36results about "Biomolecular computers" patented technology

A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and / or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

An artificial reality system performs local user adaptation of machine learning models and global improvement of the machine learning models while ensuring data security and privacy. An artificial reality headset uses smart sensors and a system on chip (SoC). The smart sensor extracts features from sensor data, encrypts them and transmits the encrypted features to the SoC of the headset. The SoC provides the encrypted features to a server, for example, a server in a cloud platform. The server updates a base model with collected features and sends the updated base model parameters to headsets. The smart sensors of a headset receiving the updated model parameters, balance the model parameters with locally stored feature data, for example, user calibration features to perform user customization of the machine learning model.

The invention relates to the technical field of electromagnetic metasurfaces and bionic computing, and discloses a bionic information processing method and system based on an electromagnetic metasurface. The method comprises the following steps: S1, mapping DNA sequence information into a multi-dimensional coding state of the bionic metasurface information processing unit in an electromagnetic adjustable parameter space; s2, realizing co-evolution of the multi-dimensional coding state in space, frequency, phase and amplitude dimensions by dynamically regulating and controlling electromagnetic response parameters of the bionic metasurface information processing unit; and S3, using propagation and interference of electromagnetic waves in the diffraction neural network and a near-field coupling effect between the bionic metasurface information processing units to complete copying, logical operation and information read-write operation of the DNA sequence information. The functions of DNA coding, copying, logic calculation, information reading and writing and the like are uniformly mapped into the programmable electromagnetic metasurface platform, and cooperative processing of information in the aspects of space, electromagnetism and algorithm is achieved.

The invention discloses an industrial control network risk monitoring method based on immune recognition, and relates to the technical field of network security risk monitoring, and the method comprises the steps: obtaining multi-source heterogeneous industrial control protocol flow data and a known data set which are collected in real time; preprocessing the data to obtain a standardized antigen vector; according to the standardized antigen vector, carrying out mixed training on a preset depth auto-encoder and a graph neural network model based on an immune algorithm to obtain a trained depth auto-encoder-graph neural network model and an initial detector population; inputting the standardized antigen vector into a lightweight model to obtain a compressed antigen vector; and detecting the compressed antigen vector based on the initial detector population to obtain an antigen vector for triggering an alarm. According to the method, the definition and expandability of original protocol semantics are improved, continuous coverage of unknown attacks is ensured, and the deployment and updating process of the model is optimized.

The governed interaction interface maintains an identity-preserving semantic-state for a knowledge entity and updates it only through validated semantic evidence generated autonomously by execution mechanisms. Semantic intentions describe semantic needs and are transformed into capability-requests containing no operational commands. Execution mechanisms—including environmental devices, perception models, symbolic analyzers, virtual actors, and software agents—interpret capability-requests independently through tool-side semantic interpreters and may act or decline to act. The system does not observe or evaluate tool behavior and receives only semantic evidence describing semantic meaning of any resulting effect. The governance engine evaluates each evidence fragment independently under identity, coherence, lineage, evidentiary sufficiency, deviation constraints, and contextual compatibility. Semantic-state transitions occur only when validated evidence satisfies governance constraints. The system performs no prediction, optimization, control computation, multimodal fusion, or supervisory coordination, remaining fully separated from device-level behavior.

The invention provides an intelligent terminal outdoor cooperation method and system based on an ant colony algorithm. The method comprises the following steps: acquiring outdoor navigation data, processing the outdoor navigation data into navigation data fragments, and distributing the navigation data fragments to a plurality of intelligent terminals; screening out a main relay node; when entering the target area, determining the intelligent terminal as main navigation equipment, and controlling the gas intelligent terminal to enter a low-power-consumption mode; summarizing the navigation data fragments stored in the intelligent terminals through the main relay node, forwarding the summarized navigation data fragments to the main navigation equipment, and splicing to generate complete navigation data of the target area; and the main navigation equipment obtains and generates navigation information and synchronizes the navigation information to each intelligent terminal through the main relay node. Through pheromone dynamic iteration of the ant colony algorithm and a distributed decision-making mechanism, efficient cooperation of multiple intelligent terminals on division storage and group collaborative splicing is realized, and on the premise of guaranteeing complete navigation data splicing precision and real-time navigation reliability, the overall energy consumption is remarkably reduced, and the equipment endurance is prolonged.

