequentially attaching two or more assayable polymer subunits to the epitope specific barcode to create unique cell origination barcodes that represent the identities of individual cells to which the at least one unique binding agent has bound; and decoding the epitope specific barcode and cell origination barcodes, thereby identifying the sub-population within the mixed population of cells.

The invention discloses a job-shop scheduling method based on multi-population evolution mechanism, which pertains to the computer field and mainly solves the problem that the present job-shop scheduling method of multi-population genetic algorithm is easy to be trapped into local optimization and poor local search ability. The method comprises the steps of (1) setting parameters and initializing the population; (2) evaluating the chromosomes and the initial memory bank of the population, optimizing the initial memory bank by means of the simulated annealing algorithm; (3) judging whether the termination condition is satisfied, if so, outputting the currently obtained optimal scheduling plan; otherwise, continuing step 4; (4) carrying out the crossover and mutation operation on the chromosomes in each sub-population; (5) communicating the sub-population with the memory bank; and (6) updating the memory bank and optimizing the memory bank by means of the simulated annealing algorithm, and then returning to step (3). The job-shop scheduling method of the invention can obtain the job-shop scheduling plan with high quality, shorten the production time and can be used for selecting the job-shop scheduling plan.

A method and apparatus for determining the best fit of a population mixture model to data. In the digital imaging area, the use of histogram data is employed. A plurality of sub-population functions are defined and then optimized to fit the data. An objective function is employed, which is based upon the parameters of the underlying functions. The number of underlying functions is added to the parameter mix, such that no a priori knowledge of the number of sub-populations is required. In an illustrative embodiment, a genetic algorithm is used to evolve the objective function to an optimal fit of the data. Once an optimal fit is found, through comparison with stopping criteria in a fitness function, the data is segmented according to threshold determined based of classification error in the data.

The invention discloses a multi-objective power flow optimization method of a VSC-HVDC (voltage source converter-high voltage direct current) containing alternating-current/direct-current system, which is characterized by comprising the following steps: establishing a model, selecting to-be-optimized variables and optimization objectives, and determining constraint conditions; setting an initial variable, and giving a to-be-optimized variable range; under the condition of taking the to-be-optimized variables as population individuals in the optimization process, in a hybrid encoding mode, randomly generating an initial population according to the to-be-optimized variable range; calculating the power flow of the alternating-current/direct-current system by using an alternate iterating algorithm; getting the corresponding optimization objective function value of each individual in the population; carrying out rapid non-dominated sorting on the individuals in the population, calculating a virtual fitness, and through selection, crossing and variation processing, generating sub-populations; retaining excellent individuals in parent individuals by using an elitist strategy; judging whether algorithm termination conditions are satisfied, if so, turning to a step 9), and completing optimization; otherwise, turning to the step 4); completing the optimization process, and outputting results. The method has the advantages of scientificity, strong applicability, good effect, and the like.

The invention relates to a grid resource allocation method based on a parallel genetic algorithm. The method comprises the following steps: firstly, the information is initialized in a main thread, such as task collection, machine collection, an execution time matrix E of the task, and mapping of a sub-task to the machine, etc.; then a plurality of sub threads are generated and mapped to different processors, an initializing sub-population is independently generated by each sub thread, evolutionary computation is performed in parallel, the optimum individual of each generation is transferred to the main thread, the main thread performs comparison, and the optimum individual is retained; when the predetermined generation arrives, the transfer operation between the sub-groups is performed; and the operation of the main thread and all the sub-groups cannot be finished until the termination conditions are met. The genetic algorithm is taken as the most effective heuristic global stochastic searching method, and the solution of the NP problem can be performed. The quality and the speed for the algorithm for solving are improved by the parallel genetic algorithm proposed according to the natural parallelism of the genetic algorithm, and the method is an effective grid energy resource optimization method and favorable for improving the service quality of the grid.

In this invention is described a method that foresees the isolation of a new subpopulation of stem cells derived form dental pulp, whose differentiation is osteoblasts lead to the subsequent production and employment of a bone tissue, called LAB (Living Autologous Bone). Specifically, the invention describes: 1) the isolation of stem cells from the pulp of deciduous and permanent teeth and of dental germs, obtained from human subjects; 2) the growth of these cells in vitro, under specific conditions that allow the isolation of a cellular sub-population, which, after differentiation in osteoblasts, is able to produce in vitro an extracellular matrix, identical to that detectable in bone tissue; 3) the use of this selected and differentiated cell population in order to produce autologous bone tissue in vitro, containing vital osteoblasts; 4) the preservation of the LAB under conditions which guarantee cellular vitality; 5) the use of the LAB in donor patients to reconstruct bone tissue, as required in the daily practice in dentistry, maxillo-facial surgery and orthopedics.

A computer network-interfaced method for public health and health care provider active reach into diverse sub-population communities for the purpose of reducing health care treatment gaps of the population-at-large; comprising a network of computing means for health care provider administrators, a network-interfaced web software registration process requiring member user characteristics and health insurance information to parse members into sub-population communities; provider secure remote access control of network-interfaced computing means of sub-population community members during scheduled consultations, and production, broadcast and storage of protocol-driven life-episodes for actively screening member health care needs and improving social inclusion of sub-population community members.

A method of determining cell cycle phase data for cells comprising at least one luminescent reporter capable of emitting radiation, the at least one luminescent reporter comprising a first luminescent reporter which is capable of being indicative of at least one cell cycle phase, said method comprising: storing classification information for classifying individual cells into different cell cycle phases using an automated classification process; receiving image data created by detecting radiation emitted by said at least one luminescent reporter; analyzing said image data to identify object areas in the image data which correspond to individual cells; analyzing said image data, on the basis of said identified object areas, to determine, for a selected cell, one or more measurements including a measurement of a parameter relating to at least a cytoplasmic component of the cell; and applying said classification information to said measurements to classify the selected cell into a selected one of a plurality of sub-populations of cells, each sub-population having cells in a different cell cycle phase.

