Nucleic acid three-dimensional structure determination method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a nucleic acid three-dimensional structure determination method and device, electronic equipment and storage medium. The electronic density map of the target nucleic acid is determined, and a plurality of cubes are obtained by sliding in the electronic density map according to a preset three-dimensional sliding window with a preset step. The nucleic acid classification, skeleton classification, atom classification and nucleotide classification are respectively performed on each cube to obtain the corresponding nucleic acid classification result, skeleton classification result, atom classification result and nucleotide classification result, so as to determine the nucleic acid classification matrix, skeleton classification matrix, atom classification matrix and nucleotide classification matrix. The three-dimensional structure of the target nucleic acid is determined according to the nucleic acid classification matrix, skeleton classification matrix, atom classification matrix and nucleotide classification matrix. The present disclosure can extract the information of the nucleic acid in multiple dimensions by using the electronic density map, and automatically reconstruct the three-dimensional structure of the nucleic acid according to the information in multiple dimensions, thereby improving the accuracy of the reconstruction result.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, electronic device, and storage medium for determining the three-dimensional structure of nucleic acids. Background Technology

[0002] As carriers and transmitters of genetic information within organisms, nucleic acids dominate multiple life processes, including protein synthesis, gene delivery, and cell signal transduction. Studying and understanding the spatial structure of nucleic acids is crucial for comprehending their biological functions and for developing corresponding drugs. Cryo-electron microscopy (cryo-EM) is an important tool for obtaining nucleic acid structures. By illuminating biomolecule samples through an electron microscope, electron density maps can be obtained. Structural biologists can then further analyze these electron density maps to reconstruct the nucleic acid structure. However, this traditional manual analysis method relies on human experience and effort, thus limiting its application. In recent years, research on how to use computer algorithms to replace manual analysis methods to achieve automated reconstruction of nucleic acid or nucleic acid complex structures has attracted widespread attention. In particular, image recognition technology in artificial intelligence has shown considerable promise for this task. Summary of the Invention

[0003] In view of this, this disclosure proposes a method, apparatus, electronic device and storage medium for determining the three-dimensional structure of nucleic acids, which aims to automatically identify and reconstruct the three-dimensional structure of nucleic acids based on the electron density map of nucleic acids.

[0004] According to a first aspect of this disclosure, a method for determining the three-dimensional structure of a nucleic acid is provided, the method comprising:

[0005] Determine the electron density map of the target nucleic acid;

[0006] A three-dimensional sliding window of a fixed size is determined, and multiple cubes are obtained by sliding the three-dimensional sliding window in the electron density map with a preset step size;

[0007] Each cube is classified into nucleic acid, backbone, atom, and nucleotide categories to obtain the corresponding nucleic acid classification results, backbone classification results, atom classification results, and nucleotide classification results.

[0008] The nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix are determined based on the nucleic acid classification results, backbone classification results, atom classification results, and nucleotide classification results corresponding to each cube, as well as their positions in the electron density map.

[0009] The three-dimensional structure of the target nucleic acid is determined based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix.

[0010] In one possible implementation, determining the electron density map of the target nucleic acid includes:

[0011] Candidate density maps are obtained by acquiring cryo-electron microscopy images of the target nucleic acid;

[0012] The candidate density map is processed to obtain the electron density map to be processed.

[0013] In one possible implementation, the image processing of the candidate density map to obtain the electron density map to be processed includes:

[0014] The candidate density map is cropped to obtain an image of the region where the three-dimensional point cloud of the target nucleic acid is located;

[0015] The region image is normalized to obtain the electron density map to be processed.

[0016] In one possible implementation, the length, width, and height of the fixed dimension are all preset lengths, and the preset lengths are greater than the preset step size.

[0017] In one possible implementation, the step of classifying each cube separately into nucleic acid, backbone, atom, and nucleotide categories to obtain corresponding nucleic acid classification results, backbone classification results, atom classification results, and nucleotide classification results includes:

[0018] Each cube is input into a pre-trained nucleic acid classification model, which determines the type of nucleic acid in the cube and outputs the corresponding nucleic acid classification result.

[0019] Each cube is input into a pre-trained backbone classification model. The backbone classification model determines whether the cube contains a nucleic acid backbone and outputs the corresponding backbone classification result.

[0020] Each cube is input into a pre-trained atom classification model, which determines whether the cube contains a P atom and outputs the corresponding atom classification result.

[0021] Each cube is input into a pre-trained nucleotide classification model, which determines the types of nucleotides included in the cube and outputs the corresponding nucleotide classification results.

[0022] In one possible implementation, determining the three-dimensional structure of the target nucleic acid based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix includes:

[0023] The nucleic acid type of the target nucleic acid is determined based on the nucleic acid classification matrix;

[0024] The three-dimensional structure of the target nucleic acid is determined based on the backbone classification matrix, atom classification matrix, nucleotide classification matrix, and nucleic acid type.

[0025] In one possible implementation, determining the three-dimensional structure of the target nucleic acid based on the backbone classification matrix, the atom classification matrix, the nucleotide classification matrix, and the nucleic acid type includes:

[0026] Determine the corresponding nucleic acid density range based on the described nucleic acid type;

[0027] The coordinates of multiple P atoms included in the target nucleic acid are determined based on the atom classification matrix;

[0028] The atomic density between every two adjacent P atom coordinates is determined based on the skeleton classification matrix;

[0029] In response to the atomic density being within the range of the nucleic acid density, two of the P atoms are linked to obtain a P atom chain;

[0030] The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix, and the three-dimensional structure of the target nucleic acid is determined based on the nucleotide type corresponding to each P atom in the P atom chain.

[0031] In one possible implementation, determining the atomic density between every two adjacent P-atom coordinates based on the skeleton classification matrix includes:

[0032] Identify the P atoms adjacent to each of the P atoms;

[0033] For every two adjacent P-atom coordinates, the mean of the skeleton classification results between the corresponding positions in the skeleton classification matrix is ​​calculated to obtain the corresponding atom density.

[0034] In one possible implementation, determining the P atoms adjacent to each of the P atoms includes:

[0035] Determine the distance between every two P atoms in the plurality of P atoms;

[0036] For a target P atom, at least one P atom is identified as an adjacent P atom among P atoms that are at a distance less than a preset distance.

[0037] In one possible implementation, determining the three-dimensional structure of the target nucleic acid based on the backbone classification matrix, the atom classification matrix, the nucleotide classification matrix, and the nucleic acid type further includes:

[0038] In response to the existence of a preset number of skeleton classification results less than a confidence threshold between the corresponding positions of the adjacent P-atom coordinates in the skeleton classification matrix, the connection between the adjacent P-atoms is disconnected.

[0039] In one possible implementation, determining the nucleotide type corresponding to each P atom in the P atom chain based on the nucleotide classification matrix, and determining the three-dimensional structure of the target nucleic acid based on the nucleotide type corresponding to each P atom in the P atom chain, includes:

[0040] The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix.

[0041] In response to the target nucleic acid being RNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain to obtain the three-dimensional structure of the target nucleic acid;

[0042] In response to the target nucleic acid being DNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, and the matching bases in two P atom chains are linked to obtain the three-dimensional structure of the target nucleic acid.

[0043] According to a second aspect of this disclosure, a nucleic acid three-dimensional structure determination apparatus is provided, the apparatus comprising:

[0044] The image determination module is used to determine the electron density map of the target nucleic acid;

[0045] An image segmentation module is used to determine a three-dimensional sliding window of a fixed size, and to obtain multiple cubes by sliding the three-dimensional sliding window in the electron density map with a preset step size;

[0046] The feature classification module is used to classify each cube by nucleic acid, backbone, atom and nucleotide respectively, and obtain the corresponding nucleic acid classification results, backbone classification results, atom classification results and nucleotide classification results;

[0047] The matrix determination module is used to determine the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix respectively based on the nucleic acid classification results, backbone classification results, atom classification results, nucleotide classification results corresponding to each cube, and their positions in the electron density map;

[0048] The structure reconstruction module is used to determine the three-dimensional structure of the target nucleic acid based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix.

[0049] In one possible implementation, the image determination module is further configured to:

[0050] Candidate density maps are obtained by acquiring cryo-electron microscopy images of the target nucleic acid;

[0051] The candidate density map is processed to obtain the electron density map to be processed.

