Abstract: A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a request for data sharding operations from a user device, determine a node comprising multiple network resources configured to create a response to the request, receive sharding feedback from the node, execute the machine learning algorithm to determine whether the performance of the network resources match a predefined performance threshold, generate a proposed sharding strategy to increase the network resources at the node in response to determining that the performance of the network resources does not match the predefined performance threshold, provide the proposed sharding strategy to the node for implementation, and generate multiple sharding commands configured to control routing of data between the first node and the user device in response to receiving the access command; and route the request to the node using the sharding commands.

Abstract: A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a request for data sharding operations from a user device, determine a node comprising multiple network resources configured to create a response to the request, receive sharding feedback from the node, execute the machine learning algorithm to determine whether the performance of the network resources match a predefined performance threshold, generate a proposed sharding strategy to increase the network resources at the node in response to determining that the performance of the network resources does not match the predefined performance threshold, provide the proposed sharding strategy to the node for implementation, and generate multiple sharding commands configured to control routing of data between the first node and the user device in response to receiving the access command; and route the request to the node using the sharding commands.

Abstract: A method may include receiving, using a processing unit, a request to calculate an estimated duration of an agricultural job, the request including an agricultural job identifier, issuing, using the processing unit, a data retrieval command using the agricultural job identifier, receiving field characteristics associated with a field, the field characteristics including a field shape value and an area of the field, encoding, using the processing unit, the field characteristics into a data structure configured to be input into a job duration machine learning model, executing, using the processing unit, the job duration machine learning model using the data structure, after executing the job duration machine learning model, accessing an output of the job duration machine learning model, the output associated with the estimated duration of the agricultural job, and transmitting, using the processing unit, the estimated duration and the job identifier to an agricultural job scheduling system.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, generating an image representation from the raw mass spectrometry data, selecting a portion of the signals corresponding to the image representation, inputting the selected portion into a machine learning model to determine or infer an existence or an absence of signals within respective retention time windows, obtaining a retention time window within which a subset of the signals exist, determining whether to expand the retention time window, determining or receiving an indication of a retention time window within which a subset of the signals are located, and determining whether to expand the retention time window. The systems and methods may selectively expand the retention time window based on the determination, and retrieve information within the expanded retention time window or the retention time window.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining true signals from the raw mass spectrometry data, determining intensities corresponding to the true signals, adjusting the determined intensities, and based on the adjusted intensities, determining concentrations of one or more constituents corresponding to the true signals.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining true signals from the raw mass spectrometry data, determining intensities corresponding to the true signals, adjusting the determined intensities, and based on the adjusted intensities, determining concentrations of one or more constituents corresponding to the true signals.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, generating an image representation from the raw mass spectrometry data, selecting a portion of the signals corresponding to the image representation, inputting the selected portion into a machine learning model to determine or infer an existence or an absence of signals within respective retention time windows, obtaining a retention time window within which a subset of the signals exist, determining whether to expand the retention time window, determining or receiving an indication of a retention time window within which a subset of the signals are located, and determining whether to expand the retention time window. The systems and methods may selectively expand the retention time window based on the determination, and retrieve information within the expanded retention time window or the retention time window. The image representation indicates intensities of signals from the samples.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, extracting a subset of the raw mass spectrometry data based on storage characteristics of the computing system, storing the subset of the raw mass spectrometry data within a storage of the computing system, and transmitting the stored subset to a machine learning component.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, and separating the raw mass spectrometry data into discrete intervals in each of the samples. At each interval of the discrete intervals of the raw mass spectrometry data, a local highest intensity signal, relative to any other signal within each interval, is determined, and a frequency of occurrence of each local highest intensity signal across the samples is determined. A subset of local highest intensity signals is retrieved based on respective frequencies of occurrence of the local highest intensity signals. The subset of the local highest intensity signals is ingested into a machine learning model.

