Abstract: The first objective comparison of automated and human segmentation of magnetic resonance images, or MRI, using a blinded controlled assessment study. Computers connected over a network divide duties including computerized segmenting of the images, manual segmenting of the images, comparison of the computer segmented images and the manually segmented images, and scoring of the images for accuracy. The scores are evaluated to update configuration parameters of a neural network.

Abstract: A surface mountable camera (1000) includes a junction box (104) and clip (300) that provide for a plurality of selectable mounting positions, each accommodating a hanging camera position. The junction box (104) provides a four-sided interior compartment, wherein at least three side walls of the compartment each include a slot (112) and an anchor feature (114) corresponding to the slot. The clip (300) is configured to interchangeably insert onto any of the three side walls of the junction box. The clip (300) includes an access port (304) formed therein for engaging with the anchor feature (114) of the junction box (104), and the clip includes first and second engagement tabs (302) for engaging into the slot (112) of the junction box (104). Once the clip (300) is anchored to the junction box (104), a camera assembly (200) is pivotably mounted, via a hinged bracket (400), to the clip of the junction box.

Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

Abstract: An improved vaporizer, system, and method for managing concentrate usage is disclosed. The vaporizer may comprise a housing to receive a cartridge configured to store a concentrate. The cartridge comprises a nozzle, at one end, with a smart chip for storing an identification code associated with the concentrate. The vaporizer, cartridge, system and method provides means for assuring accurate dosing and management of concentrate use and usage data collection.

Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

Abstract: A system comprising a vaporizing device and a central server is described. The vaporizing device includes a housing and a cartridge, received within the housing, the cartridge including a predefined quantity of concentrate and an identification code associated with the concentrate and uniquely identifying the cartridge. The vaporizing device further includes a control unit configured to read the identification code. The vaporizing device further includes a communication unit configured to transmit the identification code to a computing device of a user. The central server includes a database. The central server is configured to receive the identification code from the computing device and retrieve concentrate information corresponding to the identification code from the database. The central server is configured to transmit the concentrate information to the computing device for displaying to the user.

Abstract: A system comprising a vaporizing device and a central server is described. The vaporizing device includes a housing and a cartridge, received within the housing, the cartridge including a predefined quantity of concentrate and an identification code associated with the concentrate and uniquely identifying the cartridge. The vaporizing device further includes a control unit configured to read the identification code. The vaporizing device further includes a communication unit configured to transmit the identification code to a computing device of a user. The central server includes a database. The central server is configured to receive the identification code from the computing device and retrieve concentrate information corresponding to the identification code from the database. The central server is configured to transmit the concentrate information to the computing device for displaying to the user.

Abstract: A system comprising a vaporizing device and a central server is described. The vaporizing device includes a housing and a cartridge, received within the housing, the cartridge including a predefined quantity of concentrate and an identification code associated with the concentrate and uniquely identifying the cartridge. The vaporizing device further includes a control unit configured to read the identification code. The vaporizing device further includes a communication unit configured to transmit the identification code to a computing device of a user. The central server includes a database. The central server is configured to receive the identification code from the computing device and retrieve concentrate information corresponding to the identification code from the database. The central server is configured to transmit the concentrate information to the computing device for displaying to the user.

Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

Abstract: The first objective comparison of automated and human segmentation of magnetic resonance images, or MRI, using a blinded controlled assessment study. Computers connected over a network divide duties including computerized segmenting of the images, manual segmenting of the images, comparison of the computer segmented images and the manually segmented images, and scoring of the images for accuracy. The scores are evaluated to update configuration parameters of a neural network.

Abstract: A filament container for housing a spool of filament comprising a base and a lid that define an inner volume. The lid rotatably attached to the base and moveable between a closed position, wherein the inner volume is substantially sealed, and an open position, wherein the inner volume is accessible. First and second horizontal shafts are supported rotatably within the inner volume, parallel to and spaced from one another a distance, and rotatable about a respective longitudinal axis. The first and second horizontal shafts are adapted to support a spool of filament vertically within the inner volume and to allow the spool of filament to freely rotate about a horizontal axis that is parallel to and spaced from the longitudinal axis of each of the first and second horizontal shafts as filament is pulled from the spool and out of the filament container.

Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

Abstract: A filament container for housing a spool of filament comprising a base and a lid that define an inner volume. The lid rotatably attached to the base and moveable between a closed position, wherein the inner volume is substantially sealed, and an open position, wherein the inner volume is accessible. First and second horizontal shafts are supported rotatably within the inner volume, parallel to and spaced from one another a distance, and rotatable about a respective longitudinal axis. The first and second horizontal shafts are adapted to support a spool of filament vertically within the inner volume and to allow the spool of filament to freely rotate about a horizontal axis that is parallel to and spaced from the longitudinal axis of each of the first and second horizontal shafts as filament is pulled from the spool and out of the filament container.

Abstract: A system comprising a vaporizing device and a central server is described. The vaporizing device includes a housing and a cartridge, received within the housing, the cartridge including a predefined quantity of concentrate and an identification code associated with the concentrate and uniquely identifying the cartridge. The vaporizing device further includes a control unit configured to read the identification code. The vaporizing device further includes a communication unit configured to transmit the identification code to a computing device of a user. The central server includes a database. The central server is configured to receive the identification code from the computing device and retrieve concentrate information corresponding to the identification code from the database. The central server is configured to transmit the concentrate information to the computing device for displaying to the user.

Abstract: A system comprising a vaporizing device and a central server is described. The vaporizing device includes a housing and a cartridge, received within the housing, the cartridge including a predefined quantity of concentrate and an identification code associated with the concentrate and uniquely identifying the cartridge. The vaporizing device further includes a control unit configured to read the identification code. The vaporizing device further includes a communication unit configured to transmit the identification code to a computing device of a user. The central server includes a database. The central server is configured to receive the identification code from the computing device and retrieve concentrate information corresponding to the identification code from the database. The central server is configured to transmit the concentrate information to the computing device for displaying to the user.

Abstract: An improved vaporizer, system, and method for managing concentrate usage is disclosed. The vaporizer may comprise a housing to receive a cartridge configured to store a concentrate. The cartridge comprises a nozzle, at one end, with a smart chip for storing an identification code associated with the concentrate. The vaporizer, cartridge, system and method provides means for assuring accurate dosing and management of concentrate use and usage data collection.

Abstract: A system and method for characterizing tissues of a subject using multi-parametric imaging are provided. In some aspects, the method includes receiving a set of multi-parametric magnetic resonance (“MR”) images acquired from a subject using an MR imaging system, and selecting at least one region of interest (“ROI”) in the subject using one or more images in the set of multi-parametric MR images. The method also includes performing a texture analysis on corresponding ROIs in the set of multi-parametric MR images to generate a set of texture features, and applying a classification scheme, using the set of texture features, to characterize tissues in the ROI. The method further includes generating a report indicative of characterized tissues in the ROI.

Abstract: A system comprising a vaporizing device and a central server is described. The vaporizing device includes a housing and a cartridge, received within the housing, the cartridge including a predefined quantity of concentrate and an identification code associated with the concentrate and uniquely identifying the cartridge. The vaporizing device further includes a control unit configured to read the identification code. The vaporizing device further includes a communication unit configured to transmit the identification code to a computing device of a user. The central server includes a database. The central server is configured to receive the identification code from the computing device and retrieve concentrate information corresponding to the identification code from the database. The central server is configured to transmit the concentrate information to the computing device for displaying to the user.

Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.