[0052] In one possible implementation, the image determination module is further configured to:

[0053] The candidate density map is cropped to obtain an image of the region where the three-dimensional point cloud of the target nucleic acid is located;

[0054] The region image is normalized to obtain the electron density map to be processed.

[0055] In one possible implementation, the length, width, and height of the fixed dimension are all preset lengths, and the preset lengths are greater than the preset step size.

[0056] In one possible implementation, the feature classification module is further configured to:

[0057] Each cube is input into a pre-trained nucleic acid classification model, which determines the type of nucleic acid in the cube and outputs the corresponding nucleic acid classification result.

[0058] Each cube is input into a pre-trained backbone classification model. The backbone classification model determines whether the cube contains a nucleic acid backbone and outputs the corresponding backbone classification result.

[0059] Each cube is input into a pre-trained atom classification model, which determines whether the cube contains a P atom and outputs the corresponding atom classification result.

[0060] Each cube is input into a pre-trained nucleotide classification model, which determines the types of nucleotides included in the cube and outputs the corresponding nucleotide classification results.

[0061] In one possible implementation, the structure reconstruction module is further configured to:

[0062] The nucleic acid type of the target nucleic acid is determined based on the nucleic acid classification matrix;

[0063] The three-dimensional structure of the target nucleic acid is determined based on the backbone classification matrix, atom classification matrix, nucleotide classification matrix, and nucleic acid type.

[0064] In one possible implementation, the structure reconstruction module is further configured to:

[0065] Determine the corresponding nucleic acid density range based on the described nucleic acid type;

[0066] The coordinates of multiple P atoms included in the target nucleic acid are determined based on the atom classification matrix;

[0067] The atomic density between every two adjacent P atom coordinates is determined based on the skeleton classification matrix;

[0068] In response to the atomic density being within the range of the nucleic acid density, two of the P atoms are linked to obtain a P atom chain;

[0069] The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix, and the three-dimensional structure of the target nucleic acid is determined based on the nucleotide type corresponding to each P atom in the P atom chain.

[0070] In one possible implementation, the structure reconstruction module is further configured to:

[0071] Identify the P atoms adjacent to each of the P atoms;

[0072] For every two adjacent P-atom coordinates, the mean of the skeleton classification results between the corresponding positions in the skeleton classification matrix is ​​calculated to obtain the corresponding atom density.

[0073] In one possible implementation, the structure reconstruction module is further configured to:

[0074] Determine the distance between every two P atoms in the plurality of P atoms;

[0075] For a target P atom, at least one P atom is identified as an adjacent P atom among P atoms that are at a distance less than a preset distance.

[0076] In one possible implementation, the structure reconstruction module is further configured to:

[0077] In response to the existence of a preset number of skeleton classification results less than a confidence threshold between the corresponding positions of the adjacent P-atom coordinates in the skeleton classification matrix, the connection between the adjacent P-atoms is disconnected.

[0078] In one possible implementation, the structure reconstruction module is further configured to:

[0079] The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix.

[0080] In response to the target nucleic acid being RNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain to obtain the three-dimensional structure of the target nucleic acid;

[0081] In response to the target nucleic acid being DNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, and the matching bases in two P atom chains are linked to obtain the three-dimensional structure of the target nucleic acid.

[0082] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0083] According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.

[0084] According to a fifth aspect of this disclosure, a computer program product is provided, including computer-readable code or a non-volatile computer-readable storage medium carrying the computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0085] In this embodiment, the electron density map of the target nucleic acid is determined, and multiple cubes are obtained by sliding a preset three-dimensional sliding window across the electron density map with a preset step size. Each cube is classified into nucleic acid, backbone, atom, and nucleotide categories to obtain corresponding nucleic acid, backbone, atom, and nucleotide classification results, thereby determining the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix. The three-dimensional structure of the target nucleic acid is determined based on these matrices. This disclosure can utilize electron density maps to extract information about nucleic acids in multiple dimensions and automatically reconstruct the three-dimensional structure of nucleic acids based on this multi-dimensional information, improving the accuracy of the reconstruction results.

[0086] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0087] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0088] Figure 1 A flowchart illustrating a method for determining the three-dimensional structure of nucleic acids according to an embodiment of the present disclosure is shown.

[0089] Figure 2 A schematic diagram of a cube sorting process according to an embodiment of the present disclosure is shown;

[0090] Figure 3 A schematic diagram of a nucleic acid three-dimensional structure determination apparatus according to an embodiment of the present disclosure is shown;

[0091] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0092] Figure 5 A schematic diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0093] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0094] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0095] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0096] In one possible implementation, the nucleic acid three-dimensional structure determination method of this disclosure can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. Any electronic device can implement the nucleic acid three-dimensional structure determination method of this disclosure by having its processor call computer-readable instructions stored in its memory.

[0097] Figure 1A flowchart illustrating a method for determining the three-dimensional structure of a nucleic acid according to an embodiment of this disclosure is shown. Figure 1 As shown, the method for determining the three-dimensional structure of nucleic acids in this embodiment may include the following steps S10-S50.

[0098] Step S10: Determine the electron density map of the target nucleic acid.

[0099] In one possible implementation, an electronic device determines the electron density map of the target nucleic acid to be processed. The electron density map is a three-dimensional point cloud image of the target nucleic acid, i.e., a point cloud image obtained by acquiring the three-dimensional point cloud of the target nucleic acid. The target nucleic acid can be imaged using a cryo-electron microscope. The electron density map acquired by the electronic device can be a three-dimensional point cloud image of the target nucleic acid obtained directly by irradiating it with a cryo-electron microscope, or it can be obtained by acquiring a three-dimensional point cloud image of the target nucleic acid sent by another device to obtain the electron density map to be processed.

[0100] Optionally, the electronic device can directly determine that the acquired 3D point cloud image is an electron density map. Alternatively, the electronic device can also process the received 3D point cloud image to obtain an electron density map. For example, the electronic device can acquire a cryo-electron microscopy image of the target nucleic acid to obtain a candidate density map, and then perform image processing on the candidate density map to obtain the electron density map to be processed. The image processing process can include cropping, normalization, etc., that is, the candidate density map can be cropped to obtain an image of the region where the 3D point cloud of the target nucleic acid is located. The region image is then normalized to obtain the electron density map to be processed. The cropping process is used to remove invalid information and background information from the candidate density map, and the normalization process can be used to convert the pixel value of each voxel in the density map to a value between 0 and 1.

[0101] Step S20: Determine a three-dimensional sliding window of a fixed size, and obtain multiple cubes by sliding the three-dimensional sliding window in the electron density map with a preset step size.

[0102] In one possible implementation, after acquiring the electron density map to be processed, the electronic device can segment the electron density map by sliding a preset three-dimensional sliding window across it, thereby acquiring multiple cubes containing local information from the electron density map. The preset three-dimensional sliding window has a fixed size; its length, width, and height can be preset lengths, meaning the three-dimensional sliding window can be a cube with a preset side length. To ensure complete acquisition of all regions of the electron density map, the preset length set by the electronic device can be greater than the preset step size used to slide the three-dimensional sliding window.

[0103] Optionally, after determining the three-dimensional sliding window, the electronic device slides across the electron density map with a preset step size to acquire multiple cubes of the same size as the three-dimensional sliding window. For example, if the electronic device determines the size of the three-dimensional sliding window to be 64×64×64 and the preset step size to be 57, it can acquire one cube every 57 voxels in three directions, starting from a preset position on the electron density map. This results in multiple 64×64×64 cubes, with adjacent cubes overlapping by 7 voxels. If the size of the acquired cube is less than 64×64×64 due to the sliding position being at the edge of the electron density map, the cubes are filled to achieve a size of 64×64×64.

[0104] Step S30: Perform nucleic acid classification, backbone classification, atom classification and nucleotide classification on each cube to obtain the corresponding nucleic acid classification results, backbone classification results, atom classification results and nucleotide classification results.

[0105] In one possible implementation, after the electronic device segments the electron density map to obtain multiple cubes, it can classify each cube separately into nucleic acid, backbone, atom, and nucleotide categories, obtaining corresponding nucleic acid, backbone, atom, and nucleotide classification results. Optionally, different classification processes can be implemented using different preset classification models. Specifically, each cube can be input into a pre-trained nucleic acid classification model, which determines the type of nucleic acid in the cube and outputs the corresponding nucleic acid classification result. Similarly, each cube can be input into a pre-trained backbone classification model, which determines whether the cube contains a nucleic acid backbone and outputs the corresponding backbone classification result. Likewise, each cube can be input into a pre-trained atom classification model, which determines whether the cube contains P atoms and outputs the corresponding atom classification result. Finally, each cube can be input into a pre-trained nucleotide classification model, which determines the type of nucleotides included in the cube and outputs the corresponding nucleotide classification result.

[0106] Optionally, nucleic acid classification results may include blank, nucleic acid, and protein. Backbone classification results may include whether it is located at a backbone position or part of the backbone position; atom classification results may include whether it is a backbone atom or not; nucleotide classification results may include cytosine C, uracil U, adenine A, guanine G, and thymine T.

[0107] Figure 2 A schematic diagram of a cube sorting process according to an embodiment of the present disclosure is shown. Figure 2As shown, the embodiments of the present disclosure can respectively process the classification tasks of nucleic acid classification, backbone classification, atom classification, and nucleotide classification through four classification models. Among them, the architecture of the classification model can be a Unet model. Optionally, the Unet model includes two parts: an encoder and a decoder. The encoder contains 4 layers of convolution, each layer of convolution uses a 3x3 convolution kernel, and the convolutions are connected by a 2x2 max pooling layer. The role of the decoder is to decode the feature map of the encoder into an output image. The first layer of the decoder receives the output of the last layer of the encoder, then performs a 2x2 upsampling operation, and uses a convolutional layer to extract features. Each layer is connected to the corresponding layer of the encoder to extract the features of the encoder.

[0108] Optionally, the sample data input during the training process of different classification models can be the same, but the corresponding labeled data is different. Among them, the labeled data corresponding to the nucleic acid classification model can be determined according to the three-dimensional coordinates of the P atom and the nucleotide type it represents. For example, it can be determined that when the ratio of the nucleotide types T and U at the P atom coordinates is greater than 5, the density map is judged to be DNA, and vice versa is RNA. In addition, if 0.2 < T / U < 5, it is determined that the density map contains both DNA and RNA.

[0109] Step S40: Determine a nucleic acid classification matrix, a backbone classification matrix, an atom classification matrix, and a nucleotide classification matrix respectively according to the nucleic acid classification result, backbone classification result, atom classification result, nucleotide classification result corresponding to each cube, and the position in the electron density map.

[0110] In a possible implementation manner, after the electronic device determines the nucleic acid classification result, backbone classification result, atom classification result, and nucleotide classification result corresponding to each cube in the electron density map, it arranges the nucleic acid classification results according to the position of the cube in the electron density map to obtain a nucleic acid classification matrix, arranges the backbone classification results to obtain a backbone classification matrix, arranges the atom classification results to obtain an atom classification matrix, and arranges the nucleotide classification results to obtain a nucleotide classification matrix. Each matrix is a three-dimensional matrix.

[0111] Step S50: Determine the three-dimensional structure of the target nucleic acid according to the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix.

[0112] In one possible implementation, after obtaining the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix, the electronic device can first determine the nucleic acid type of the target nucleic acid based on the nucleic acid classification matrix, and then determine the three-dimensional structure of the target nucleic acid based on the backbone classification matrix, atom classification matrix, nucleotide classification matrix, and nucleic acid type. The nucleic acid type of the target nucleic acid can be determined by calculating the eigenvalues ​​corresponding to the nucleic acid classification matrix. For example, the mean, variance, or standard deviation of each voxel value in the nucleic acid classification matrix can be calculated as the corresponding eigenvalue, and the nucleic acid type of the target nucleic acid can be determined based on the eigenvalues.

[0113] Optionally, after determining the nucleic acid type, the electronic device can connect P atoms according to a preset connection rule to obtain the three-dimensional structure of the target nucleic acid. This can include the distance between P atoms. Furthermore, it preferentially links to nearby points. The average framework density is >0.85. There cannot be more than two discrete points between two P atoms with a framework density <0.2. And a P atom can only be connected to two P atoms, one before and one after.

