Abstract: Mass spectrometer based analytical systems and methods in which a feedback control system can be utilized to control the flow of liquid within a sampling probe to adjust and/or maintain the surface profile (e.g., shape) of the liquid-air interface within an open sampling port of the sampling probe. The feedback control systems can automatically monitor and/or detect the surface profile of the liquid-air interface and adjust the flow rate of the sampling liquid to ensure that experimental conditions remain consistent at the time of sample introduction during serial samplings. These can provide stable and reproducible analyte flows of consistent dilution to the ion source, increasing reproducibility and/or accuracy of data generated by MS analysis. Can be used with a change in the desired set point according to the particular experimental workflow (e.g., automated adjustment between an interface corresponding to a sampling set point and a cleaning set point).

Abstract: Methods and systems for delivering liquid sample to an ion source and subsequent analysis by mass spectrometry. In accordance with various aspects of the present teachings, MS-based systems and methods are provided in which desorption solvent is used in sampling interface to desorb analyte species from an SPME device that is coupled to an ion source to ionize analyte species desorbed into the desorption solvent for MS analysis (e.g., without a liquid chromatography (LC) column between the sampling interface and the ion source). In various aspects of the methods and systems described herein, configuring the sampling interface can be optimized so as to reduce the fluid volume dead space about the fluid inlet so as to concentrate the one or more analyte species desorbed at optimized conditions from the SPME substrate in a decreased volume of the desorption solvent when the SPME device is inserted into sampling interface.

Abstract: Electromagnetic systems and corresponding methods for assembling the electromagnetic systems are described. The electromagnetic systems can be used in fluid processing systems that include a plurality of fluid containers, each configured to define a fluid chamber that receives a fluid and a plurality of magnetic particles, and a plurality of electromagnets configured to generate a magnetic field within at least one of the plurality of the fluid containers. The fluid processing system can also include a PCB board that supplies the electromagnets with electrical current by establishing an electrical connection between electrical contact terminals included on the PCB board and spring loaded connections included on each electromagnet.

Abstract: A system and method are provided for loading a sample into an analytical instrument using acoustic droplet ejection (“ADE”) in combination with a continuous flow sampling probe. An acoustic droplet ejector is used to eject small droplets of a fluid sample containing an analyte into the sampling tip of a continuous flow sampling probe, where the acoustically ejected droplet combines with a continuous, circulating flow stream of solvent within the flow probe. Fluid circulation within the probe transports the sample through a sample transport capillary to an outlet that directs the analyte away from the probe to an analytical instrument, e.g., a device that detects the presence, concentration quantity, and/or identity of the analyte. When the analytical instrument is a mass spectrometer or other type of device requiring the analyte to be in ionized form, the exiting droplets pass through an ionization region, e.g.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to behavioral changes in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series in the set; for each similarity measurement, assigning weights to the candidate time series based on similarity values; generating, for each candidate time series, a similarity score based on the assigned weights; automatically identifying, based on the similarity scores, a candidate time series as contributing to an anomaly exhibited in the primary time series; and outputting identifying information of the at least one identified candidate time series for use in one or more automated actions associated with the storage system.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of resources in contention in storage systems using machine learning techniques are provided herein.

Abstract: A system and method are provided for loading a sample into an analytical instrument using acoustic droplet ejection (“ADE”) in combination with a continuous flow sampling probe. An acoustic droplet ejector is used to eject small droplets of a fluid sample containing an analyte into the sampling tip of a continuous flow sampling probe, where the acoustically ejected droplet combines with a continuous, circulating flow stream of solvent within the flow probe. Fluid circulation within the probe transports the sample through a sample transport capillary to an outlet that directs the analyte away from the probe to an analytical instrument, e.g., a device that detects the presence, concentration quantity, and/or identity of the analyte. When the analytical instrument is a mass spectrometer or other type of device requiring the analyte to be in ionized form, the exiting droplets pass through an ionization region, e.g.

Abstract: Methods and systems for delivering liquid sample to an ion source and subsequent analysis by mass spectrometry. In accordance with various aspects of the present teachings, MS-based systems and methods are provided in which desorption solvent is used in sampling interface to desorb analyte species from an SPME device that is coupled to an ion source to ionize analyte species desorbed into the desorption solvent for MS analysis (e.g., without a liquid chromatography (LC) column between the sampling interface and the ion source). In various aspects of the methods and systems described herein, configuring the sampling interface can be optimized so as to reduce the fluid volume dead space about the fluid inlet so as to concentrate the one or more analyte species desorbed at optimized conditions from the SPME substrate in a decreased volume of the desorption solvent when the SPME device is inserted into sampling interface.

Abstract: Integrated system for delivering sample to a mass spectrometer, which includes a chamber extending from a top to bottom end, an open port probe disposed in the chamber such that an open end of the probe, which is configured for receiving a sample, is positioned in proximity to top end of the chamber. The system can further include a solvent inlet port coupled to said chamber for receiving a solvent and directing said solvent to said probe, and a solvent outlet port for receiving a flow of the solvent from the open port probe and directing the received solvent out of the chamber. The system can also include an adapter for receiving a sample holder having an outlet port, the adapter is releasable and replaceable and couple with chamber to align the outlet port of sample holder with open end of probe for delivering sample to the probe.

