Abstract: Disclosed is a use of a liposomal pharmaceutical preparation of mitoxantrone in the preparation of a medicament for treating lymphoma, wherein the lymphoma is preferably non-Hodgkin's lymphoma, further preferably aggressive non-Hodgkin's lymphoma, more preferably diffuse large B-cell lymphoma or peripheral T-cell lymphoma, and more further preferably relapsed or refractory diffuse large B-cell lymphoma or peripheral T-cell lymphoma. The mitoxantrone liposomes are used as single anti-tumor therapeutic agent without being combined with other anti-tumor agents.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.

Abstract: An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: The present disclosure discloses a method, device, apparatus, and storage medium for sleep monitoring. The method includes: monitoring physical sign information of a user in response to receiving a monitoring command; determining sleep quality information of the user according to the physical sign information of the user, wherein the physical sign information comprises at least breathing information.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from Supervisory Control and Data Acquisition (SCADA) system(s), retrieving patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receiving current sensor data from the sensors of the components, applying the selected model(s) to the current sensor data to generate a component failure prediction, comparing the component failure prediction to a threshold, and generating an alert and report based on the comparison to the threshold.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for enriching data. A computer receives a limited dataset and a target insight. In addition, the computer identifies an applicable richer dataset and determines a population relatedness between the limited dataset and the applicable richer dataset. Moreover, the computer calculates estimators of features using the richer dataset as well as calculates estimators for features using the limited dataset. The computer then updates the estimators of the limited dataset using estimators of the richer dataset and evaluates the updated estimators. Lastly, the computer provides a data collection recommendation as it relates to the limited dataset based on the evaluation.

Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving failure data, and asset data from SCADA system(s), receiving and dividing historical sensor data from sensors of components of wind turbines into different classes of different lead times, training a set of models to predict faults for each component using the historical sensor data and lead times with a deep neural network, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events from the SCADA data, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: Purified therapeutic nanoparticles are provided herein. Such nanoparticles comprise an active pharmaceutical ingredient and human serum albumin, wherein the weight ratio of human serum albumin to the active ingredient in the therapeutic nanoparticles is from 0.01:1 to 1:1, and wherein the nanoparticles are substantially free of free human serum albumin that is not incorporated in the nanoparticles. The present disclosure also provides pharmaceutical compositions that comprise the purified therapeutic nanoparticles and are also substantially free of free human serum albumin. Methods for preparing and using the purified therapeutic nanoparticles and compositions thereof are also provided.

Abstract: The present invention discloses a method for lowering an oil pipe in a gas well without well-killing, a soluble bridge plug and a material preparation method thereof, wherein, the method comprises the steps of: lowering a bridge plug in a wellbore such that the bridge plug blocks the wellbore at a predetermined location in the wellbore; injecting water in the wellbore after the pressure in the wellbore has been relieved so as to replace gases in the wellbore; and lowering an oil pipe in the wellbore to the location of the bridge plug. The method for lowering an oil pipe in a gas well without well-killing, the soluble bridge plug and the material preparation method thereof provided in the present invention successfully solve the problem of high cost for lowering an oil pipe under pressure after a fracturing fluid has been injected into the casing.

Abstract: Purified therapeutic nanoparticles are provided herein. Such nanoparticles comprise an active pharmaceutical ingredient and human serum albumin, wherein the weight ratio of human serum albumin to the active ingredient in the therapeutic nanoparticles is from 0.01:1 to 1:1, and wherein the nanoparticles are substantially free of free human serum albumin that is not incorporated in the nanoparticles. The present disclosure also provides pharmaceutical compositions that comprise the purified therapeutic nanoparticles and are also substantially free of free human serum albumin. Methods for preparing and using the purified therapeutic nanoparticles and compositions thereof are also provided.

Abstract: In one embodiment, a computer-implemented method includes transforming a plurality of electronic health records into a plurality of temporal graphs indicating an order in which events observed in the plurality of electronic health records occur and learning a temporal pattern from the plurality of temporal graphs, wherein the temporal pattern indicates an order of events that is observed to occur repeatedly across the plurality of temporal graphs.

Abstract: Embodiments of the present invention provide methods, computer program products, and a system for determining and providing health risk alerts. Embodiments of the present invention can be used to collect numbers entered by a user, which in turn are used to determine the health risks of the behavior of the user. Embodiments of the present invention can be used to issue alerts to a person of interest based, at least in part, on the number strings entered by the user.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for enriching data. A computer receives a limited dataset and a target insight. In addition, the computer identifies an applicable richer dataset and determines a population relatedness between the limited dataset and the applicable richer dataset. Moreover, the computer calculates estimators of features using the richer dataset as well as calculates estimators for features using the limited dataset. The computer then updates the estimators of the limited dataset using estimators of the richer dataset and evaluates the updated estimators. Lastly, the computer provides a data collection recommendation as it relates to the limited dataset based on the evaluation.

Abstract: Embodiments of the present invention provide methods, computer program products, and a system for determining and providing health risk alerts. Embodiments of the present invention can be used to collect numbers entered by a user, which in turn are used to determine the health risks of the behavior of the user. Embodiments of the present invention can be used to issue alerts to a person of interest based, at least in part, on the number strings entered by the user.