Abstract: The present invention relates to methods of using dulaglutide for the treatment of chronic kidney disease in patients having moderate to late stage chronic kidney disease.

Abstract: Provided is a method for the model-based determination of a system status of a dynamic system by means of a model, wherein: a recurrent neural network is provided as the model of the dynamic system; the model is supplied with a time series of potentially recordable measurement values as an input variable, the values comprising recorded and missing measurement values; at least one system status associated with a time point is generated from the model, from which status at least one target value belonging to the respective time point can be determined; sequential system statuses transition into one other by means of a respective status transition; and a correction of at least one system status is carried out on the basis of the time series with the aid of the status transition.

Abstract: A therapy system includes a therapy source (2) emitting radiation or waves, an ultrasound probe (14), a patient rest as well as a multi-axis positioning system (X, Y, Z) with several drives, by way of which the patient rest and the therapy source (2) can be moved to one another in all three spatial directions. A control device (26) is coupled to the ultrasound probe (14) and configured such that in at least one operating mode, the control device (26) simultaneously activates the drives of the multi-axis positioning system (X, Y, Z) such that the patient rest is moved relative to the therapy source along a selectable movement path (B) which lies within the plane of an ultrasound picture (20) which is currently recorded by the ultrasound probe (14). A method for positioning a patient rest relative to a therapy source is also provided.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Abstract: A biochemical analytical device and a biochemical analytical method for determining an analyte in a test sample are provided. In the technique, the biochemical analytical device includes a sample port to receive the test sample, a sensor to probe the test sample and to generate sensor data, and a processor. The sensor data corresponds to the analyte in the test sample. The processor receives the sensor data from the sensor and selects a non-linear function for the received sensor data. The processor fits the selected non-linear function to the sensor data. Additionally, the processor compares the fitted non-linear function to a reference data to determine the analyte in the test sample.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: A therapy system includes a therapy source (2) emitting radiation or waves, an ultrasound probe (14), a patient rest as well as a multi-axis positioning system (X, Y, Z) with several drives, by way of which the patient rest and the therapy source (2) can be moved to one another in all three spatial directions. A control device (26) is coupled to the ultrasound probe (14) and configured such that in at least one operating mode, the control device (26) simultaneously activates the drives of the multi-axis positioning system (X, Y, Z) such that the patient rest is moved relative to the therapy source along a selectable movement path (B) which lies within the plane of an ultrasound picture (20) which is currently recorded by the ultrasound probe (14). A method for positioning a patient rest relative to a therapy source is also provided.

Abstract: A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.

Abstract: Modeling effectiveness of a verum includes dividing a group of patients into a placebo group and a verum group, defining a plurality of characteristics of the group of patients, and generating a model for the placebo group based on the plurality of characteristics. The method also includes generating a model for the verum group based on the plurality of characteristics, and isolating a placebo effect in the verum group in order to determine a pure verum effect.

Abstract: Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Abstract: Winding crown for a timepiece, said crown (40) including a bottom (42) and a skirt (44), which delimits an inner volume housing at least one elastically deformable element, the crown (40) being characterized in that a stop member is inserted between the elastically deformable element (50) and the bottom (42) of the crown (40) to prevent any surface contact between said elastically deformable element (50) and said bottom (42).

Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely modeling the dynamic behavior of the technical system with a recurrent neural network using training data, the recurrent neural network includes states and actions determined using a simulation model at different times and learning an action selection rule by the recurrent neural network to a further neural network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.

Abstract: A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.

Abstract: Winding crown for a timepiece, said crown (32) including an uncoupling mechanism (1) comprising a rigid drive ring (4, 400) secured to the crown (32) and cooperating with at least one elastically deformable element (2; 200) secured to a pipe (12) to one end of which a winding stem is secured, the rigid ring (4; 400) driving the deformable elastic element (2; 200) in at least one rotational direction until the resistant torque opposed by said elastic element (2; 200) exceeds a threshold value beyond which said elastic element (2; 200) is deformed so as to interrupt the drive connection thereof with the rigid ring (4; 400), the elastically deformable element (2; 200) therefore having means for temporary coupling said element to said rigid drive ring (4; 400), the crown (32)) being characterized in that the elastically deformable element (2; 200) includes means for centering said element relative to the rigid drive ring (4; 400).

Abstract: Winding crown for a timepiece, said crown (40) including a bottom (42) and a skirt (44), which delimits an inner volume housing at least one elastically deformable element, the crown (40) being characterized in that a stop member is inserted between the elastically deformable element (50) and the bottom (42) of the crown (40) to prevent any surface contact between said elastically deformable element (50) and said bottom (42).

Abstract: Winding crown for a timepiece, said crown (32) including an uncoupling mechanism (1) comprising a rigid drive ring (4, 400) secured to the crown (32) and cooperating with at least one elastically deformable element (2; 200) secured to a pipe (12) to one end of which a winding stem is secured, the rigid ring (4; 400) driving the deformable elastic element (2; 200) in at least one rotational direction until the resistant torque opposed by said elastic element (2; 200) exceeds a threshold value beyond which said elastic element (2; 200) is deformed so as to interrupt the drive connection thereof with the rigid ring (4; 400), the elastically deformable element (2; 200) therefore having means for temporary coupling said element to said rigid drive ring (4; 400), the crown (32)) being characterized in that the elastically deformable element (2; 200) includes means for centring said element relative to the rigid drive ring (4; 400).

Abstract: Winding crown for a timepiece, said crown (32) including an uncoupling mechanism (1) comprising a rigid drive ring (4, 400) secured to the crown (32) and cooperating with at least one elastically deformable element (2; 200) secured to a pipe (12) to one end of which a winding stem is secured, the rigid ring (4; 400) driving the deformable elastic element (2; 200) in at least one rotational direction until the resistant torque opposed by said elastic element (2; 200) exceeds a threshold value beyond which said elastic element (2; 200) is deformed so as to interrupt the drive connection thereof with the rigid ring (4; 400), the elastically deformable element (2; 200) therefore having means for temporary coupling said element to said rigid drive ring (4; 400), the crown (32)) being characterized in that the elastically deformable element (2; 200) includes means for centring said element relative to the rigid drive ring (4; 400).

Abstract: The medical therapy device includes at least one picture-providing locating device, a treatment device, a patient rest, means for the motorized positioning of at least the treatment device relative to the patient rest, means for control of the therapy device, and a display and operating device. The treatment center of the treatment device may be represented on the display in at least one picture of the locating device on the display, and operating elements are provided, with which the positioning in coordinates of the locating device may be controlled.

Abstract: Winding crown for a timepiece, said crown (32) including an uncoupling mechanism (1) comprising a rigid drive ring (4, 400) secured to the crown (32) and cooperating with at least one elastically deformable element (2; 200) secured to a pipe (12) to one end of which a winding stem is secured, the rigid ring (4; 400) driving the deformable elastic element (2; 200) in at least one rotational direction until the resistant torque opposed by said elastic element (2; 200) exceeds a threshold value beyond which said elastic element (2; 200) is deformed so as to interrupt the drive connection thereof with the rigid ring (4; 400), the elastically deformable element (2; 200) therefore having means for temporary coupling said element to said rigid drive ring (4; 400), the crown (32)) being characterized in that the elastically deformable element (2; 200) includes means for centring said element relative to the rigid drive ring (4; 400).