Abstract: Methods and systems for delivering a liquid sample to an ion source for the generation of ions and subsequent analysis by mass spectrometry are provided herein. In accordance with various aspects of the present teachings, MS-based systems and methods are provided in which the flow of desorption solvent within a sampling probe fluidly coupled to an ion source can be selectively controlled such that one or more analyte species can be desorbed from a sample substrate inserted within the sampling probe within a decreased volume of desorption solvent for subsequently delivery to the ion source. In various aspects, sensitivity can be increased due to higher desorption efficiency (e.g., due to increased desorption time) and/or decreased dilution of the desorbed analytes.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to system performance degradation are provided herein. An example computer-implemented method includes obtaining, in connection with a system exhibiting performance degradation, a primary time series and a set of multiple candidate time series; calculating, using machine learning, similarity measurements between the primary time series and each time series in the set; for each measurement, assigning weights to the time series based on similarity to the primary time series relative to the other time series in the set; generating, for each time series in the set, a similarity score based on the weights assigned across the similarity measurements; and outputting, based on the similarity scores, identification of a candidate time series for use in automated actions.

Abstract: Methods and systems for delivering a liquid sample to an ion source for the generation of ions and subsequent analysis by mass spectrometry are provided herein. In accordance with various aspects of the present teachings, MS-based systems and methods are provided in which the flow of desorption solvent within a sampling probe fluidly coupled to an ion source can be selectively controlled such that one or more analyte species can be desorbed from a sample substrate inserted within the sampling probe within a decreased volume of desorption solvent for subsequently delivery to the ion source. In various aspects, sensitivity can be increased due to higher desorption efficiency (e.g., due to increased desorption time) and/or decreased dilution of the desorbed analytes.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to behavioral changes in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series in the set; for each similarity measurement, assigning weights to the candidate time series based on similarity values; generating, for each candidate time series, a similarity score based on the assigned weights; automatically identifying, based on the similarity scores, a candidate time series as contributing to an anomaly exhibited in the primary time series; and outputting identifying information of the at least one identified candidate time series for use in one or more automated actions associated with the storage system.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of resources in contention in storage systems using machine learning techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to system performance degradation are provided herein. An example computer-implemented method includes obtaining, in connection with a system exhibiting performance degradation, a primary time series and a set of multiple candidate time series; calculating, using machine learning, similarity measurements between the primary time series and each time series in the set; for each measurement, assigning weights to the time series based on similarity to the primary time series relative to the other time series in the set; generating, for each time series in the set, a similarity score based on the weights assigned across the similarity measurements; and outputting, based on the similarity scores, identification of a candidate time series for use in automated actions.

Abstract: Methods, apparatus, and processor-readable storage media for determining similarity between time series using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of multiple candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each of the candidate time series; for each of the similarity measurements, assigning weights to the candidate time series based on similarity to the primary time series relative to the other candidate time series; generating, for each of the candidate time series, a similarity score based on the weights assigned to each of the candidate time series across the similarity measurements; and outputting, based on the similarity scores, identification of at least one candidate time series for use in one or more automated actions relating to at least one system.

Abstract: A system and method are provided for loading a sample into an analytical instrument using acoustic droplet ejection (“ADE”) in combination with a continuous flow sampling probe. An acoustic droplet ejector is used to eject small droplets of a fluid sample containing an analyte into the sampling tip of a continuous flow sampling probe, where the acoustically ejected droplet combines with a continuous, circulating flow stream of solvent within the flow probe. Fluid circulation within the probe transports the sample through a sample transport capillary to an outlet that directs the analyte away from the probe to an analytical instrument, e.g., a device that detects the presence, concentration quantity, and/or identity of the analyte. When the analytical instrument is a mass spectrometer or other type of device requiring the analyte to be in ionized form, the exiting droplets pass through an ionization region, e.g.

Abstract: A method includes obtaining, in a given log processing node, at least two different types of logs associated with assets of an enterprise system. The method also includes generating, at the given log processing node, frequency scores for terms in unstructured log data of each of the different log types, the generated frequency score for a given term in unstructured log data of a given log type being based on (i) occurrence of the given term in historical logs of the given log type previously processed by log processing nodes and (ii) occurrence of the given term in the obtained logs of the given log type. The method further includes extracting, at the given log processing node, features from the obtained logs based on the frequency scores, detecting events affecting the assets utilizing the extracted features, and modifying a configuration of the assets responsive to detecting the events.

Abstract: A method includes obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems, and generating a plurality of model-specific storage resource capacity predictions utilizing the historical storage resource utilization data and respective ones of a plurality of time series capacity prediction forecasting models. The method also includes selecting a subset of the model-specific storage resource capacity predictions having one or more designated characteristics, determining an overall storage resource capacity prediction based at least in part on a combination of the selected subset of the model-specific storage resource capacity predictions, and modifying a provisioning of storage resources of the one or more storage systems based at least in part on the overall storage resource capacity prediction.

Abstract: Methods, apparatus, and processor-readable storage media for machine learning-based anomaly detection using time series decomposition are provided herein. An example computer-implemented method includes processing, via machine learning techniques pertaining to time series decomposition functions, a first set of historical time series data derived from multiple systems within an enterprise; generating, based on the processed data, one or more pairs of upper bounds and lower bounds directed to system metrics; identifying system anomalies attributed to one or more of the multiple systems within the enterprise by comparing a second set of historical time series data derived from the one or more systems against the one or more pairs of upper bounds and lower bounds; prioritizing, via machine learning techniques pertaining to weighting functions, the system anomalies; and outputting, in accordance with the prioritization, the system anomalies to a user within the enterprise.