Abstract: The invention comprises a method and apparatus for estimating state of a cardiovascular system, comprising a cardiac stroke volume analyzer, comprising: (1) a blood pressure sensor generating a time-varying pressure state waveform output from a limb of the person; (2) a system processor connected to the blood pressure sensor; and (3) a dynamic state-space model; the system processor receiving cardiovascular input data, from the blood pressure sensor, related to a transient pressure state of the cardiovascular system; at least one probabilistic model, of the dynamic state-space model, operating on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system; iteratively updating the probability distribution function using output from the blood pressure sensor; and processing the probability distribution function to generate a non-pressure state output related to stroke volume of a heart of the person and arterial compliance

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. In one example, a digital signal processor is integrated into a biomedical device. The processor is configured to: use a dynamic state-space model configured with a physiological model of a body system to provide a prior probability distribution function; receive sensor data input from at least two data sources; and iteratively use a probabilistic updater to integrate the sensor data as a fused data set and generate a posterior probability distribution function using all of: (1) the fused data set; (2) an application of Bayesian probability; and (3) the prior probability distribution function. The processor further generates an output of a biomedical state using the posterior probability function.

Abstract: The invention comprises a method and apparatus for estimating state of a cardiovascular system, comprising a cardiac stroke volume analyzer, comprising: (1) a blood pressure sensor generating a time-varying pressure state waveform output from a limb of the person; (2) a system processor connected to the blood pressure sensor; and (3) a dynamic state-space model; the system processor receiving cardiovascular input data, from the blood pressure sensor, related to a transient pressure state of the cardiovascular system; at least one probabilistic model, of the dynamic state-space model, operating on the time-varying pressure state waveform output to generate a probability distribution function to a non-pressure state of the cardiovascular system; iteratively updating the probability distribution function using output from the blood pressure sensor; and processing the probability distribution function to generate a non-pressure state output related to stroke volume of a heart of the person and arterial compliance

Abstract: A method for non-invasively measuring cardiac output, stroke volume, or both comprises collecting plethysmographic waveform data of a patient, providing the plethysmographic waveform to a data processor, and calculating measured values for SV, CO, or both. Software of the data processor comprises a mathematical model of the cardiovascular system integrated in a dynamic state space model (DSSM).

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. In one example, an analyzer is configured to: receive discrete first and second input data, related to a first and second sub-system of the system, from a first and second instrument, respectively. A system processor is used to fuse the first and second input data into fused data. The system processor optionally includes: (1) a probabilistic processor configured to convert the fused data into at least two probability distribution functions and (2) a dynamic state-space model, the dynamic state-space model including at least one probabilistic model configured to operate on the at least two probability distribution functions. The system processor iteratively circulates the at least two probability distribution functions in the dynamic state-space model in synchronization with receipt of updated input data, processes the probability distribution functions, and generates an output related to the state of the system.

Abstract: A system for non-invasively measuring cardiac output, stroke volume, or both comprises a pulse oximeter, a data processor, and means for generating an output reporting measured one or more CO or SV values to a user. A method for non-invasively measuring cardiac output, stroke volume, or both comprises collecting plethysmographic waveform data of a patient, providing the plethysmographic waveform to a data processor, and calculating measured values for CO or SV. The data processor comprises a mathematical model of the cardiovascular system integrated in a dynamic state space model (DSSM).

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. In one example, a digital signal processor is integrated into a biomedical device. The processor is configured to: use a dynamic state-space model configured with a physiological model of a body system to provide a prior probability distribution function; receive sensor data input from at least two data sources; and iteratively use a probabilistic updater to integrate the sensor data as a fused data set and generate a posterior probability distribution function using all of: (1) the fused data set; (2) an application of Bayesian probability; and (3) the prior probability distribution function. The processor further generates an output of a biomedical state using the posterior probability function.

Abstract: Without limitation, the disclosure provides processes for (a) dissolving biomass in ionic liquids, (b) deconstructing cellulose, hemicellulose and/or lignin into derivatives including fermentable sugars, (c) separating the biomass derivatives from the ionic liquid, and (d) converting the biomass derivatives to useful fuels or chemicals, either dissolved within or separated from the ionic liquid. It should be understood that processes described herein can be used in isolation or in combination with each other.