Methods of developing equations for objectively assigning or predicting an apparent age are disclosed. Advantageously, the equations of objective apparent age may be based on a relatively small number of critical parameters combined in a way that accounts for all or most of the apparent skin aging in a defined population or sub-population. Despite the relatively small data collection requirements, the present invention includes an objective model of apparent age that is useful for evaluation of products, useful for predicting treatment outcomes and useful for predicting the effects of deteriorative factors. The formalization of an Objective Apparent Age Score allows one to identify the biophysical and biochemical parameters that mostly influence an individual's apparent age, and can be used to select specific anti-aging treatments with increased chances of visible success. The Objective Apparent Age Score may also be used to support marketing claims.

The invention provides compositions and methods for characterizing breast cancer stem In particular, the invention provides for the identification of cells expressing Twist and CD44 that express little or virtually undetectable levels of CD24 (i.e. a Twist⁺/CD44⁺/CD24^−/low cell sub-population). The presence of such cells in a breast cancer specimen identifies the breast cancer as having increased metastic potential. Such cancers are identified as requiring aggressive therapies. Accordingly, the invention provides biomarkers suitable for identifying, diagnosing, and monitoring treatment of a subject with breast cancer.

The invention provides a self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling. The overall detection and local mining capability of the multi-object evolution method can be improved. The multi-object evolution method comprises the steps that S1, the evolution states of populations in the evolution process are detected according to the number of Pareto solutions and Pareto entropies, and corresponding individual evaluation strategy processing constraint conditions are self-adaptively utilized to sort individuals in the populations according to the detected evolution states of the populations in the evolution process, wherein a constraint violation processing method is adopted to process the constraint conditions in individual evaluation strategies; S2, according to individual sorting results, individuals are selected from the populations to perform genetic manipulation, and sub-populations are obtained, wherein genetic manipulation parameters are self-adaptively adjusted according to the evolution states of the populations in the evolution process during genetic manipulation. The self-adaptive multi-object evolution method is suitable for solving the multi-object evolution problem having constraints and can be applied to the technical field of workflow scheduling in a cloud computing environment.

A method of obtaining and analyzing a mass spectrum of a sample comprising components is characterized by: setting values of a first energy level and a second energy level; chromatographically separating the components; ionizing a portion of the separated components to create precursor ions; introducing a first portion of the precursor ions into a collision or reaction cell and generating a first sub-population of ions corresponding to the first energy level; introducing a second portion of the precursor ions into the cell and generating a second sub-population of ions corresponding to the second energy level; transferring a mixture of the first and second sub-populations of ions into a mass analyzer; producing an analysis of the ions of the mixture; varying the value of at least one of the first and the second energy levels according to a pre-determined cyclical variation; repeating various above steps; and analyzing the time-variation of the analyses.

The invention discloses a method for improving a standard shuffled frog leaping algorithm.The method comprises the steps of initializing parameters; calculating the adaptive value of each frog individual, and finding the adaptive value and position of the global optimum frog individual of a frog population; conducting optimum drawdown ranking on the frog population; conducting dividing for obtaining frog sub-populations; finding the positions of the optimum and the worst frog individual of each frog sub-population; conducting updating operation on the position of the worst frog individual of each frog sub-population; calculating the adaptive value of the frog individual with the position updated in each frog sub-population, and finding the global optimum adaptive value and the position of the frog population at this moment; implementing prediction of the global optimum adaptive value of the frog population obtained after iteration is completed next time, and furthermore adjusting the movement step-length variable coefficient dj and skip among steps; judging whether the ending conditions are met or not.By means of the method, the defects that at the later stage, the convergence rate of the standard shuffled frog leaping algorithm is severely lowered, convergence precision is insufficient, and the algorithm is prone to getting into local optimum are overcome.

The invention discloses a dynamic task perception-oriented cloud manufacturing collaborative task scheduling method, which comprises the following steps of firstly, constructing a collaborative task scheduling model according to the proportion of tasks completed by a supply chain; generating an initial population as a current population when a new task arrives according to an original manufacturing task and an optional manufacturing service, dividing the current population into a plurality of sub-populations for subsequent iteration, and representing each collaborative task scheduling scheme in the current population by using matrix coding; and finally, solving is performed through an improved multi-population biogeography optimization algorithm, and fusing a multi-population strategy, a local search strategy and a cooperation mechanism, so that the task scheduling problem solving performance of the algorithm is improved. According to the method, a better scheduling scheme can be obtained in the scheduling stage and the re-scheduling stage.

The invention discloses a resource scheduling method in a cloud computing system. According to the method, the position of an updating frog is computed by the aid of a leapfrog updating formula when each sub-population is locally searched, the fitness of the updating frog is computed to judge whether the fitness of the updating frog is superior to that of the worst frog or not, the position of the updating frog replaces that of the worst frog if the fitness of the updating frog is superior to that of the worst frog, the position of the optimal frog in the whole population replaces that of the worst frog if not, whether the fitness of the updated frog is superior to that of the worst frog or not is judged, the position of the updated frog replaces that of the worst frog if the fitness of the updated frog is superior to that of the worst frog, a new step length is generated by a double learning factor formula if not, the new step length is mutated according to mutation probability to obtain the position of the updated frog and replace the position of the worst frog, and the fitness of the updated frog is computed. The method can achieve good performances in optimal time span and load balance for task scheduling. The invention further discloses a resource scheduling system in the cloud computing system.