Abstract: Delivery systems, devices, and methods for delivering a valve prosthesis to a native valve annulus are provided. The valve prosthesis may be secured to the native valve via an anchor that encircles the native valve annulus and the valve prosthesis. The anchor may be deployed around the chordae tendinea and/or leaflets of the native valve. A tether may be connected to the anchor and extend outside of the patient's body to maintain communication with the anchor. After the anchor is deployed, a guidewire may be advanced through the patient's vessels parallel to the tether and through the native valve annulus. A valve delivery catheter carrying the valve prosthesis may be advanced over the guidewire and into the native valve annulus where the valve prosthesis is released and deployed. The tether may be used to position the anchor and/or valve prosthesis during one or more operations of the valve delivery process.

Abstract: A method for monitoring the thread run at a workstation of a textile machine having a thread feeding device for supplying a thread, a traversing device for traversing the supplied thread, a thread winding device for winding the traversed thread onto a run-on bobbin, a pneumatic thread storage, a thread storage unit with a thread guide arm pivotably mounted about a pivot axis, a controllable drive unit for reversible pivoting of the thread guide arm, and a sensor unit collecting sensor information about a thread tension acting on the thread guide arm and/or a position of the thread guide arm, and transmitting the sensor information to a control system associated with the workstation. The thread storage unit is arranged at the workstation such that the control system detects a thread breakage and/or an emptied pneumatic thread storage on the basis of the transmitted sensor information.

Abstract: Examples of the present disclosure describe systems and methods for probabilistic neural network architecture generation. In an example, an underlying distribution over neural network architectures based on various parameters is sampled using probabilistic modeling. Training data is evaluated in order to iteratively update the underlying distribution, thereby generating a probability distribution over the neural network architectures. The distribution is iteratively trained until the parameters associated with the neural network architecture converge. Once it is determined that the parameters have converged, the resulting probability distribution may be used to generate a resulting neural network architecture. As a result, intermediate architectures need not be fully trained, which dramatically reduces memory usage and/or processing time.

Abstract: A device and system for use with medical devices, such as catheter devices or systems. The device or system comprises an anchor for securing to tissue. In some examples, the device or system is used in treating a diseased native valve in a patient. The anchor may be part of a delivery device to implant a prosthetic valve. Subsequent to delivery, the components of the delivery device are actuated to secure the prosthetic valve within the diseased valve.

Abstract: Provided herein are KRAS G12C inhibitors, such as composition of the same, and methods of using the same. These inhibitors are useful for treating a number of disorders, including pancreatic, colorectal, and lung cancers.

Abstract: Examples of the present disclosure describe systems and methods for probabilistic neural network architecture generation. In an example, an underlying distribution over neural network architectures based on various parameters is sampled using probabilistic modeling. Training data is evaluated in order to iteratively update the underlying distribution, thereby generating a probability distribution over the neural network architectures. The distribution is iteratively trained until the parameters associated with the neural network architecture converge. Once it is determined that the parameters have converged, the resulting probability distribution may be used to generate a resulting neural network architecture. As a result, intermediate architectures need not be fully trained, which dramatically reduces memory usage and/or processing time.

Abstract: A method of manufacturing a pressure sensor for sensing a pressure of a process fluid includes obtaining a sensor body having a sensor cavity formed therein. A metal tube is placed through an opening in the sensor body into the sensor cavity. A rod is placed through the metal tube and into the sensor cavity. The sensor cavity is at least partially filled with a dielectric material and the dielectric material completely covers the metal tube carried in the sensor cavity and a portion of the rod. The rod is removed and thereby forming a dielectric passageway which is fluidically coupled to the metal tube. The sensor cavity is sealed with a deflectable diaphragm which is configured to deflect in response to applied pressure from the process fluid. A differential pressure sensor for sensing a differential pressure of a process fluid includes a sensor body having a sensor cavity formed therein is also provided.

Abstract: An agricultural implement is configured to be advanced in a forward direction and includes an implement frame. The implement frame includes a pair of laterally extending fore-and-aft spaced frame members. The frame also includes a fore-and-aft extending interconnecting member that extends between the frame members and presents forward and aft ends. At least one of the frame members comprises a tubular beam presenting forward and aft walls. The forward and aft walls at least partly define an interior beam space, with a window opening being defined in a first one of the walls. The tubular beam receives a respective end of the interconnecting member so that the interconnecting member extends into and out of the interior beam space through the window opening. The respective end of the interconnecting member is welded to a second one of the walls.

Abstract: An integrated system and method for personalized stratification and prediction of neurodegenerative disease state and progression rate. Homogenous patient clusters are identified among heterogeneous patient data. Each patient cluster is characterized by specific disease factors that are used to predict disease progression state and rate for patients assigned to a particular patient cluster in accordance with his disease profile.