Abstract: A system for non-invasively measuring cardiac output, stroke volume, or both comprises a pulse oximeter, a data processor, and means for generating an output reporting measured one or more CO or SV values to a user. A method for non-invasively measuring cardiac output, stroke volume, or both comprises collecting plethysmographic waveform data of a patient, providing the plethysmographic waveform to a data processor, and calculating measured values for CO or SV. The data processor comprises a mathematical model of the cardiovascular system integrated in a dynamic state space model (DSSM).

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. In one example, an analyzer is configured to: receive discrete first and second input data, related to a first and second sub-system of the system, from a first and second instrument, respectively. A system processor is used to fuse the first and second input data into fused data. The system processor optionally includes: (1) a probabilistic processor configured to convert the fused data into at least two probability distribution functions and (2) a dynamic state-space model, the dynamic state-space model including at least one probabilistic model configured to operate on the at least two probability distribution functions. The system processor iteratively circulates the at least two probability distribution functions in the dynamic state-space model in synchronization with receipt of updated input data, processes the probability distribution functions, and generates an output related to the state of the system.

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. In one example, an analyzer is configured to: receive discrete first and second input data, related to a first and second sub-system of the system, from a first and second instrument, respectively. A system processor is used to fuse the first and second input data into fused data. The system processor optionally includes: (1) a probabilistic processor configured to convert the fused data into at least two probability distribution functions and (2) a dynamic state-space model, the dynamic state-space model including at least one probabilistic model configured to operate on the at least two probability distribution functions. The system processor iteratively circulates the at least two probability distribution functions in the dynamic state-space model in synchronization with receipt of updated input data, processes the probability distribution functions, and generates an output related to the state of the system.

Abstract: A probabilistic digital signal processor for medical function is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm. For example, the probabilistic processor operates using a physical model on data from a medical meter, where the medical meter uses a first physical parameter, such as blood oxygen saturation levels from a pulse oximeter, to generate a second physical parameter not output by the medical meter, such as a heart stroke volume, a cardiac output flow rate, and/or a blood pressure.

Abstract: Without limitation, the disclosure provides processes for (a) dissolving biomass in ionic liquids, (b) deconstructing cellulose, hemicellulose and/or lignin into derivatives including fermentable sugars, (c) separating the biomass derivatives from the ionic liquid, and (d) converting the biomass derivatives to useful fuels or chemicals, either dissolved within or separated from the ionic liquid. It should be understood that processes described herein can be used in isolation or in combination with each other.

Abstract: A probabilistic data signal processor used to determine health of a system is described. Initial probability distribution functions are input to a dynamic state-space model, which iteratively operates on probability distribution functions, such as state and model probability distribution functions, to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled and used as an input to the dynamic state-space model in the iterative algorithm. In various embodiments, the probabilistic data signal processor is used to filter output from any mechanical device using appropriate physical models, which optionally include chemical, electrical, optical, mechanical, or fluid based models.

Abstract: A pulse oximeter system comprises a data processor configured to perform a method that combines a sigma point Kalman filter (SPKF) or sequential Monte Carlo (SMC) algorithm with Bayesian statistics and a mathematical model comprising a cardiovascular model and a plethysmography model to remove contaminating noise and artifacts from the pulse oximeter sensor output and measure blood oxygen saturation, heart rate, left-ventricular stroke volume, aortic pressure and systemic pressures.

Abstract: In one aspect, provided herein are efficient methods for using ionic liquids. In some embodiments, only a small amount of ionic liquid is lost in a chemical process. For example, described herein is a method for separating one or more biomass components from an ionic liquid comprising contacting a composition comprising an ionic liquid and a biomass component with a fluid.

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and/or enhanced parameter information.

Abstract: A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and/or enhanced parameter information.

Abstract: The invention comprises a slide preparation apparatus and method of operation therefor for aiding in microscope slide preparation. The slide preparation apparatus includes an inclined plane surface configured to support a microscope slide assembly at an angle that ergonomically facilitates delivery of a fluid sample to an upper edge of a cover slip of the microscope slide assembly, which allows gravity to aid fluid delivery a microscope slide. Preferably, the slide preparation apparatus is heated and/or temperature controlled to reduce viscosity of the sampled fluid, which enables the fluid to flow between the microscope slide and the cover slip.

Abstract: A probabilistic digital signal processor for medical function is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm. For example, the probabilistic processor operates using a physical model on data from a medical meter, where the medical meter uses a first physical parameter, such as blood oxygen saturation levels from a pulse oximeter, to generate a second physical parameter not output by the medical meter, such as a heart stroke volume, a cardiac output flow rate, and/or a blood pressure.