10 results about "Affinity label" patented technology

Methods and systems are described for a fast, sustainable, and automatic method to meet Cloud RAN service run-time requirements with the usage of Cloud Affinity methods. In some embodiments, for each installed RU a telecom operator can instantiate a special set of virtual functions, referred as Radio Affinity POD (RAPD), correlated with the RU in all the Cloud clusters that have compatible Cloud and Transport characteristics to host the RU relevant Distributed Unit (DU) services. The special RAPD images can contain the identification of the RU, microservices to expose the RU identity and affinity labels and, optionally, microservices to continuously monitor the communication status between the RU and the Cloud server to validate the expected latency assumptions. In certain embodiments, the RAPD can also provide additional telemetry and observability services.

A process for synthesizing and separating secretory IgA from a mixture of IgA monomer and IgA dimer is provided The process includes covalently binding affinity tagged or epitope tagged recombinant secretory component to the IgA dimer in the mixture and then binding the affinity tagged or an epitope tagged secretory IgA to immobilized moieties on the solid phase support resin to which the affinity tag or epitope tag binds and then eluting the affinity tagged or an epitope tagged secretory IgA with release buffer. A process for synthesizing and separating secretory IgM from a mixture of IgM and other plasma proteins is provided. A process is provided for inhibiting or preventing symptoms of necrotizing enterocolitis in a subject that includes the oral administration to the subject of a human polyclonal secretory IgA formed by the conjugation of human recombinant secretory component and pooled human plasma derived dimeric and polymeric.

The invention discloses a preparation method and application of a multifunctional fluorescent probe, a synthesis strategy taking allene iodine sulfonylation and then amination as a core is adopted, different functional groups can be flexibly introduced through the strategy, and the multifunctional fluorescent probe is rapidly, efficiently and modularly synthesized. The prepared fluorescent probe simultaneously has the following three main functions: function 1: through a click chemical reaction, the fluorescent probe is connected with an affinity label to realize biomolecular marking; secondly, a binding site of a potential drug and cell protein is visualized, and intracellular fluorescence imaging is achieved; and function 3: marking drug small molecules, and determining the interaction structure-function relationship between the small molecule drug and the target protein. The preparation method of the fluorescent probe is simple, reliable, green and efficient. The synthesized fluorescent probe is good in selectivity and high in biocompatibility, related instruments are convenient to operate, and the fluorescent probe is a high-sensitivity detection means in current chemiluminescence detection methods and has important potential application value.

The invention relates to the technical field of computer-aided drug design, and discloses a small sample drug virtual screening method and system based on interaction information guidance. According to the method, a prompt adaptation mechanism based on atomic-scale interaction information is introduced into a small sample medicine virtual screening task for the first time, so that the model can realize target-specific dynamic structure adaptation under the condition of a very small number of samples, and the generalization performance on a new target is remarkably improved; according to the method disclosed by the invention, the understanding capability of the model on the structure rule of the combined interface is enhanced by constructing the fine-grained interaction characteristics between the protein pockets and the small molecules, and the performance fluctuation caused by the structure difference between different target spots is weakened; the method does not need to depend on docking conformations or a large number of affinity tags, the dependence on experimental data and computing resources is reduced, and the availability of the method in a data scarce scene is improved; the method is suitable for structure-driven drug design and various virtual screening tasks, and has a good practical application prospect.

The application aims to provide a method, device and medium for obtaining a protein-small molecule evaluation model, the method comprising: inputting protein information and small molecule information as training data into a protein-small molecule evaluation model; generating corresponding atomic representation according to the protein information and the small molecule information by the protein-small molecule evaluation model, inputting the atomic representation into a distance distribution prediction model, obtaining a first distance distribution of atomic pairs with first short-range interaction output by the distance distribution prediction model; inputting the first distance distribution into a binding affinity prediction model, generating corresponding first energy term based on a preset physical energy function by the binding affinity prediction model, obtaining affinity evaluation information output by the binding affinity prediction model based on the first energy term; fitting the distance label and the binding affinity label corresponding to the training data by the protein-small molecule evaluation model, obtaining a trained protein-small molecule evaluation model.

This invention discloses a method and system for heterogeneous GPU hybrid scheduling of computing resources, belonging to the field of computer equipment technology. The key technical points include: identifying the allocation type corresponding to the task to be allocated, including single task allocation, group task allocation, and urgent task allocation; if the allocation type is group task allocation, obtaining the priority of each task in the group to be allocated; allocating the first task to a GPU node according to the priority, so that the GPU node generates an affinity label between the first task and the unallocated tasks, the affinity label responding to the identity label of the unallocated tasks; and allocating the unallocated tasks to GPU nodes according to the affinity label. This invention obtains the affinity label through a response mechanism and performs task allocation based on the affinity label, which reduces allocation time and improves scheduling efficiency compared to traversing a lookup table to obtain the affinity relationship of unallocated tasks and then performing allocation.

The application discloses a label-free purification method of influenza virus neuraminidase, a recombinant protein and application. The application provides a set of mild, stable and repeatable purification route aiming at the technical problems of difficult purification, low purity and easy conformation change of the label-free neuraminidase. The supernatant of the label-free neuraminidase expressed by CHO-k1 suspension cells is concentrated and liquid changed through ultrafiltration, and then is sequentially subjected to multistage purification through hydrophobic interaction chromatography, anion exchange chromatography and molecular sieve chromatography, so that the recombinant neuraminidase protein with a purity of greater than or equal to 94% is finally obtained. The obtained protein is a label-free natural tetramer conformation, is stable in the range of pH 5.0-9.0 and has neuraminidase activity, and can be directly used for efficient screening of influenza virus neuraminidase inhibitors. The process condition of the application is mild, the repeatability is good, the affinity label is not needed, the natural structure of the protein is retained to the maximum extent, and the application is suitable for use in scientific research and drug screening platform.

The invention relates to the technical field of artificial intelligence data processing, in particular to a collaboration method and system based on data driving. Defining a data storage mode and an affinity label according to an activity participation model, using a large model to decompose data, constructing a dynamic label according to data content, performing quantification processing on the affinity of activity participation in combination with a general label and the dynamic label, and performing quantification calculation on the affinity of activity participants and activity items; according to the method, dynamic management of activities and participating instances and functions and users is realized, association of association positioning and business collaboration of the activities and the participating instances and the functions and the users is realized, operation of triggering association functions with proximity during function operation of the users is supported, and linkage between business systems, data connection and business starting of a data driving mode are realized. The working mode that a user starts the service through menu navigation is improved, repeated input of repeated data in the system is reduced, the workload is reduced, the efficiency is improved, and the data consistency is improved.

The application provides a computing power network scheduling method, device, system, equipment and medium, and relates to the field of computing power network scheduling, which comprises the following steps: inputting a preset training task to a computing power center node to generate a model training task; receiving a model training result and selecting a target edge-end computing power node to construct an aggregated model; determining an affinity label value according to a model contribution degree, a successful rate of reporting a training task completion quantity and a successful rate of a time length spent in reporting a model training result; issuing all affinity label values to each edge-end computing power node to update the affinity label values; the model training result is determined according to the processing of the models related to each edge-end computing power node; and the model contribution degree is determined according to the model training result of each edge-end computing power node and the aggregated model. The application can improve the robustness of the model under the conditions of network congestion and unstable edge-end computing power nodes, and realize the overall optimization of application efficiency, model accuracy, network performance and energy consumption.

The invention aims to provide a method and equipment for obtaining a protein small molecule evaluation model and a medium. The method comprises the following steps: inputting protein information and small molecule information serving as training data into the protein small molecule evaluation model; generating corresponding atomic characterization according to the protein information and the micromolecule information through a protein micromolecule evaluation model, inputting the atomic characterization into a distance distribution prediction model, and obtaining first distance distribution of atom pairs with first short-range interaction output by the distance distribution prediction model; inputting the first distance distribution into a binding affinity prediction model, generating a corresponding first energy item based on a preset physical energy function through the binding affinity prediction model, and obtaining affinity evaluation information output by the binding affinity prediction model based on the first energy item; and fitting based on the distance tag corresponding to the training data and the affinity tag through a protein small molecule evaluation model to obtain a trained protein small molecule evaluation model.