[0114] For example, after determining the nucleic acid type of the target nucleic acid, the electronic device can first determine the corresponding nucleic acid density range based on the nucleic acid type, and then determine the coordinates of multiple P atoms included in the target nucleic acid based on the atomic classification matrix. The atomic density between every two adjacent P atom coordinates is determined based on the backbone classification matrix. In response to the atomic density being within the nucleic acid density range, two P atoms are connected to obtain a P atom chain. The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix, and the three-dimensional structure of the target nucleic acid is determined based on the nucleotide type corresponding to each P atom in the P atom chain. The method for determining the atomic density between adjacent P atoms can be as follows: first, determine the P atoms adjacent to each P atom, and for every two adjacent P atom coordinates, calculate the average backbone classification result between corresponding positions in the backbone classification matrix to obtain the corresponding atomic density.

[0115] Optionally, adjacent P atoms can be determined by calculating the distance between P atoms. That is, the electronic device can determine the distance between every two P atoms in a plurality of P atoms. For the target P atom, at least one P atom is identified as an adjacent P atom among P atoms whose distance is less than a preset distance.

[0116] Furthermore, the electronic device can also disconnect the connection between adjacent P atoms if there are a preset number of backbone classification results less than a confidence threshold between the corresponding positions of adjacent P atom coordinates in the backbone classification matrix. For different nucleic acid types, the electronic device can also connect bases according to the type. That is, the nucleotide type corresponding to each P atom in the P atom chain is determined according to the nucleotide classification matrix. If the target nucleic acid is RNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, thus obtaining the three-dimensional structure of the target nucleic acid. If the target nucleic acid is DNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, and the matching bases in two P atom chains are connected, thus obtaining the three-dimensional structure of the target nucleic acid.

[0117] Based on the above technical features, the embodiments of this disclosure can use electron density maps to extract information about nucleic acids in multiple dimensions, and automatically reconstruct the three-dimensional structure of nucleic acids based on the information in multiple dimensions, thereby improving the efficiency of the nucleic acid structure reconstruction process and the accuracy of the reconstruction results.

[0118] Figure 3 A schematic diagram of a nucleic acid three-dimensional structure determination apparatus according to an embodiment of the present disclosure is shown. Figure 3 As shown, the nucleic acid three-dimensional result determination device according to an embodiment of this disclosure includes:

[0119] Image determination module 30 is used to determine the electron density map of the target nucleic acid;

[0120] Image segmentation module 31 is used to determine a three-dimensional sliding window of a fixed size, and to obtain multiple cubes by sliding the three-dimensional sliding window in the electron density map with a preset step size;

[0121] The feature classification module 32 is used to classify each cube by nucleic acid, backbone, atom and nucleotide respectively, and obtain the corresponding nucleic acid classification results, backbone classification results, atom classification results and nucleotide classification results;

[0122] The matrix determination module 33 is used to determine the nucleic acid classification matrix, backbone classification matrix, atom classification matrix and nucleotide classification matrix respectively based on the nucleic acid classification result, backbone classification result, atom classification result and nucleotide classification result corresponding to each cube and their positions in the electron density map;

[0123] The structure reconstruction module 34 is used to determine the three-dimensional structure of the target nucleic acid based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix and nucleotide classification matrix.

[0124] In one possible implementation, the image determination module 30 is further configured to:

[0125] Candidate density maps are obtained by acquiring cryo-electron microscopy images of the target nucleic acid;

[0126] The candidate density map is processed to obtain the electron density map to be processed.

[0127] In one possible implementation, the image determination module 30 is further configured to:

[0128] The candidate density map is cropped to obtain an image of the region where the three-dimensional point cloud of the target nucleic acid is located;

[0129] The region image is normalized to obtain the electron density map to be processed.

[0130] In one possible implementation, the length, width, and height of the fixed dimension are all preset lengths, and the preset lengths are greater than the preset step size.

[0131] In one possible implementation, the feature classification module 32 is further configured to:

[0132] Each cube is input into a pre-trained nucleic acid classification model, which determines the type of nucleic acid in the cube and outputs the corresponding nucleic acid classification result.

[0133] Each cube is input into a pre-trained backbone classification model. The backbone classification model determines whether the cube contains a nucleic acid backbone and outputs the corresponding backbone classification result.

[0134] Each cube is input into a pre-trained atom classification model, which determines whether the cube contains a P atom and outputs the corresponding atom classification result.