Systems and techniques are provided for making genetically engineered ion channels (ICs) with bioluminescent resonance energy transfer (BRET) complexes and using such ICs for efficient readout of neural activity and output of biological neuronal networks.SOLUTION: In one implementation, the disclosed technology includes identifying a target location in the IC for expression of a target protein including a donor tag protein and an acceptor tag protein, and modifying the genome of the neuronal cell at a portion associated with the target location in the IC. The technique further includes causing the neuronal cell to express the target protein in the IC according to the modified genome. In the first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein that is related to the absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.SELECTED DRAWING: Figure 3A-3C

Aspects herein describe a new approach to providing an interpretable solution for a domain-specific problem using artificial intelligence. Problems of a particular domain, such as biological problems, are identified by a computing unit. The computing unit determines an initial subset of features including various features for observable characteristics of the problem. The computing unit optimizes the subset of initial features to improve the accuracy of the subset in generating a solution to the problem. The computing unit trains one or more predictive artificial intelligence models based on the optimized subset to output a solution to the problem. The computing unit then uses the trained predictive model to output solutions to the one or more additional domain-specific problems. The computing unit may additionally provide an interpretable solution for a problem of a particular field by outputting a representation of steps performed by the predictive model to output the solution.

Techniques for identifying types of cells present in a biological sample using flow cytometry performed using a panel of markers and multiple machine learning models. The techniques include: obtaining flow cytometry data for the biological sample, the biological sample previously-obtained from a subject and comprising a plurality of cells, the flow cytometry data including flow cytometry measurements obtained during respective flow cytometry events, the flow cytometry events including a subset of events corresponding to cells in the biological sample being measured by the flow cytometry platform; and identifying types of cells of the plurality of cells using the multiple machine learning models to obtain a respective plurality of cell types.

Provided herein are novel Class 2 Type II and Type V CRISPR-Cas RNA-guided endonucleases such as Cas9 and Cas12 endonucleases and systems comprising the same. Methods of making and methods of using the same are also provided. Exemplary methods of use include modifying target DNA and detecting targeted DNA, which can be useful for therapeutic and diagnostic applications. Some diagnostic applications can take advantage of the collateral nuclease activity of the enzyme bound to the target sequence.

The present invention is directed to a single input multiplex decision expression system, and a method of using same, such as for expressing a reporter protein.

A wearable includes a sensor to generate sensor data and a processor. The sensor includes compute resources configured to update a machine learning model based on feature data extracted from the sensor data. The processor is configured to receive the encrypted data associated with the machine learning model from the sensor and transmit the encrypted data to a server. The parameters of a base machine learning model are received from the server and the parameters are transmitted to the sensor. The sensor is configured to locally update the received base machine learning model based on the feature data.

Disclosed herein are methods, systems, computer-implemented methods, and computer-accessible media for modifying and / or redesigning peptide-centric chimeric antigen receptors (PC-CARs) against novel peptide antigens.

The invention discloses an elastic load balancing and tuning method and system, and belongs to the technical field of communication, and the method comprises the steps: determining the load difference of an edge server and the total time delay of a terminal task through constructing a terminal equipment-edge server two-layer system model; constructing an optimization function and setting a constraint condition by taking minimization of the weighted sum of the two as a target; and finally, solving through a two-stage bionic algorithm, training a plurality of initial deep neural networks by means of a multi-group intelligent algorithm, optimizing through a genetic algorithm to obtain an optimal model, and inputting current scene parameters to obtain a final equalization strategy. According to the method, the problem of rapid load redistribution in an abnormal scene is solved, the search efficiency and the generalization ability are considered, the system efficiency and the load balance degree are improved, the energy consumption is reduced, overload or idling of the server is avoided, and the performance is superior to that of a traditional algorithm.