Abstract: A non-transitory processor-readable medium storing code representing instructions to be executed by a processor can cause the processor to receive an indication to load balance a group of sessions associated with a network node and a switch across a group of links between a gateway device and the switch at a first time. The code causes the processor to calculate at a second time, a load based on the group of sessions and associated with a first set of links in an active configuration before the first time. The code causes the processor to send a signal to cause a set of sessions from the group of sessions to re-establish themselves at a third time based on a threshold value calculated based on the load such that the set of sessions are load balanced across a second set of links in the active configuration at the third time.

Abstract: Examples of the present disclosure describe systems and methods for probabilistic neural network architecture generation. In an example, an underlying distribution over neural network architectures based on various parameters is sampled using probabilistic modeling. Training data is evaluated in order to iteratively update the underlying distribution, thereby generating a probability distribution over the neural network architectures. The distribution is iteratively trained until the parameters associated with the neural network architecture converge. Once it is determined that the parameters have converged, the resulting probability distribution may be used to generate a resulting neural network architecture. As a result, intermediate architectures need not be fully trained, which dramatically reduces memory usage and/or processing time.

Abstract: Examples of the present disclosure describe systems and methods for probabilistic neural network architecture generation. In an example, an underlying distribution over neural network architectures based on various parameters is sampled using probabilistic modeling. Training data is evaluated in order to iteratively update the underlying distribution, thereby generating a probability distribution over the neural network architectures. The distribution is iteratively trained until the parameters associated with the neural network architecture converge. Once it is determined that the parameters have converged, the resulting probability distribution may be used to generate a resulting neural network architecture. As a result, intermediate architectures need not be fully trained, which dramatically reduces memory usage and/or processing time.

Abstract: An integrated system and method for personalized stratification and prediction of neurodegenerative disease state and progression rate. Homogenous patient clusters are identified among heterogeneous patient data. Each patient cluster is characterized by specific disease factors that are used to predict disease progression state and rate for patients assigned to a particular patient cluster in accordance with his disease profile.

Abstract: A non-transitory processor-readable medium storing code representing instructions to be executed by a processor can cause the processor to receive an indication to load balance a group of sessions associated with a network node and a switch across a group of links between a gateway device and the switch at a first time. The code causes the processor to calculate at a second time, a load based on the group of sessions and associated with a first set of links in an active configuration before the first time. The code causes the processor to send a signal to cause a set of sessions from the group of sessions to re-establish themselves at a third time based on a threshold value calculated based on the load such that the set of sessions are load balanced across a second set of links in the active configuration at the third time.

Abstract: An optical network is provided that includes at least one strand of a plurality of strands of optical fiber optically connected to a first fiber distribution hub and an access terminal. The at least one strand optically is also connected to a second fiber distribution hub and the access terminal. The at least one strand thus provides a full duplex optical path in a first direction from the first fiber distribution hub to the access terminal and in a second direction from the second fiber distribution hub to the access terminal.

Abstract: Embodiments of the present disclosure relate to a vaccine composition comprising a bacterial strain with a deleted or inactive ntrX gene.

Abstract: In one embodiment, an apparatus can include a switch fabric. The apparatus can also include a first edge device operatively coupled to an edge of the switch fabric and having a plurality of ports. The apparatus can also include a second edge device operatively coupled to the edge of the switch fabric and having a plurality of ports, the switch fabric defining a plurality of single-hop paths between the first edge device and the second edge device. The first edge device configured to send to a peripheral processing device operatively coupled to the first edge device a representation of a mapping of a portion of the plurality of ports of the first edge device and a portion of the plurality of ports of the second edge device to a plurality of ports included in a non-edge device represented within a virtual multi-hop network topology.

Abstract: An optical network is provided that includes at least one strand of a plurality of strands of optical fiber optically connected to a first fiber distribution hub and an access terminal. The at least one strand optically is also connected to a second fiber distribution hub and the access terminal. The at least one strand thus provides a full duplex optical path in a first direction from the first fiber distribution hubs to the access terminal and in a second direction from the second fiber distribution hub to the access terminal.

Abstract: In an embodiment, a method comprises: receiving, by a mechanical offset controller, input data; detecting, by the mechanical offset controller, a waveform command in the input data; responsive to the detecting, generating, by the mechanical offset controller, an unparking command; receiving, by a closed-loop controller, the unparking command; and moving, by the closed-loop controller, a mass in a haptic module from a mechanical resting position to a sensor reference position in accordance with the unparking command. The method further comprises: detecting, by the mechanical offset controller, that the input data does not include the waveform command; responsive to the detecting, generating, by the mechanical offset controller, a parking command; receiving, by a closed-loop controller, the parking command; and moving, by the closed-loop controller, the mass in the haptic module from the sensor reference position to the mechanical resting position in accordance with the parking command.

Abstract: An optical network is provided that includes at least one strand of a plurality of strands of optical fiber optically connected to a first fiber distribution hub and an access terminal. The at least one strand optically is also connected to a second fiber distribution hub and the access terminal. The at least one strand thus provides a full duplex optical path in a first direction from the first fiber distribution hub to the access terminal and in a second direction from the second fiber distribution hub to the access terminal.