Abstract: The invention provides particle compositions having applications in nucleic acid analysis. Nucleic acid polymer particles of the invention allow polynucleotides to be attached throughout their volumes for higher loading capacities than those achievable solely with surface attachment. In one aspect, nucleic acid polymer particles of the invention comprise polyacrylamide particles with uniform size distributions having low coefficients of variations, which result in reduced particle-to-particle variation in analytical assays. Such particle compositions are used in various amplification reactions to make amplicon libraries from nucleic acid fragment libraries.

Abstract: The invention provides a passive fluidics circuit for directing different fluids to a common volume, such as a reaction chamber or flow cell, without intermixing or cross contamination. The direction and rate of flow through junctions, nodes and passages of the fluidics circuit are controlled by the states of upstream valves (e.g. opened or closed), differential fluid pressures at circuit inlets or upstream reservoirs, flow path resistances, and the like. Free diffusion or leakage of fluids from unselected inlets into the common outlet or other inlets at junctions or nodes is prevented by the flow of the selected inlet fluid, a portion of which sweeps by the inlets of unselected fluids and exits the fluidics circuit by waste ports, thereby creating a barrier against undesired intermixing with the outlet flow through leakage or diffusion. The invention is particularly advantageous in apparatus for performing sensitive multistep reactions, such as pH-based DNA sequencing reactions.

Abstract: The invention is directed to apparatus and methods for delivering multiple reagents to, and monitoring, a plurality of analytical reactions carried out on a large-scale array of electronic sensors underminimal noise conditions. In one aspect, the invention provides method of improving signal-to-noise ratios of output signals from the electronic sensors sensing analytes or reaction byproducts by subtracting an average of output signals measured from neighboring sensors where analyte or reaction byproducts are absent. In other aspects, the invention provides an array of electronic sensors integrated with a microwell array for confining analytes and/or particles for analytical reactions and a method for identifying microwells containing analytes and/or particles by passing a sensor-active reagent over the array and correlating sensor response times to the presence or absence of analytes or particles.

Abstract: With a continuous source of data relating to transactions, the data may be segmented and processed in a data flow arrangement, optionally in parallel, and the data may be processed without storing the data in an intermediate database. Data from multiple sources may be processed in parallel. The segmentation also may define points at which aggregate outputs may be provided, and where checkpoints may be established.

Abstract: A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks.

Abstract: A database often contains sparse, i.e., under-represented, conditions which might be not represented in a training data set for training an analytical model if the training data set is created by stratified sampling. Sparse conditions may be represented in a training set by using a data set which includes essentially all of the data in a database, without stratified sampling. A series of samples, or “windows,” are used to select portions of the large data set for phases of training. In general, the first window of data should be a reasonably broad sample of the data. After the model is initially trained using a first window of data, subsequent windows are used to retrain the model. For some model types, the model is modified in order to provide it with some retention of training obtained using previous windows of data. Neural networks and Kohonen networks may be used without modification.

Abstract: A performance monitor represents execution of a data flow graph by changing performance information along different parts of a representation of that graph. If the graph is executed in parallel, the monitor can show parallel operator instances, associated datalinks, and performance information relevant to each. The individual parallel processes executing the graph send performance messages to the performance monitor, and the performance monitor can instruct such processes to vary the information they send. The monitor can provides 2D or 3D views in which the user can change focus, zoom and viewpoint. In 3D views, parallel instances of the same operator are grouped in a 2D array. The data rate of a datalink can be represented by both the density and velocity of line segments along the line which represent it. The line can be colored as a function of the datalink's source or destination, its data rate, or the integral thereof.

Abstract: A system provides an environment for parallel programming by providing a plurality of modular parallelizable operators stored in a computer readable memory. Each operator defines operation programming for performing an operation, one or more communication ports, each of which is either an input port for providing the operation programming a data stream of records, or an output port for receiving a data stream of records from the operation programming and an indication for each of the operator's input ports, if any, of a partitioning method to be applied to the data stream supplied to the input port.

Abstract: Checkpointing of operations on data may be provided by partitioning the data into temporal segments. Operations may be performed on the temporal segments and checkpoints may be established by storing a persistent indication of the segment being processed. The entire processing state need not be saved. If a failure occurs, processing can be restarted using the saved indication of the segment to be processed. Such data partitioning and checkpointing may be applied to relational databases, databases with dataflow operation and/or parallelism and other database types with or without parallel operation.

Abstract: The present invention provides a description of the behavior of a model that indicates the sensitivity of the model in subspaces of the input space and which indicates which dimensions of the input data are salient in subspaces of the input space. By implementing this description using a decision tree, the subspaces and their salient dimensions are both described and determined hierarchically. A sensitivity analysis is performed on the model to provide a sensitivity profile of the input space of the model according to sensitivity of outputs of the model to variations in data input to the model. The input space is divided into at least two subspaces according to the sensitivity profile. A sensitivity analysis is performed on the model to provide a sensitivity profile of each of the subspaces according to sensitivity of outputs of the model to variations in data input to the model.

Abstract: A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined by the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks.