A method is described for the identification of multi-subunit biocomplex drug targets. The method includes identifying a target that performs a biological function, wherein the target comprises one or more subunits, wherein a minimum number of the one or more subunits is inactivated to inhibit the biological function. The method includes selecting a drug that binds specifically to each subunit of the one or more subunits with a target probability. The method describes a relationship between inhibition efficiency of the drug and total number of the one or more subunits using a binomial distribution, wherein the inhibition efficiency comprises a probability that the delivered drug blocks the biological function. The method includes confirming empirically the relationship using an experimental target. The method includes administering the drug to the target to treat a multi-drug resistant disease, wherein the target comprises a biological complex in a mammalian subject.

A method is described for the identification of multi-subunit biocomplex drug targets. The method includes identifying a target that performs a biological function, wherein the target comprises one or more subunits, wherein a minimum number of the one or more subunits is inactivated to inhibit the biological function. The method includes selecting a drug that binds specifically to each subunit of the one or more subunits with a target probability. The method describes a relationship between inhibition efficiency of the drug and total number of the one or more subunits using a binomial distribution, wherein the inhibition efficiency comprises a probability that the delivered drug blocks the biological function. The method includes confirming empirically the relationship using an experimental target. The method includes administering the drug to the target to treat a multi-drug resistant disease, wherein the target comprises a biological complex in a mammalian subject.

The present invention provides a method of generating a sequence of pseudo-random numbers which are difficult to predict. The method includes: (i) generating a plurality of candidate pseudo-random numbers by a respectively corresponding plurality of (differently structured) linear feedback shift registers; (ii) generating a selector number from one or more additional linear feedback shift registers; and (iii) selecting a candidate number from the plurality of candidate numbers, based on the selection number to produce a selected pseudo-random number for output.

A device obtains a series of measurements of a physiological parameter of one or more patients. The device displays a monitoring workflow home screen when the device is operating within a monitoring workflow. The device displays a non-monitoring workflow home screen when the device is operating within a non-monitoring workflow. The device displays a continuous workflow home screen when the device is operating within a continuous workflow. The monitoring workflow home screen, the non-monitoring workflow home screen, and the continuous workflow home screen are each different.

Analysis system and computer implemented method for analyzing biological samples are disclosed. The system has at least one analyzer for performing an analysis and a decision unit being operable to determine in response to the receipt of the analysis request whether results obtained from performing the analysis on the sample indicated in the analysis request are valid. This determination is executed by retrieving the meta information assigned to the sample and by applying the at least one condition on the meta information and wherein the at least one applied condition has at least a condition on whether the sample allows a valid analysis on the sample, and wherein the decision unit returns the decision that the analysis exercised on the indicated sample will return a valid result in case the conditions of the condition set are met by the sample.

A method of simulating the activity of the human nervous system includes providing a networked server for access by a user of a general purpose computer, with a database having predetermined data on human nervous system activity being in communication with the networked server. User information is input into the general purpose computer which is correlated with data in the networked database to determine what part of the human nervous system is impacted by the user information input. The general purpose computer displays a simulated image of a portion of the human nervous system and animates the impacted part of the human nervous system determined in the correlation.

The present invention relates to a biological network analysis device and a method therefor. The biological network analysis method includes receiving biological data from an external server, extracting biological information from the received biological data to generate a biological interaction database, storing the generated biological interaction database, receiving input of a plurality of biological objects for a simulation, and performing a simulation by setting each of the biological objects to a node and automatically and generating a Boolean role based on the generated biological interaction database. Accordingly, the biological network analysis method conveniently designs a biological network and simulates the biological network based on biological interaction information by generating the biological interaction database.

The present disclosure provides methods of making and methods of using IL-18 mimic polypeptides for use in therapeutic and non-therapeutic applications. The synthetic IL-18 mimics an increase IL-18R signaling activity even in the presence of an inhibitory molecule such as IL-18BP.

Systems and methods for providing a reservoir simulation are based on data from an unstructured grid using a structured grid reservoir simulator. Exemplary methods comprise obtaining an unstructured grid reservoir model comprising a reservoir model discretized on an unstructured grid. A virtual structured grid is defined for the unstructured grid reservoir model. The unstructured grid is aligned with the virtual structured grid by adding cells to the unstructured grid to make the unstructured grid and virtual structured grid have the same number of cells. The virtual structured grid may be represented in the unstructured grid. Structured grid reservoir simulator input data comprising reservoir model data assigned to the virtual structured grid is prepared based on reservoir model data in the unstructured grid model. A structured grid reservoir simulation is performed using the structured grid reservoir simulator input data to produce a reservoir simulation.

A system, method and apparatus for executing a sequence analysis pipeline on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits are arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. Each subset of the hardwired digital logic circuits is formed in a wired configuration to perform the one or more steps in the sequence analysis pipeline.