Abstract: One embodiment includes a method for limiting an eligible population for a randomized controlled trial. The method generates panel data for a plurality of digital subjects. The panel data for a given digital subject includes a pre-trial characteristic corresponding to the given digital subject, to be tracked in a virtual RCT. The method derives a preliminary estimate for inclusion criteria used in the virtual RCT, wherein the preliminary estimate includes an upper boundary and a lower boundary on the pre-trial characteristic. The method combines an inclusion function and an interest function to create a cost function. The inclusion function approximates the preliminary estimate for the inclusion criteria as soft constraints. The interest function maps a conditional distribution of potential values to an interest quantity. The method updates the preliminary estimate to derive an updated estimate for the inclusion criteria by optimizing the cost function with respect to the preliminary estimate.

Abstract: Modular active surface devices for microfluidic systems and methods of making same is disclosed. In one example, the modular active surface device includes an active surface layer mounted atop an active surface substrate, a mask mounted atop the active surface layer wherein the mask defines the area, height, and volume of the reaction chamber, and a substrate mounted atop the mask wherein the substrate provides the facing surface to the active surface layer. In other examples, both facing surfaces of the reaction chamber include active surface layers. Further, the modular active surface device can include other layers, such as, but not limited to, adhesive layers, stiffening layers for facilitating handling, and peel-off sealing layers. Further, a large-scale manufacturing method is provided of mass producing the modular active surface devices. Further, a method is provided of using a plasma bonding process to bond the active surface layer to the active surface substrate.

Abstract: Systems and methods for Bayesian PROCOVA operations are illustrated. One embodiment includes a method for updating predictive models. The method trains a set of one or more generative models based on RCT data. The method defines a mixture prior distribution that includes: an informative component that follows an informative prior distribution defined, at least in part, on the RCT data; and a flat component that follows a flat prior distribution defined independently of the RCT data. The method generates, using the set of one or more generative models, predicted panel data for a plurality of digital subjects. The method derives a mixture posterior distribution corresponding to the unknown parameters of the set of one or more generative models, based on the predicted panel data. The method determines, based on at least one of the predicted panel data or the mixture posterior distribution, a set of one or more decision rules.

Abstract: A method for a redox flow battery system may include flowing an electrolyte from an electrolyte storage tank to a multi-stage rebalancing reactor, the multi-stage rebalancing reactor comprising reactor vessels grouped to form stages. Hydrogen gas may be injected into the electrolyte upstream of the multi-stage rebalancing reactor via a gas line and a metal ion of the electrolyte may be chemically reduced by oxidizing the hydrogen gas at a catalyst bed of each of the reactor vessels to maintain a charge balance of the electrolyte and a pH of the electrolyte within a predetermined range.

Abstract: Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model depends, at least in part, on target trial data. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.

Abstract: Systems and techniques for time-series forecasting are illustrated. One embodiment includes a method for refining time-series forecasts, the method obtains timestep information including baseline information, a time gap, and context information. The baseline information includes information known about the system at a time when the multivariate time-series is generated. The context information includes at least one vector of time-independent background variables related to the system. The method determines, based on the timestep information, parameter predictions for the system at a first timestep and a second timestep. The method derives actual state values for the system at the first timestep. The method updates the parameter predictions for the system at the second timestep, using a gating function, based on a discrepancy between: the parameter predictions for the system at the first timestep, and the actual state values for the system at the first timestep.

Abstract: A cell processing system, fluidics cartridge, and methods for using actuated surface-attached posts for processing cells are disclosed. Particularly, the cell processing system includes a fluidics cartridge and a control instrument. The fluidics cartridge includes a cell processing chamber that has a micropost array therein, a sample reservoir and a wash reservoir that supply the cell processing chamber, and a waste reservoir and an eluent reservoir at the output of the cell processing chamber. A micropost actuation mechanism and a cell counting mechanism are provided in close proximity to the cell processing chamber. A method is provided of using the cell processing system to collect, wash, and recover cells. Another method is provided of using the cell processing system to collect, wash, count, and recover cells at a predetermined cell density.

Abstract: Systems and methods for determining treatment effects of a randomized control trial (RCT) in accordance with embodiments of the invention are illustrated. One embodiment includes a method for determining treatment effects. The method includes steps for receiving data from a RCT, generating result data using a set of one or more generative models, and determining treatment effects for the RCT using the generated result data.

Abstract: Systems and methods for generating and utilizing artificial intelligence generated badges can include processing web information associated with a subject to determine particular qualities of the subject. The qualities can then be utilized to generate one or more badges. The badges can then be utilized for search result determination and display. The badges may be utilized for search result ranking and may be utilized to annotate search results in a search results interface.