Abstract: A method of treating a pediatric cancerous solid tumor in a mammalian subject is disclosed that comprises intralesionally administering an amount of a halogenated xanthene or a pharmaceutically acceptable salt thereof, preferably Rose Bengal disodium, that elicits ablation of tumor cells of the administered tumor. Another contemplated method comprises the steps of intralesionally administering an amount of a halogenated xanthene or a pharmaceutically acceptable salt thereof, preferably Rose Bengal disodium, that elicits ablation of tumor cells of the administered tumor and systemically administering a tumor-inhibiting effective amount of a systemic anti-cancer medication that provides synergistic cytotoxicity with the halogenated xanthene. The two administrations can occur concurrently, or one prior to the other.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, determining a bin value to apply to the filtered mass spectrometry data, and after determining the bin value, generating an image-based representation of the raw mass spectrometry data, wherein the image-based representation indicates frequencies of peak intensities in each bin.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, extracting a subset of the raw mass spectrometry data based on storage characteristics of the computing system, storing the subset of the raw mass spectrometry data within a storage of the computing system, and transmitting the stored subset to a machine learning component.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, and separating the raw mass spectrometry data into discrete intervals in each of the samples. At each interval of the discrete intervals of the raw mass spectrometry data, a local highest intensity signal, relative to any other signal within each interval, is determined, and a frequency of occurrence of each local highest intensity signal across the samples is determined. A subset of local highest intensity signals is retrieved based on respective frequencies of occurrence of the local highest intensity signals. The subset of the local highest intensity signals is ingested into a machine learning model.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples and generating an image representation of the mass spectrometry data. The image representation illustrates frequencies of occurrence of respective signals across the samples. A portion of the mass spectrometry data is obtained or extracted based on the frequencies. This portion is divided into segments. The segments correspond to different ranges or bins of retention times and mass-to-charge ratios. Veracities of signals within the segments are determined using a machine learning model. A subset of the segments that correspond to true signal is extracted. Mass-to-charge ratios and retention times corresponding to the subset of the segments are extracted.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, determining a bin value to apply to the filtered mass spectrometry data, and after determining the bin value, generating an image-based representation of the raw mass spectrometry data, wherein the image-based representation indicates frequencies of peak intensities in each bin.

Abstract: Systems and methods for machine learning model retraining is described. In one example, a system includes a computing device that is configured to determine an anomaly score from a first input of a featurized data set for a machine learning model of a deployment environment. The computing device is configured to determine a feature correlation score for the machine learning model based at least in part on a second input of a featurized historical data set for the machine learning model. A model retraining frequency time period for the machine learning model is determined based at least in part on the feature correlation score and the anomaly score.

Abstract: The present disclosure relates generally to systems and methods for identifying and qualifying microbial species, such as those isolated from a human individual. Once the individual is identified as be deficient in certain microbial species, care can be given accordingly to increase the microbial species and thus improve health of the individual.

Abstract: Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, determining a bin value to apply to the filtered mass spectrometry data, and after determining the bin value, generating an image-based representation of the raw mass spectrometry data, wherein the image-based representation indicates frequencies of peak intensities in each bin.

Abstract: Systems and methods for generating digital content and selecting targets for presentation of digital content using artificial intelligence are disclosed. In one example, a nanosegment of customers can be selected as targets for digital content based on their propensity to accept offers for a given campaign. By incorporating a nanosegment-based classification of customers, there can be significantly reduced memory usage by the system, since the customer base will be reduced to a fewer number of groups, and each group more specifically targets traits for one cluster of customers. Attributes of the selected customers can be used to automatically design and generate digital content, such as personalized offers for distribution to the customers, including AI-generated taglines, content, and images. Feedback from each cycle of the campaign can be fed back into subsequent cycles to continuously improve performance and offer outcomes.

Abstract: A method of treating a mammalian subject having hematologic, non-tumorous cancer cells is disclosed. The method comprises the steps of: (A) administering to such a mammalian subject a therapeutically effective amount of a halogenated xanthene, a pharmaceutically acceptable salt or a C1-C4 alkyl ester thereof as a first cancer cytotoxic agent dissolved or dispersed in a pharmaceutically acceptable aqueous medium. The mammalian subject is maintained for a period of time sufficient to induce death of hematologic, non-tumorous cancer cells. A contemplated administration is typically repeated. A contemplated treatment method can also be carried out in conjunction with administration to said mammalian subject of a second therapeutically effective amount of a second, differently-acting cancer cytotoxic agent dissolved or dispersed in a pharmaceutically acceptable medium. The second cancer cytotoxic agent can be a small molecule or an intact antibody or paratope-containing portion thereof.