Method for screening effect factors of hyphantria cunea larva saliva and female oviposition secretions

Abstract

The invention provides a screening method of alien invasive species hyphantria cunea larva saliva and female hyphantria cunea oviposition secreta effect factors. The method comprises the following steps: collecting saliva of invasive species hyphantria cunea larvae and spawning secretions of female hyphantria cunea, concentrating the saliva and the spawning secretions, performing mass spectrometry, performing KEGG and GO clustering analysis on protein information obtained by analysis, and screening possible key proteins in the protein information. The functions and effects of the effector factors in key pest-causing processes such as hyphantria cunea larva feeding and female hyphantria cunea oviposition are further explored by means of CRISPR / Cas9, RNAi and the like, screening of the effector factors of the alien invasive pests is effectively achieved, and effective means are provided for prevention of maintenance and outbreak of hyphantria cunea populations and harm of the hyphantria cunea populations to agriculture and forestry crops.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for screening effect factors of saliva of larvae of the American white moth and oviposition secretions of female insects. Background Art

[0002] The gypsy moth is a global quarantine pest and is included in my country's first list of invasive alien species.

[0003] The main reasons why the gypsy moth causes serious harm to agriculture and forestry are that it lays a large number of eggs, has a wide range of food, and its older larvae are voracious. At present, we are considering providing targeted solutions to this major invasive alien pest through genetic analysis. For example, by extracting protein information from the saliva and egg-laying secretions of the larvae, we can then screen out protein information that characterizes the characteristic genes of the invasive species and acts on the gypsy moth as effector factors, so as to develop and provide genetic control measures that can target this invasive pest without affecting local species.

[0004] In related technologies, when analyzing effect factors, the international standard gene function classification (Gene Ontology, GO) database or the Kyoto Encyclopedia of Genes and Genomes (KEGG) database is usually used as a reference, and the functional classification of one of the databases is integrated and calculated by enrichment analysis to achieve the screening of effect factors. However, most of the current enrichment analyses only consider the functional classification of one of the databases, and the screened effect factors have poor practicality. In addition, there are a few schemes that consider referring to both databases at the same time, but because the classification methods of the two databases are different and the data differences are large, most of these schemes need to set an error threshold to normalize the similar data, and the final screening results are also prone to large errors due to extreme data.

[0005] Therefore, the present application provides a method for screening effector factors of saliva of larvae and oviposition secretions of female invasive species, the gypsy moth, to solve the above-mentioned technical problems. Summary of the invention

[0006] The purpose of this application is to provide a method and system for screening effect factors of saliva of larvae of the American white moth and secretions of female oviposition, which can solve at least one of the technical problems mentioned above. The specific scheme is as follows: According to the specific implementation of the present application, in the first aspect, the present application provides a method for screening effect factors of saliva of larvae of the invasive species American white moth and oviposition secretions of female insects, comprising: Mass spectrometry analysis is performed on the saliva of gypsy moth larvae and the egg-laying secretions of female gypsy moths to obtain protein information related to the feeding of gypsy moth larvae and the egg-laying of female gypsy moths; based on the functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) data, a first sample graph of the protein information is generated; wherein the first sample graph identifies different proteins based on pixel coordinates, the first sample graph contains three-channel data, and different channel data in the three-channel data are respectively used to identify molecular functions, cellular components, and one of the biological processes in which the protein participates; based on the functional classification of standard gene function classification GO data, a second sample graph of the protein information is generated; wherein the second sample graph identifies different proteins based on pixel coordinates, the second sample graph contains two-channel data, and different channel data in the two-channel data are respectively used to identify one of the metabolic pathways and the signal transduction pathways; a protein coloring graph is determined based on the first sample graph and the second sample graph; and an effect factor is determined based on the color value of each pixel position in the protein coloring graph.

[0007] In one embodiment, determining a protein coloring image based on the first sample image and the second sample image includes: performing channel fusion on the first sample image and the second sample image respectively to obtain a first grayscale image of the first sample image, and a second grayscale image of the second sample image; coloring the first grayscale image and the second grayscale image respectively to obtain a first coloring image of the first grayscale image, and a second coloring image of the second grayscale image, wherein the coloring image includes three-channel data for characterizing color; performing image fusion on the first coloring image and the second coloring image to obtain the protein coloring image.