The invention discloses a cooperative control power distribution network harmonic suppression method of a multi-agent tissue enzyme numerical membrane system, and belongs to the technical field of electric energy quality treatment. The method comprises the following steps: sampling current at a bus node, extracting dominant subharmonic current by using sliding window discrete Fourier transform, and calculating the distortion rate of each subharmonic; setting a multi-agent communication connection topology, and adopting a two-way communication connection mode between adjacent regional controllers; using distributed consensus iteration to identify the maximum harmonic source position; distributed calculation is realized by establishing a tissue enzyme numerical membrane system, and then a multi-agent tissue enzyme numerical membrane system model is established to cooperatively control and suppress harmonic waves by using the residual capacity of a photovoltaic grid-connected inverter. The invention provides a novel method for realizing harmonic suppression of the power distribution network through distributed control, the state convergence rate of the system can be effectively improved, and the method can adjust the communication of the controller, reduce the transmission of data and save the calculation and resources of the system.

The invention relates to a strategy determination method and electronic equipment, which are used for improving the strategy determination accuracy. Comprising the following steps: inputting a to-be-processed pulse signal into a decision model; wherein the decision model comprises two first modules and a Purkinje cell layer, and any one of the first modules comprises a moss fiber layer and a granular cell layer; for any first module, inputting a to-be-processed pulse signal to the granular cell layer through the moss fiber layer; performing high-dimensional expansion processing and sparse processing on the pulse signal to be processed by using the granular cell layer to obtain an intermediate pulse signal; the Purkinje cell layer is used for prolonging the time scale of instantaneous input stimulation and gradually accumulating stimulation on intermediate pulse signals obtained by the two first modules respectively, and working memory is realized by self-discharge of two groups of Purkinje neurons in the Purkinje cell layer. And strong mutual inhibition between the two groups of Purkinje neurons is utilized to complete a win-win eating process, and a target decision is obtained.

Analysis system (100) showing: an electrochemical sensor device (110) for detecting a respective property of several markers (204) in a sample (201), wherein the electrochemical sensor device (110) is configured to provide a voltage signal for each of the multiple markers (204), wherein the respective voltage signal represents the property of the respective marker (204) associated with the voltage signal, wherein the electrochemical sensor device (110) comprises several organic electrochemical transistors (111), wherein each of the several organic electrochemical transistors (111) is sensitive with respect to a respective marker of the several markers (204), and wherein the voltage signal is generated based on an electrical property of the respective organic electrochemical transistor (111); a nonlinear electronic circuit (120) based on a polymer network (121) for modifying the multiple voltage signals as they pass through the nonlinear electronic circuit (120); and an analysis device (130) which is set up to analyze the multiple voltage signals and / or the multiple modified voltage signals to determine a property of the sample (201) based on the respective properties of the multiple markers (204).

Systems and techniques for producing genetically engineered ion channels (ICs) with bioluminescence resonant energy transfer (BRET) complexes and using such ion channels for efficient readout of neural activity and outputs of networks of biological neurons are described. In one embodiment, the disclosed techniques include identifying a target location in an IC, to host a target protein including a donor tag protein and an acceptor tag protein and modifying a genome of the neuron cell in a part associated with the target location in the IC. The techniques further include causing the neuron cell to express the target protein into the IC according to the modified genome. In a first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein associated with absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.