[0135] Each cube is input into a pre-trained nucleotide classification model, which determines the types of nucleotides included in the cube and outputs the corresponding nucleotide classification results.

[0136] In one possible implementation, the structure reconstruction module 34 is further configured to:

[0137] The nucleic acid type of the target nucleic acid is determined based on the nucleic acid classification matrix;

[0138] The three-dimensional structure of the target nucleic acid is determined based on the backbone classification matrix, atom classification matrix, nucleotide classification matrix, and nucleic acid type.

[0139] In one possible implementation, the structure reconstruction module 34 is further configured to:

[0140] Determine the corresponding nucleic acid density range based on the described nucleic acid type;

[0141] The coordinates of multiple P atoms included in the target nucleic acid are determined based on the atom classification matrix;

[0142] The atomic density between every two adjacent P atom coordinates is determined based on the skeleton classification matrix;

[0143] In response to the atomic density being within the range of the nucleic acid density, two of the P atoms are linked to obtain a P atom chain;

[0144] The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix, and the three-dimensional structure of the target nucleic acid is determined based on the nucleotide type corresponding to each P atom in the P atom chain.

[0145] In one possible implementation, the structure reconstruction module 34 is further configured to:

[0146] Identify the P atoms adjacent to each of the P atoms;

[0147] For every two adjacent P-atom coordinates, the mean of the skeleton classification results between the corresponding positions in the skeleton classification matrix is ​​calculated to obtain the corresponding atom density.

[0148] In one possible implementation, the structure reconstruction module is further configured to:

[0149] Determine the distance between every two P atoms in the plurality of P atoms;

[0150] For a target P atom, at least one P atom is identified as an adjacent P atom among P atoms that are at a distance less than a preset distance.

[0151] In one possible implementation, the structure reconstruction module 34 is further configured to:

[0152] In response to the existence of a preset number of skeleton classification results less than a confidence threshold between the corresponding positions of the adjacent P-atom coordinates in the skeleton classification matrix, the connection between the adjacent P-atoms is disconnected.

[0153] In one possible implementation, the structure reconstruction module 34 is further configured to:

[0154] The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix.

[0155] In response to the target nucleic acid being RNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain to obtain the three-dimensional structure of the target nucleic acid;

[0156] In response to the target nucleic acid being DNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, and the matching bases in two P atom chains are linked to obtain the three-dimensional structure of the target nucleic acid.

[0157] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0158] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0159] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0160] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0161] Figure 4 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0162] Reference Figure 4 The electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0163] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0164] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0165] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0166] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0167] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0168] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0169] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0170] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0171] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0172] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0173] Figure 5 A schematic diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 5 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0174] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0175] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0176] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0177] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0178] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0179] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0180] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0181] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0182] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0183] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0184] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining the three-dimensional structure of nucleic acids, characterized in that, The method includes: Determine the electron density map of the target nucleic acid; A three-dimensional sliding window of a fixed size is determined, and multiple cubes are obtained by sliding the three-dimensional sliding window in the electron density map with a preset step size; Each cube is classified into nucleic acid, backbone, atom, and nucleotide categories to obtain the corresponding nucleic acid classification results, backbone classification results, atom classification results, and nucleotide classification results. The nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix are determined based on the nucleic acid classification results, backbone classification results, atom classification results, and nucleotide classification results corresponding to each cube, as well as their positions in the electron density map. The three-dimensional structure of the target nucleic acid is determined based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix. The determination of the three-dimensional structure of the target nucleic acid based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix includes: The nucleic acid type of the target nucleic acid is determined based on the nucleic acid classification matrix; Determine the corresponding nucleic acid density range based on the described nucleic acid type; The coordinates of multiple P atoms included in the target nucleic acid are determined based on the atom classification matrix; Identify the P atoms adjacent to each of the P atoms; For every two adjacent P-atom coordinates, the mean value of the skeleton classification results between the corresponding positions in the skeleton classification matrix is ​​calculated to obtain the corresponding atom density; In response to the atomic density being within the range of the nucleic acid density, two of the P atoms are linked to obtain a P atom chain; The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix. In response to the target nucleic acid being RNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain to obtain the three-dimensional structure of the target nucleic acid; In response to the target nucleic acid being DNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, and the matching bases in two P atom chains are linked to obtain the three-dimensional structure of the target nucleic acid.

