Abstract: A tubing spool of a wellhead assembly can include a body having a body cavity, a first channel, and a second channel disposed therein, where the body cavity is configured to receive a tubing hanger used to couple to and suspend a tubing string, where the first channel is configured to be in communication with a tubing string cavity within the tubing string, where the second channel is configured to be in communication with an annulus located between the tubing string and a production casing, and where the first channel and the second channel are each configured to facilitate flow of at least one of a plurality of fluids in either direction independently of each other. The tubing spool can also include a first back pressure valve disposed within the first channel and a second back pressure valve disposed within the second channel.

Abstract: Techniques for securing data are disclosed. A security device includes: one or more processors; one or more authentication components including one or more of a biometric reader, a positioning system, and a wireless receiver; and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including securing data on a storage device that is external to the security device, using multiple authentication factors obtained using the one or more authentication components.

Abstract: An example method includes storing healthcare information related to a patient on the cloud-based clinical information system and determining if a first local information system of a first healthcare entity includes a first existing patient record associated with the patient. The example method also includes communicating a first command from the cloud-based clinical information system to an edge device located at a facility associated with the first healthcare entity and in communication with the first local information system. The edge device is to automatically attach a first portion of the healthcare information to the first existing patient record if the first local information system includes the first existing patient record or automatically generate a first new patient record to be populated by the first portion of the healthcare information if the first local information system does not include the first existing patient record based on the first command.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Embodiments discussed herein facilitate determination of a likelihood of biochemical recurrence (BCR) of cancer (e.g., prostate cancer, etc.). One example embodiment is a method, comprising: accessing at least a portion of a digitized stained histology slide comprising a tumor; automatically segmenting, via a trained deep learning (DL) model, cribriform morphology in connection with the tumor on the at least the portion of the digitized stained histology slide; determining a cribriform-to-tumor area ratio (CAR) based at least in part on an area of the segmented cribriform morphology and an area of the tumor; and determining a risk of biochemical recurrence (BCR) of a cancer associated with the tumor based at least in part on the CAR.

Abstract: Embodiments discussed herein facilitate determination of risk of relapse of AML post-transplant. One example embodiment is a method, comprising: accessing a digital whole slide image (WSI) comprising a post-transplant bone marrow aspirate from a patient that has acute myeloid leukemia (AML); segmenting one or more myeloblasts on the digital WSI; extracting one or more features from the segmented one or more myeloblasts; providing the one or more features extracted from the segmented one or more myeloblasts to a trained machine learning model; and receiving, from the trained machine learning model, an indication of a risk of relapse of the AML.

Abstract: Embodiments discussed herein facilitate determination of one of a probability of prostate cancer recurrence-free survival or a risk factor associated with prostate cancer based on intra-tumor stromal morphology. Example embodiments can perform operations comprising: accessing a digitized histological image of a prostate of a patient, wherein the histological image comprises a region of interest associated with prostate cancer; identifying nuclei of intra-tumoral stromal cells within the region of interest; extracting, for the region of interest of the digitized histological image, one or more features describing the structure of the intra-tumoral stromal cells; and generating, via a model based at least on the one or more features, one of a probability of prostate cancer recurrence-free survival or a risk score associated with prostate cancer for the patient based at least on the extracted one or more features.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: An example method includes monitoring healthcare information employed by a local information system of a first healthcare entity via an edge device located at a facility of the first healthcare entity and in communication with a local information system. The edge device is to implement a local cloud system. The local cloud system is to be accessible by only the first healthcare entity and healthcare entities affiliated with the first healthcare entity. The example method also includes determining if the healthcare information has a first characteristic via the edge device, determining if the healthcare information has a second characteristic via the edge device, and automatically uploading the healthcare information onto the local cloud system if the healthcare information has the first characteristic. The example method also includes automatically uploading the healthcare information onto a remote cloud system if the healthcare information has the second characteristic.

Abstract: Embodiments include controlling a processor to perform operations for predicting biochemical recurrence (BCR) in prostate cancer (PCa), including accessing a first digitized pathology slide having a first stain channel of a region of tissue demonstrating PCa; accessing a second digitized pathology slide having a second, different stain channel of the region of tissue; extracting morphology features from the first stain channel; extracting stain intensity features from the second stain channel, where a stain intensity feature quantifies an amount of a molecular biomarker present in a cellular nucleus; controlling a first machine learning classifier to generate a first probability of BCR based on the morphology features; controlling a second machine learning classifier to generate a second, different probability of BCR based on the stain intensity features; computing an aggregate probability of BCR based on the first probability and the second probability; and displaying the aggregate probability.

Abstract: Methods of code conversion are provided. Aspects include obtaining a first code point from a source data string in a first character encoding, wherein the source data string is to be converted to a target data string in a second character encoding. A target code point corresponding to the first code point is looked up in a map table, wherein the target code point is in the second character encoding. It is determined whether the first code point is a first combining character in response to receiving a lookup failure generated from the looking up operation. A combination unit having the first combining character and a base character next to the first combining character in the source data string is identified in response to determining that the first code point is the first combining character. The combination unit is converted to a substitute character in the target data string.