The invention discloses a cognitive calculation method and system based on a biological neural network. A biological neural network BNN core unit comprises a nerve cell culture, an input stimulation unit and an output readout unit, and can be controlled through various life cycles of the nerve cell culture, so as to provide a data processing function. An automated system includes environmental and chemical controller units adapted to operate with BNN stimulation and readout data interfaces, facilitating monitoring and adjustment of BNN core unit parameters. The pre-processing and post-processing of the BNN interface signal can further promote the training and reinforcement learning of the BNN. A plurality of BNN core units may also be assembled together as a stack. The proposed system provides a BNN operating system as a core component for a wet piece server to receive, process, and send data for different client applications without exposing the BNN core unit component to client users, while improving the performance of the system as compared to conventional silicon-based hardware and software information processing for advanced cognitive computing tasks. And obviously less energy is needed.

A nano computing device includes: a nanoparticle memory including a first molecule bound so as to store a molecular input; a nanoparticle reporter including a second molecule bound so as to generate an output; and a nanoparticle floater including at least two third molecules and fourth molecules so as to be bound to one of the nanoparticle memory and the nanoparticle reporter based on the molecular input and an instruction molecule.

Disclosed are metalloproteins (e.g, vanadyl-substituted metalloproteins), such as metal-substituted rubredoxins including vanadyl-substituted rubredoxins. These metalloproteins can be used in quantum computing applications, for example, as quantum bits (qubits) or quantum dits (qudits).

A method 800 for making molecular computations comprises: i) providing a mixture 100 comprising a scaffold 110 comprising N scaffold positions 120; ii) designing a set of computing tiles to drive a desired computation, the set of computing tiles comprising at least N different computing tile types, wherein the computing tile types are selected to be used for the computation, in such a way that a target output has a higher probability of being reached than the probability for any other potential output, wherein for each computing tile 140, a bottom position domain 160 is arranged to bind directly to a matching scaffold position 120 with a first binding strength, and to the compute domains 150 of other computing tiles 140 with a set of second binding strengths, which are each weaker than the first binding strength; iii) adding the designed set of computing tiles to the mixture 100; iv) allowing computing tiles 140 to bind to scaffold positions 120 until all scaffold positions 120 required for the computation have been filled; v) allowing replacement of all mismatched computing tiles 140, based on correct compute domain bindings being enthalpically favoured over incorrect compute domain bindings; and vi) reaching an output configuration.

The application belongs to the field of biological computer technology and DNA nanotechnology, and particularly relates to a logic element based on a DNA nanostructure and application of the logic element in manufacturing a true random number generator. The logic element based on the DNA nanostructure takes a two-dimensional cross DNA nanostructure as a main body of the element, utilizes basic characteristics of the DNA nanostructure, such as uniform size, accurate site programming and rich extensible edge chains, constructs logic elements carrying different information through site programming, takes the extensible edge chains as sticky ends to realize a stable and reliable coaxial physical connection relationship between the logic elements, and only when there is a complementary relationship between the sticky ends of the logic elements, can a dimer or a multimer be formed through self-assembly. Therefore, when the logic element is applied to manufacturing the random number generator, the multimer connected by the cross paper folds still has good rigidity, and the correctness of the random generation result is ensured.

Systems and techniques for producing genetically engineered ion channels (ICs) with bioluminescence resonant energy transfer (BRET) complexes and using such ion channels for efficient readout of neural activity and outputs of networks of biological neurons are described. In one embodiment, the disclosed techniques include identifying a target location in an IC, to host a target protein including a donor tag protein and an acceptor tag protein and modifying a genome of the neuron cell in a part associated with the target location in the IC. The techniques further include causing the neuron cell to express the target protein into the IC according to the modified genome. In a first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein associated with absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.

The main object of the present technology is to provide a technology for automatically proposing a good combination of fluorescent dye-labeled antibodies. The present technology provides an information processing apparatus including a processing unit that generates a combination list of fluorescent dyes with respect to a biological molecule based on an expression level category that classifies a plurality of biological molecules for analyzing a sample according to respective expression levels in the sample, a brightness category that classifies a plurality of fluorescent dyes for analyzing the sample according to brightness, and correlation information between the plurality of fluorescent dyes. The processing unit selects fluorescent dyes to be assigned to the biological molecule in the combination list from among the fluorescent dyes belonging to the brightness category associated with the expression level category to which the biological molecule belongs.

