Abstract: The present disclosure generally relates to methods and systems for generating a void profile for user void events. The method includes receiving by a processing element a plurality of outputs from a fluid level sensor corresponding to a plurality of levels of fluid flowing through a voiding device during a flow event; determining by the processing element a beginning and an end time of the fluid flow into the voiding device during the flow event; analyzing the plurality of outputs of the fluid level sensor over a time interval defined by the beginning and end time of the fluid flow to determine at least one of a fluid flow rate data and an accumulated volume data for the fluid flowing through the voiding device; and storing the fluid flow rate data and the accumulated flow volume data in a memory.

Abstract: A method for manufacturing a container includes forming a top portion of a preform by injecting a first material into a mold. A bottom portion of the preform is formed by injecting a second material into the mold to form inner and outer layers of the bottom portion and injecting a third material into the mold to form an intermediate layer of the bottom portion that is positioned between the inner layer and the outer layer. The third material includes virgin polyethylene terephthalate and an additive.

Abstract: A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

Abstract: A method for manufacturing a container includes injecting a first material into a first mold to form a top portion of a preform. The first material and a second material are injected into the first mold to form a bottom portion of the preform. The preform is disposed in a second mold. The preform is blow molded into a finished container.

Abstract: A method for authenticated asset assessment is provided. The method involves executing a scan assistant on an asset to allow a remote scan engine to execute one or more scan operations on the asset for determining a state of the asset. The scan assistant may verify the identity of the scan engine by checking that a certificate received from the scan engine is signed with a private key associated with the scan engine. In some embodiments, the authentication may be performed as part of a TLS handshake process that establishes a TLS connection between the scan engine and the scan assistant. Once the scan engine is authenticated, the scan engine may communicate with the scan assistant according to a communication protocol to collect data about the asset. Advantageously, the disclosed technique reduces security risks associated with authenticated scans and improves the performance of authenticated scans.

Abstract: A detection system and method using multiple detector arrays for single or multi-frequency imaging of an object or scene, with one or more channels arranged along perpendicular axes. A polarizing beam element can be arranged to pass or reflect radiation to first and second detector blocks of the detection system.

Abstract: Provided herein are methods of ex vivo administration of a lipid particle or a payload gene, to a subject. In some embodiments, the methods are in-line methods of administration of a lipid particle or payload gene that are performed in a closed fluid circuit. Also provided are related compositions, containers, and systems in connection with the provided methods.

Abstract: The present invention provides novel amino chromen-2-one compounds that are inhibitors of mitochondrial RNA polymerase for treating various diseases such as cancer and others associated with metabolic disorders and mitochondrial dysfunction.

Abstract: The present disclosure is directed to a method for screening a signal peptide for efficient expression and secretion of a heterologous polypeptide in mammalian cells, the method comprising the steps of: providing a pool of viral expression vectors encoding a polypeptide of interest with various candidate signal peptides, wherein each viral expression vector of said pool comprises at least a polynucleotide encoding a fusion protein, said polynucleotide comprising: i) a promoter, ii) a sequence encoding a signal peptide, iii) a sequence encoding the polypeptide of interest, iv) optionally a sequence encoding an epitope tag, and v) a sequence encoding a C-terminal signal peptide for glycosylphosphatidylinositol, GPI, attachment or a sequence encoding a transmembrane domain; transforming host cells with said pool of viral vectors so that each host cell is preferably transformed on average by only one viral vector from said pool; expressing said fusion protein in said host cells in order to produce fusion proteins

Abstract: Disclosed are compounds according to Formula (1) which inhibit LONP1, and pharmaceutical compositions comprising compounds of the disclosure. Compounds and pharmaceutical compositions of the disclosure may be useful for the treatment of diseases and disorders associated with LONP1, including oncologic diseases and disorders, such as cancer, and diseases and disorders related to mitochondrial dysfunction, such as neurodegenerative disorders, metabolic disorders, and diseases associated with the aging process. The disclosure also relates to methods of using such compounds and compositions for the treatment of such diseases and disorders.

Abstract: Enhanced capabilities within a telephone network are made accessible to a telecommunications device. The telecommunications device is used to dial a sequence. A network server receives and identifies the sequence invokes an advanced network function. A advanced network function server in communication with the network server provides instructions as to how the network server is to provide the advanced network function. The network server processes the call in accordance with the received instructions to provide the particular advanced network function.

Abstract: Disclosed are compounds according to Formula (1) and Formula (2) which inhibit LONP1, and pharmaceutical compositions comprising compounds of the disclosure. Compounds and pharmaceutical compositions of the disclosure may be useful for the treatment of diseases and disorders associated with LONP1, including oncologic diseases and disorders, such as cancer, and diseases and disorders related to mitochondrial dysfunction, such as neurodegenerative disorders, metabolic disorders, and diseases associated with the aging process. The disclosure also relates to methods of using such compounds and compositions for the treatment of such diseases and disorders.

Abstract: Disclosed are compounds according to Formula (1) which inhibit LONP1, and pharmaceutical compositions comprising compounds of the disclosure. Compounds and pharmaceutical compositions of the disclosure may be useful for the treatment of diseases and disorders associated with LONP1, including oncologic diseases and disorders, such as cancer, and diseases and disorders related to mitochondrial dysfunction, such as neurodegenerative disorders, metabolic disorders, and diseases associated with the aging process. The disclosure also relates to methods of using such compounds and compositions for the treatment of such diseases and disorders.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: Described herein are avian follistatin products, and methods for producing such products, in which such products are effective for a variety of conditions, including increasing muscle mass. Avian follistatin products described herein are packaged as dietary supplements or nutritional supplements are useful in muscle regeneration.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.