Abstract: A flow cell is provided that includes surface-attached structures in a chamber. The structures are movable in response to an actuation force. The flow cell may be utilized to extract or isolate nucleic acids from a sample flowing through the flow cell, wherein some portion of the flow cell comprises nucleic acid adsorbant material (e.g. the outer surface of the surface-attached structures, an inside surface of the chamber of the flow cell, beads attached to the outer surface of the surface-attached structures, or beads integrated into the outer surface of the surface-attached structures). Further, systems and methods for extraction of nucleic acids using such flow cells are also provided.

Abstract: One embodiment includes a method for predicting the progression of a current state. The method obtains input information concerning time-series forecasts of a state of an entity. The input information includes baseline information known about the state of the entity at a start time; and context information that includes a vector of time-independent background variables related to the entity. The method determines a first forecast for the entity at a first timestep that is separated from the start time by a time gap. The first forecast is determined, by a point prediction model, based on the baseline information and the context information. The method derives, from an autoregressive function, a mean parameter for a probabilistic function. The mean parameter is derived based on: the first forecast; and a learnable function trained based on the time gap and context information. The method parameterizes the probabilistic function based on the mean parameter.

Abstract: Systems and methods for determining treatment effects of a randomized control trial (RCT) in accordance with embodiments of the invention are illustrated. One embodiment includes a method for determining treatment effects. The method includes steps for receiving data from a RCT, generating result data using a set of one or more generative models, and determining treatment effects for the RCT using the generated result data.

Abstract: Systems and techniques for time-series forecasting are illustrated. One embodiment includes a method for refining time-series forecasts, the method obtains timestep information including baseline information, a time gap, and context information. The baseline information includes information known about the system at a time when the multivariate time-series is generated. The context information includes at least one vector of time-independent background variables related to the system. The method determines, based on the timestep information, parameter predictions for the system at a first timestep and a second timestep. The method derives actual state values for the system at the first timestep. The method updates the parameter predictions for the system at the second timestep, using a gating function, based on a discrepancy between: the parameter predictions for the system at the first timestep, and the actual state values for the system at the first timestep.

Abstract: Systems and techniques for adjusting experiment parameters are illustrated. One embodiment includes a method that defines a joint distribution, wherein the joint distribution corresponds to a combination of a probabilistic model and a point prediction model, and wherein the point prediction model is configured to obtain a measurement of regression accuracy. The method derives an energy function for the joint distribution. The method obtains, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation. The method determines, from a loss function, at least one training parameter. The method trains the probabilistic based on the at least one parameter to operate as a conditional generative model, wherein the trained probabilistic model follows the conditional distribution. The method applies the trained probabilistic model to a dataset corresponding to a randomized trial.

Abstract: Systems and techniques for adjusting experiment parameters are illustrated. One embodiment includes a method that defines a joint distribution, wherein the joint distribution corresponds to a combination of a probabilistic model and a point prediction model, and wherein the point prediction model is configured to obtain a measurement of regression accuracy. The method derives an energy function for the joint distribution. The method obtains, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation. The method determines, from a loss function, at least one training parameter. The method trains the probabilistic based on the at least one parameter to operate as a conditional generative model, wherein the trained probabilistic model follows the conditional distribution. The method applies the trained probabilistic model to a dataset corresponding to a randomized trial.

Abstract: Systems and methods for training and utilizing predictive models that ignore missing features in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for, at a single layer in a multi-layer model, receiving an input includes a set of one or more values for several features and identifying a missingness pattern of the input, wherein the missingness pattern indicates whether the set of values is missing a value for each of the several features. The method further includes determining a set of one or more transformation weights based on the missingness pattern and transforming the input based on the determined transformation weights.

Abstract: One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for receiving an input includes a set of one or more values for several features, wherein the set of values for at least one of the several features includes values for each of several points in time, and for identifying a missingness pattern of the input, wherein the missingness pattern for the at least one feature indicates whether the set of values is missing a value for each of the several points in time. The method further includes steps for determining a set of one or more transformation weights based on the missingness pattern, and transforming the input based on the determined transformation weights.

Abstract: The invention provides a system and methods of multiplexed, solid-phase isothermal nucleic acid amplification. In various aspects, the invention uses a microfluidic device that includes a field of actuatable microposts in a reaction (or assay) chamber to enhance fluid flow, mixing, and hybridization/capture efficiency in a solid-phase capture assay. In various other aspects, the invention uses oligonucleotide primers immobilized in a field of actuatable microposts in a reaction chamber of a microfluidics device for capture and amplification of target-specific nucleic acids in a sample fluid. The invention provides methods of producing a micropost field (array) on a substrate for printing of a capture array (e.g., an array of primer spots). The invention also provides methods of printing an array of capture spots (e.g., primer spots) on the substrate surface of a micropost field.

Abstract: Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model depends, at least in part, on target trial data. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.

Abstract: Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model is performed independently of data that will be applied to a target trial. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.