[0008] In one embodiment, the target grayscale image is colored in the following manner to obtain a target colored image: based on two different sub-networks in the cluster analysis model, feature extraction is performed on the target grayscale image respectively to obtain two feature maps each having single-channel data; wherein the receptive fields of the two different sub-networks are different, and the feature maps correspond one-to-one to the sub-networks; the target grayscale image and the feature map are channel-joined to obtain the target colored image with three-channel data; wherein the target grayscale image is a first grayscale image, and the target colored image is a first colored image; or the target grayscale image is a second grayscale image, and the target colored image is a second colored image.

[0009] In one embodiment, determining the effect factor based on the color value of each pixel position in the protein coloring map includes: screening the target pixel position in the protein coloring map whose color difference with the target color value is less than a preset color difference threshold, and using the protein information represented by the target pixel position as the effect factor.

[0010] In one embodiment, the method further includes: in response to determining the effector factor, performing a functional mechanism analysis on the effector factor based on a target processing method to obtain a functional mechanism analysis result; wherein the target processing method includes at least one of CRISPR / Cas9 treatment, RNAi treatment, overexpression treatment and transgenic treatment; based on the molecular functions, cellular components, biological processes involved in proteins, metabolic pathways and signal transduction pathways represented by the effector in the first sample graph and the second sample graph, performing a consistency check on the functional mechanism analysis result, and outputting the functional mechanism analysis result if the check results are consistent.

[0011] According to the specific implementation of the present application, in the second aspect, the present application provides a screening system for effector factors of saliva of larvae of the American white moth and secretions of female oviposition, comprising: A processing unit is used to perform mass spectrometry analysis on the saliva of larvae of the American white moth and the egg-laying secretions of female insects to obtain the protein information of the invasive species; a generating unit is used to generate a first sample map of the protein information based on the functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) data; wherein the first sample map identifies different proteins based on pixel coordinates, and the first sample map contains three-channel data, and different channel data in the three-channel data are respectively used to identify molecular functions, cellular components, and one of the biological processes in which the protein participates; a second sample map of the protein information is generated based on the functional classification of standard gene function classification GO data; wherein the second sample map identifies different proteins based on pixel coordinates, and the second sample map contains two-channel data, and different channel data in the two-channel data are respectively used to identify one of the metabolic pathway and the signal transduction pathway; the processing unit is also used to determine a protein coloring map based on the first sample map and the second sample map; a determining unit is used to determine an effect factor based on the color value of each pixel position in the protein coloring map.

[0012] In one embodiment, the processing unit uses the following method to input the first sample image and the second sample image as input to a pre-trained cluster analysis model to obtain a protein coloring image output by the cluster analysis model and containing three-channel data: performing channel fusion on the first sample image and the second sample image respectively to obtain a first grayscale image of the first sample image and a second grayscale image of the second sample image; coloring the first grayscale image and the second grayscale image respectively to obtain a first coloring image of the first grayscale image and a second coloring image of the second grayscale image, wherein the coloring image contains three-channel data for characterizing color; performing image fusion on the first coloring image and the second coloring image to obtain the protein coloring image.

[0013] In one embodiment, the processing unit colors the target grayscale image in the following manner to obtain a target colored image: based on two different sub-networks in the clustering analysis model, feature extraction is performed on the target grayscale image respectively to obtain two feature maps each having single-channel data; wherein the receptive fields of the two different sub-networks are different, and the feature maps correspond one-to-one to the sub-networks; channel splicing is performed on the target grayscale image and the feature map to obtain the target colored image with three-channel data; wherein the target grayscale image is a first grayscale image, and the target colored image is a first colored image; or the target grayscale image is a second grayscale image, and the target colored image is a second colored image.

[0014] In one embodiment, the determination unit determines the effect factor based on the color value of each pixel position in the protein coloring map in the following manner: in the protein coloring map, a target pixel position whose color difference with the target color value is less than a preset color difference threshold is screened, and the protein information represented by the target pixel position is used as the effect factor.

[0015] In one embodiment, the processing unit is also used for: in response to determining the effector factor, performing a functional mechanism analysis on the effector factor based on a target processing method to obtain a functional mechanism analysis result; wherein the target processing method includes at least one of CRISPR / Cas9 treatment, RNAi treatment, overexpression treatment and transgenic treatment; based on the molecular functions, cellular components, biological processes involved in proteins, metabolic pathways and signal transduction pathways represented by the effector in the first sample graph and the second sample graph, performing a consistency check on the functional mechanism analysis result, and outputting the functional mechanism analysis result if the check results are consistent.

[0016] Compared with the prior art, the above solution of the embodiment of the present application has at least the following beneficial effects: The present application provides a method and system for screening effect factors of saliva of larvae of the American white moth and oviposition secretions of female insects. The method constructs corresponding sample graphs for the functional classification of KEGG data and the functional classification of GO data, respectively, to characterize the characteristics of protein information under different classification dimensions, and then realizes the feature analysis, merging, and output of the sample graphs through the pre-trained cluster analysis model, and obtains a color image that can characterize the functional characteristics of the protein through color, that is, a protein coloring graph. On this basis, different pixel positions in the protein coloring graph correspond to different protein information, and the differences in protein information in structure, function, metabolism, and conduction pathways can be expressed through the difference in pixel color values, thereby realizing easy reading and easy search of protein information, so that effect factors can be distinguished based on machines or humans, and flexibility is increased. At the same time, since the method is directly characterized by accurate color values, it can effectively improve the problem of normalizing similar data through error thresholds in related technologies, resulting in large errors in the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart showing a method for screening saliva of larvae of the American white moth and oviposition secretions of female insects according to an embodiment of the present application is shown; Figure 2 It shows that the first sample graph and the second sample graph are input into the pre-trained cluster analysis model; Figure 3 A flow chart of a method for coloring a target grayscale image to obtain a target colored image is shown; Figure 4 A unit block diagram of an invasive species effect factor screening system according to an embodiment of the present application is shown; Figure 5 The present invention is a block diagram of an electronic device for screening effect factors of invasive species according to an exemplary embodiment. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0019] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0020] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0021] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0022] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0023] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0024] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0025] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0026] The embodiments provided in this application are embodiments of a method for screening the saliva of larvae of the American white moth and the egg-laying secretions of female insects.

[0027] Combine the following Figure 1 The embodiments of the present application are described in detail.

[0028] Figure 1 The flowchart of the method for screening saliva of invasive species gypsy moth larvae and oviposition secretions of female gypsy moths according to an embodiment of the present application is shown as follows: Figure 1 As shown, the process includes the following steps S101 to S104.

[0029] In step S101, mass spectrometry analysis is performed on the saliva of the invasive species cuneiform moth larvae and the oviposition secretions of the female cuneiform moth to obtain protein information related to the feeding of the cuneiform moth larvae and the oviposition of the female cuneiform moth. This example conducts mass spectrometry analysis on the saliva of larvae and oviposition secretions of female gypsy moth, an invasive species, which helps to accurately identify and analyze protein information directly related to the feeding habits of larvae and oviposition behavior of female gypsy moth.

[0030] During their growth and development, nymphs generally take in a variety of protein sources, among which the silk protein gene of nymphs shows different expression patterns in different tissues.

[0031] For example, the three silk fibroin genes HcP25, HcFib-H and HcFib-L are most highly expressed in the silk gland tissue of the third-instar gypsy moth larvae, and are significantly higher than those in other tissues.

[0032] Generally speaking, the expression profiles of these genes also show significant differences after feeding on different host plants.

[0033] For example, the relative expression levels of HcP25 and HcFib-H genes of the gypsy moth larvae feeding on poplar trees were significantly higher than those of populations on other hosts, while the relative expression level of HcFib-L of the gypsy moth larvae feeding on mountain cherry trees was significantly higher than that of other populations.

[0034] From the above, it can be seen that when the larvae of the gypsy moth feed on different plants, they generally adjust their protein intake and metabolism according to the plant species to meet their growth and development needs.

[0035] Therefore, this application obtains corresponding protein information by performing mass spectrometry analysis on the saliva of the invasive species gypsy moth larvae and the egg-laying secretions of female insects, which can effectively determine the distribution and growth characteristics of the gypsy moth and provide assistance for subsequent analysis and research.

[0036] In step S102, a first sample graph of protein information is generated based on the functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) data, and a second sample graph of protein information is generated based on the functional classification of standard gene functional classification GO data.

[0037] Taking the gypsy moth as an example, this embodiment uses the Kyoto Encyclopedia of Genes and Genomes (KEGG) data to functionally classify the protein information of the gypsy moth and generate the first sample graph. This graph shows in detail the distribution of gypsy moth proteins in biological processes such as metabolic pathways, gene expression regulation, and signal transduction, revealing its complex biological functions. At the same time, this embodiment further classifies the protein information of the gypsy moth based on the standard gene function classification (GO) data, and generates a second sample graph, which focuses on the specific role of proteins in molecular functions, cellular components, and biological processes.

[0038] The first sample image identifies different proteins based on pixel coordinates, and the first sample image includes three-channel data, in which different channel data are used to identify molecular functions, cell components, and one of the biological processes in which the protein participates. The second sample image identifies different proteins based on pixel coordinates, and the second sample image includes two-channel data, in which different channel data are used to identify one of the metabolic pathways and the signal transduction pathways.

[0039] In step S103, a protein coloring graph is determined based on the first sample graph and the second sample graph. For example, taking the gypsy moth as an example, the first sample image can be an image of the leopard-patterned upper wings of a male gypsy moth, and the second sample image can be an image of the gorgeous, elegant, and flawless wings of a female gypsy moth. This embodiment uses the above-mentioned first and second sample images to perform protein coloring image analysis, uses image analysis to identify the areas of protein expression on the wings of the gypsy moth, and draws a protein coloring image based on the grayscale values ​​and optical density values ​​of these areas. The protein coloring image can reflect the distribution of proteins on the wings of the gypsy moth, which is helpful for further understanding of the biological characteristics and ecological habits of the gypsy moth.

[0040] In step S104, the effect factor is determined based on the color value of each pixel position in the protein coloring map.

[0041] In the present application, the three-channel data of the protein coloring diagram is suitable for color characterization and can be output to the display end by color development. On this basis, the machine end can directly screen the effect factor with the corresponding color value according to the three-channel data of the protein coloring diagram, or the color map based on the three-channel data of the protein coloring diagram can be manually analyzed to complete the screening of the effect factor, and the method has the characteristics of flexibility and convenience. In addition, since the whole process is completed by the cluster analysis model, there is no process such as error data normalization that affects data accuracy in data processing, therefore, the present application can achieve accurate color output, and maintain extremely high data accuracy, which provides convenience for the screening of effect factors.

[0042] In the present application, the first sample graph and the second sample graph can be input into a pre-trained cluster analysis model to obtain a corresponding protein coloring graph. The cluster analysis model is a machine learning algorithm suitable for processing classification problems, for example, it can be a deep learning network. By setting a network branch adapted to the input end for the first sample graph and the second sample graph, a corresponding architecture of the feature layer is given. On this basis, according to the output characteristics, three output layer subnetworks are set for the three-channel data required for outputting the protein coloring graph, and finally output as a protein coloring graph with three-channel data in a superimposed form.

[0043] For example, after training, the cluster analysis model can output the color values ​​corresponding to different protein information after given the first sample graph and the second sample graph, that is, given the input data including molecular functions, cell components, and biological processes, metabolic pathways, and signal transduction pathways in which proteins participate. On this basis, the protein information can be further classified or grouped according to the result values ​​given by the cluster analysis model by accessing the color processing model.

[0044] To facilitate understanding, the data processing process within the cluster analysis model is explained below.

[0045] Figure 2 A flowchart of a method for inputting a first sample graph and a second sample graph into a pre-trained cluster analysis model to obtain a protein coloring graph is shown, as shown in FIG. Figure 2 As shown, the process includes the following steps S201 to S203.

[0046] In step S201, channel fusion is performed on the first sample image and the second sample image respectively to obtain a first grayscale image of the first sample image and a second grayscale image of the second sample image.

[0047] In step S202, the first grayscale image and the second grayscale image are colored respectively to obtain a first colored image of the first grayscale image and a second colored image of the second grayscale image, wherein the colored images contain three-channel data for representing colors.

[0048] In step S203, the first colored image is fused with the second colored image to obtain a protein colored image.

[0049] For example, first, the first sample image (such as a larval stage photo) and the second sample image (such as an adult stage photo) of the gypsy moth can be fused through channels to obtain a first grayscale image of the first sample image and a second grayscale image of the second sample image. Channel fusion can ensure the integrity and accuracy of image information.

[0050] Secondly, the first grayscale image and the second grayscale image can be colored based on the biological characteristics of the gypsy moth to obtain a first colored image (green larvae) of the first grayscale image and a second colored image (white adult) of the second grayscale image. For example, the biological characteristics of the gypsy moth may include the green body of the larvae and the white wings of the adults. The first colored image and the second colored image contain three-channel data for representing colors, making the image closer to the actual biological characteristics.

[0051] Finally, the first colored image and the second colored image can be fused to obtain a complete protein colored image of the gypsy moth. The protein colored image of the gypsy moth can show the morphological changes of the gypsy moth from larvae to adults, and also emphasizes its biological characteristics through color information, providing certain visual support for subsequent scientific research analysis.

[0052] In the present application, the first colored image and the second colored image represent color images with three-channel data respectively. When the first colored image and the second colored image are image-fused, the data fusion can be performed in a color channel corresponding manner.

[0053] In the present application, the grayscale image can be processed into a colored image in the following manner. In particular, for ease of description, the present application defines a target grayscale image for characterizing the first grayscale image or the second grayscale image, and defines a target colored image for characterizing the first colored image or the second colored image. When the target grayscale image in the embodiment is the first grayscale image, the target colored image should be regarded as the first colored image, and correspondingly, when the target grayscale image in the embodiment is the second grayscale image, the target colored image should be regarded as the second colored image.

[0054] Figure 3 The flowchart of the method for coloring the target grayscale image to obtain the target colored image is shown in the present application. Figure 3 As shown, the process includes the following steps S301 to S302.

[0055] In step S301, based on two different sub-networks in the cluster analysis model, feature extraction is performed on the target grayscale image to obtain two feature maps each having single-channel data.

[0056] In step S302, the target grayscale image and the feature image are channel-joined to obtain a target colored image with three-channel data.

[0057] In this application, the receptive fields of two different subnetworks are different, and the two feature maps obtained correspond one-to-one to the two different subnetworks in the cluster analysis model. Since the branch with a larger receptive field can focus on a wider area to capture high-level semantic information, the branch with a smaller receptive field can locate a more accurate color value in a small range, thereby ensuring the accuracy of the coloring result.

[0058] In a feasible embodiment, the color of the protein coloring image can be expressed in YUV format. The first coloring image and the second coloring image are also expressed in YUV format. Correspondingly, the first grayscale image and the second grayscale image can be regarded as the Y color channel image in the YUV format image. The present application realizes image coloring based on protein information by filling the U color channel and the V color channel for the Y color channel image.

[0059] In the present application, the effect factor can be determined by the color value of each pixel position in the protein coloring map. In one embodiment, the target pixel position whose color difference value with the target color value is less than the preset color difference threshold can be screened in the protein coloring map, and the protein information represented by the target pixel position is used as the effect factor. This method can ensure that the data is not missed and improve the screening rate of the effect factor. In another embodiment, the pixel position with a preset color value in the image can also be directly searched according to the preset color value. This method can ensure the screening accuracy of the effect factor. As a feasible implementation method, one of the above two implementation methods can be selected for implementation according to the integrity of the model expression to ensure both the screening rate and the screening accuracy.

[0060] In one embodiment, the first sample image and the second sample image can be colored with protein Figure 1 The same output is used to provide reference data for subsequent analysis of the functional mechanism of the effector.