The application provides a facade reconstruction module material combination optimization method based on a genetic algorithm and a polycephalocystis algorithm.The method comprises the following steps: step one, dividing material categories;step two, establishing a genetic algorithm model;step three, based on the obtained core cavity distribution scheme, generating a path in the core cavity distribution framework based on the polycephalocystis algorithm, so as to form the solid material part and other cavity parts, thereby providing support for the core filling material and reserving space for other filling materials; and step four, combination assembly.The advanced materials including phase change materials, air bags and the like and the thermal insulation materials are deeply combined with the intelligent algorithm, the integrated design of the external wall reconstruction module is realized, the requirements of the thermal insulation effect, the material utilization rate and the construction cost and the like are taken into account, and the method has remarkable popularization and application value.

To provide a method, system and ecosystem security platform for implementing an application-specific security architecture based on a genome network topology interoperable with modern application and network stacks.SOLUTION: The CG technology is supported by a wide range of digital capability platforms and component configurations and is configured to ensure the orderly execution of important computationally complex genomic construction and digital DNA regulatory functions and processes, and the CG shows computational complexity by enabling virtual non-constraint by generating an information-theoretically constructed genome structure. CG digital rendering greatly expands the range of its unique differences and correlations while maintaining the biochemically unlimited properties, and CG retains the ability to strategically control different digital DNA-based genome structures without compromising computational integrity.SELECTED DRAWING: Figure 1

The application discloses a kind of based on DNA chain replacement's delayed enzymatic reaction ultra-sensitive Brink control implementation method, comprising: with single molecule and bimolecular chemical reaction to describe enzymatic reaction process, introduce time delay factor, obtain the enzymatic reaction process model with time delay;Brink controller based on CRN is constructed;The static mapping expression between the output of the Brink controller and the system output under steady-state condition is obtained, and then the analytical condition for ensuring the performance of the controller is obtained;The construction of Brink controller is realized using DNA strand displacement reaction;Through DNA strand displacement mechanism, and based on delay material and compensation mechanism, obtain time delay representation, apply the time delay representation to the DNA implementation of enzymatic reaction process model;At the same time, in combination with the constructed Brink controller, the control of rewritten enzymatic reaction process model is realized.The application does not involve subtraction operation in structure, reduces the number of abstract chemical reactions required for implementation, greatly simplifies the complexity of DNA implementation.

A regulatory network machine, including: a dissipative dynamic system as a regulatory network including a plurality of nodes and directed edges, in which the plurality of edges connects at least two nodes in the plurality of nodes, and the plurality of edges determines how the connected nodes interact, in which the dissipative dynamic system occupies an initial state; a plurality of inputs to the dissipative dynamic system, in which applying an input in the plurality of inputs to the dissipative dynamic system transitions the dynamic system from the initial state to corresponding equilibrium state, in which the corresponding equilibrium state is based on the input in the plurality of inputs and the initial state; and a finite state machine which includes a record of the plurality of inputs, the corresponding equilibrium state to each input in the plurality of inputs, and a map of possible transitions between equilibrium states.

This disclosure provides techniques and systems for using neuronal cell cultures such as organoids as a testing platform for drug development. The neuronal cell cultures are trained to perform goal-oriented behavioral tasks. The ability of the neurons to perform a task is identified through connections to a computing device that provides inputs and measures outputs from the neurons. The neuronal cell culture is then exposed to a potential drug while evaluating its ability to perform the task. The performance of the neuronal cell culture in the presence and absence of the drug is compared and this provides the basis for evaluating the effects of the drug. Better performance on the task may indicate efficacy of the drug while decreased competency decreased competency may indicate toxicity. Personalized testing can be provided by using induced pluripotent stem cells generated from a patient's own somatic cells.