Abstract: According to one embodiment, a computer program embodied on a tangible computer readable medium includes computer code for identifying a wireless communications gateway in communication with a management controller, computer code for establishing a wireless communications connection between one or more drives and the management controller, utilizing the wireless communications gateway, and computer code for communicating one or more of monitoring data and control data between the management controller and the one or more drives, utilizing the wireless communications connection.

Abstract: Systems and methods for initializing computing device bus lanes during boot are disclosed. According to an aspect, a method may be implemented at a processing module configured to operate when a computing device is not booted. The method includes determining, prior to the computing device being booted, a number of lanes for communication via a bus by the computing device. The method also includes initializing, during boot of the computing device, the lanes of the bus according to the determined number of lanes.

Abstract: Embodiments include controlling a processor to perform operations for predicting biochemical recurrence (BCR) in prostate cancer (PCa), including accessing a first digitized pathology slide having a first stain channel of a region of tissue demonstrating PCa; accessing a second digitized pathology slide having a second, different stain channel of the region of tissue; extracting morphology features from the first stain channel; extracting stain intensity features from the second stain channel, where a stain intensity feature quantifies an amount of a molecular biomarker present in a cellular nucleus; controlling a first machine learning classifier to generate a first probability of BCR based on the morphology features; controlling a second machine learning classifier to generate a second, different probability of BCR based on the stain intensity features; computing an aggregate probability of BCR based on the first probability and the second probability; and displaying the aggregate probability.

Abstract: A storage-area network (SAN) manager running on a computing device connected to a fabric receives a request to allocate and mount a logical volume on a client computing device connected to the fabric. The manager groups storage drives connected to the fabric by failure domains. The manager filters the storage drives by native capabilities, available capacities, and dynamic performance to remove from consideration any storage drive having insufficient native capabilities, an insufficient available capacity, and/or an insufficient dynamic performance to satisfy the request. The manager selects a set of storage drives on which to store data of the logical volume, from the remaining storage drives, taking into account the failure domains. The manager sends a command to each such storage drive to provision a drive volume, and a command to a software agent on the client device to create and mount the logical volume using these drive volumes.

Abstract: A storage-area network (SAN) manager running on a computing device connected to a fabric receives a request to allocate and mount a logical volume on a client computing device connected to the fabric. The manager groups storage drives connected to the fabric by failure domains. The manager filters the storage drives by native capabilities, available capacities, and dynamic performance to remove from consideration any storage drive having insufficient native capabilities, an insufficient available capacity, and/or an insufficient dynamic performance to satisfy the request. The manager selects a set of storage drives on which to store data of the logical volume, from the remaining storage drives, taking into account the failure domains. The manager sends a command to each such storage drive to provision a drive volume, and a command to a software agent on the client device to create and mount the logical volume using these drive volumes.

Abstract: A storage-area network (SAN) system includes one or more storage drives directly connected to a fabric. Each storage drive provisions and operates a drive volume, and creates a security token for the drive volume. The system includes a client computing device directly connected to the fabric, and that executes a SAN software agent to create, mount, and use a logical volume realized by drive volumes of the storage drives. The client computing device accesses each drive volume using the security token for the drive volume. The system includes a SAN manager directly connected to the fabric that manages the drive volumes of the storage drives, manages the logical volume that the SAN software agent operates, receives from each storage drive the security token for the drive volume of the storage drive, and sends the security token for the drive volume of each storage drive to the SAN software agent.

Abstract: Cloud-based clinical information system apparatus and associated methods are disclosed. An example cloud-based clinical information system includes a processor to receive a referral order including trauma case information for a first patient from a first local information system of a first healthcare entity to refer the first patient to a second healthcare entity, the referral order to include a portion of the first patient's information from the first local information system. The cloud-based clinical information system is to notify the second healthcare entity of the referral and provide a second local information system of the second healthcare entity with the referral order. The second local information system of the second healthcare entity is to process the referral order and accept or reject the referral order from the first local information system via the cloud-based clinical information system.

Abstract: A firearm safety apparatus is provided that includes two or more locking arms or coverings connected by a hinge and configured to engage a trigger guard of a firearm, in a closed position preventing access to the trigger. A locking mechanism retains the arms in a closed position until opened by an activation signal. A firearm safety apparatus is also provided that includes a housing connected to a firearm. A trigger guard cover is slideably or hingably connected to the housing. A locking mechanism is coupled to the trigger guard cover to surround a trigger of the firearm until an activation signal is received. The locking mechanism exposes the trigger through a hinged or slide motion. The housing is amenable to inclusion of various tactical accessories such as a light source or a camera.

Abstract: An example method includes storing healthcare information related to a patient on the cloud-based clinical information system and determining if a first local information system of a first healthcare entity includes a first existing patient record associated with the patient. The example method also includes communicating a first command from the cloud-based clinical information system to an edge device located at a facility associated with the first healthcare entity and in communication with the first local information system. The edge device is to automatically attach a first portion of the healthcare information to the first existing patient record if the first local information system includes the first existing patient record or automatically generate a first new patient record to be populated by the first portion of the healthcare information if the first local information system does not include the first existing patient record based on the first command.