[0061] On this basis, when the effector is determined, the effector can be subjected to functional mechanism analysis through target processing to obtain the functional mechanism analysis result. Then, according to the molecular functions, cell components, biological processes, metabolic pathways and signal transduction pathways represented by the effector in the first sample graph and the second sample graph, the functional mechanism analysis result is checked for consistency, and the functional mechanism analysis result is output if the verification results are consistent.

[0062] The target treatment method can be understood as a specific treatment method selected, for example, it can be at least one of CRISPR / Cas9 treatment, RNAi treatment, overexpression treatment and transgenic treatment.

[0063] Of course, if the verification results are inconsistent, the functional mechanism analysis results, the first sample graph, and the second sample graph can be further analyzed manually or by setting a specific AI model. If the functional mechanism analysis results are determined to be inaccurate after verification, the information represented by the first sample graph and the second sample graph will be output as the updated functional mechanism analysis results. If it is determined after verification that the information represented by the first sample graph and the second sample graph is inaccurate, the inaccurate first sample graph and the second sample graph are set as the negative gold finger of the cluster analysis model, and the cluster analysis model is updated and trained to further improve the accuracy of the cluster analysis model.

[0064] In addition, as some feasible embodiments, the present application can use CRISPR / Cas9 and RNAi and other means to deeply explore the functions and mechanisms of these effectors in the key processes of feeding and oviposition of invasive species of American white moth. Based on this, the expression of specific genes can be specifically inhibited or activated to study the effects of these genes on the harmful process of American white moth. At the same time, the effects of these factors on the interaction between American white moth and host plants are analyzed by overexpression and transgenic techniques. Through overexpression or transgenic technology, specific genes can be overexpressed or silenced in host plants to study the effects of these genes on the interaction between American white moth and host plants. In addition, various molecular interaction analysis methods, such as yeast two-hybridization, immunoprecipitation, fluorescence resonance energy transfer, etc., are used at the same time to clarify the regulation mode of these factors on host plants and the influence mode and mechanism of the interaction between American white moth and host plants. These molecular interaction analysis methods can help us to deeply understand the interaction between these factors and host plants, thereby providing important clues for further studying the harmful mechanism of American white moth. Finally, the evolution of these factors and their relationship with the invasion process of invasive species are studied by bioinformatics analysis.

[0065] On this basis, through bioinformatics analysis, we can compare the sequence and structural characteristics of these factors in different species, so as to study the evolutionary history of these factors. At the same time, through bioinformatics analysis, we can also study the relationship between these factors and the invasion process of invasive species, thus providing important clues for further research on the invasion mechanism of invasive species.

[0066] The present application also provides a system embodiment that is based on the above embodiment, which is used to implement the method steps of the above embodiment. The explanation based on the same name meaning is the same as the above embodiment, and has the same technical effect as the above embodiment, which will not be repeated here.

[0067] like Figure 4 As shown, the present application provides an invasive species effect factor screening system 400, comprising: The processing unit 402 is used to perform mass spectrometry analysis on the saliva of the larvae of the American white moth and the oviposition secretions of the female insects to obtain protein information related to the feeding of the larvae of the American white moth and the oviposition of the female insects. The generating unit 401 is used to generate a first sample map of protein information based on the functional classification of the Kyoto Encyclopedia of Genes and Genomes KEGG data. Among them, the first sample map identifies different proteins based on pixel coordinates, and the first sample map contains three-channel data, and different channel data in the three-channel data are respectively used to identify molecular functions, cell components, and one of the biological processes in which the protein participates. Based on the functional classification of the standard gene function classification GO data, a second sample map of protein information is generated. Among them, the second sample map identifies different proteins based on pixel coordinates, and the second sample map contains two-channel data, and different channel data in the two-channel data are respectively used to identify one of the metabolic pathways and signal transduction pathways. The processing unit 402 is also used to determine the protein coloring map based on the first sample map and the second sample map. The determining unit 403 is used to determine the effect factor based on the color value of each pixel position in the protein coloring map.