2. The method according to claim 1, characterized in that, The determination of the electron density map of the target nucleic acid includes: Candidate density maps are obtained by acquiring cryo-electron microscopy images of the target nucleic acid; The candidate density map is processed to obtain the electron density map to be processed.

3. The method according to claim 2, characterized in that, The step of image processing the candidate density map to obtain the electron density map to be processed includes: The candidate density map is cropped to obtain an image of the region where the three-dimensional point cloud of the target nucleic acid is located; The region image is normalized to obtain the electron density map to be processed.

4. The method according to any one of claims 1-3, characterized in that, The length, width, and height of the fixed dimensions are all preset lengths, and the preset lengths are greater than the preset step sizes.

5. The method according to any one of claims 1-3, characterized in that, The process of classifying each cube into nucleic acid, backbone, atom, and nucleotide categories to obtain corresponding nucleic acid, backbone, atom, and nucleotide classification results includes: Each cube is input into a pre-trained nucleic acid classification model, which determines the type of nucleic acid in the cube and outputs the corresponding nucleic acid classification result. Each cube is input into a pre-trained backbone classification model. The backbone classification model determines whether the cube contains a nucleic acid backbone and outputs the corresponding backbone classification result. Each cube is input into a pre-trained atom classification model, which determines whether the cube contains a P atom and outputs the corresponding atom classification result. Each cube is input into a pre-trained nucleotide classification model, which determines the types of nucleotides included in the cube and outputs the corresponding nucleotide classification results.

6. The method according to claim 1, characterized in that, The step of determining the P atoms adjacent to each of the P atoms includes: Determine the distance between every two P atoms in the plurality of P atoms; For a target P atom, at least one P atom is identified as an adjacent P atom among P atoms that are at a distance less than a preset distance.

7. The method according to claim 1, characterized in that, The step of determining the three-dimensional structure of the target nucleic acid based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix further includes: In response to the existence of a preset number of skeleton classification results less than a confidence threshold between the corresponding positions of the adjacent P-atom coordinates in the skeleton classification matrix, the connection between the adjacent P-atoms is disconnected.

8. A device for determining the three-dimensional structure of nucleic acids, characterized in that, The device includes: The image determination module is used to determine the electron density map of the target nucleic acid; An image segmentation module is used to determine a three-dimensional sliding window of a fixed size, and to obtain multiple cubes by sliding the three-dimensional sliding window in the electron density map with a preset step size; The feature classification module is used to classify each cube by nucleic acid, backbone, atom and nucleotide respectively, and obtain the corresponding nucleic acid classification results, backbone classification results, atom classification results and nucleotide classification results; The matrix determination module is used to determine the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix respectively based on the nucleic acid classification results, backbone classification results, atom classification results, nucleotide classification results corresponding to each cube, and their positions in the electron density map; The structure reconstruction module is used to determine the three-dimensional structure of the target nucleic acid based on the nucleic acid classification matrix, backbone classification matrix, atom classification matrix, and nucleotide classification matrix. The structure reconstruction module is further used for: The nucleic acid type of the target nucleic acid is determined based on the nucleic acid classification matrix; Determine the corresponding nucleic acid density range based on the described nucleic acid type; The coordinates of multiple P atoms included in the target nucleic acid are determined based on the atom classification matrix; Identify the P atoms adjacent to each of the P atoms; For every two adjacent P-atom coordinates, the mean value of the skeleton classification results between the corresponding positions in the skeleton classification matrix is ​​calculated to obtain the corresponding atom density; In response to the atomic density being within the range of the nucleic acid density, two of the P atoms are linked to obtain a P atom chain; The nucleotide type corresponding to each P atom in the P atom chain is determined based on the nucleotide classification matrix. In response to the target nucleic acid being RNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain to obtain the three-dimensional structure of the target nucleic acid; In response to the target nucleic acid being DNA, the bases in the nucleotide containing each P atom are labeled according to the nucleotide type corresponding to each P atom in the P atom chain, and the matching bases in two P atom chains are linked to obtain the three-dimensional structure of the target nucleic acid.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 7 when executing instructions stored in the memory.

10. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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