[0068] In one embodiment, the processing unit 402 uses the first sample image and the second sample image as inputs to a pre-trained cluster analysis model in the following manner to obtain a protein coloring image output by the cluster analysis model and containing three-channel data: the first sample image and the second sample image are channel-fused to obtain a first grayscale image of the first sample image, and a second grayscale image of the second sample image. The first grayscale image and the second grayscale image are colorized to obtain a first coloring image of the first grayscale image and a second coloring image of the second grayscale image, respectively, and the coloring image contains three-channel data for characterizing color. The first coloring image and the second coloring image are image-fused to obtain a protein coloring image.

[0069] In one embodiment, the processing unit 402 colors the target grayscale image in the following manner to obtain a target colored image: based on two different sub-networks in the cluster analysis model, feature extraction is performed on the target grayscale image respectively to obtain two feature maps each having single-channel data. The receptive fields of the two different sub-networks are different, and the feature maps correspond to the sub-networks one-to-one. The target grayscale image and the feature map are channel-joined to obtain a target colored image with three-channel data. The target grayscale image is a first grayscale image, and the target colored image is a first colored image. Or the target grayscale image is a second grayscale image, and the target colored image is a second colored image.

[0070] In one embodiment, the determination unit 403 determines the effect factor based on the color value of each pixel position in the protein coloring image in the following manner: the target pixel position whose color difference value with the target color value is less than a preset color difference threshold is screened in the protein coloring image, and the protein information represented by the target pixel position is used as the effect factor.

[0071] In one embodiment, the processing unit 402 is further used to: in response to determining the effector, perform a functional mechanism analysis on the effector based on a target processing method to obtain a functional mechanism analysis result. The target processing method includes at least one of CRISPR / Cas9 processing, RNAi processing, overexpression processing, and transgenic processing. Based on the molecular functions, cellular components, biological processes involved in proteins, metabolic pathways, and signal transduction pathways represented by the effector in the first sample graph and the second sample graph, the functional mechanism analysis results are checked for consistency, and the functional mechanism analysis results are output if the verification results are consistent.

[0072] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0073] Figure 5 It is a block diagram of an electronic device 500 for screening invasive species effect factors according to an exemplary embodiment.

[0074] like Figure 5 As shown, one embodiment of the present application provides an electronic device 500. The electronic device 500 includes a memory 501, a processor 502, and an input / output (I / O) interface 503. The memory 501 is used to store instructions. The processor 502 is used to call the instructions stored in the memory 501 to execute the method for screening effect factors of the saliva of the larvae of the American white moth and the oviposition secretions of the female insects in the embodiment of the present application. The processor 502 is connected to the memory 501 and the I / O interface 503 respectively, for example, through a bus system and / or other forms of connection mechanisms (not shown). The memory 501 can be used to store programs and data, including the program of the method for screening effect factors involved in the embodiment of the present application, and the processor 502 executes various functional applications and data processing of the electronic device 500 by running the program stored in the memory 501.

[0075] In the embodiment of the present application, the processor 502 can be implemented in at least one hardware form of a digital signal processor (Digital Signal Processing, DSP), a field programmable gate array (Field Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). The processor 502 can be a central processing unit (Central Processing Unit, CPU) or one or a combination of other forms of processing units with data processing capabilities and / or instruction execution capabilities.

[0076] The memory 501 in the embodiment of the present application may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD), etc.

[0077] In the embodiment of the present application, the I / O interface 503 may be used to receive input instructions (such as digital or character information, and key signal input related to user settings and function control of the electronic device 500), and may also output various information to the outside (such as images or sounds). In the embodiment of the present application, the I / O interface 503 may include one or more of a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, and a touch panel.

[0078] In some embodiments, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, any of the methods described above is performed.

[0079] In some embodiments, the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, any of the methods described above is performed.

[0080] Although operations are described in a particular order in the drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in serial order, or that all shown operations be performed to achieve desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0081] The methods and systems of the present application can be implemented using standard programming techniques, using rule-based logic or other logic to implement various method steps. It should also be noted that the terms "system" and "module" used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0082] Any steps, operations or procedures described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product including a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the described steps, operations or procedures.

[0083] The foregoing description of the implementation of the present application has been given for the purpose of illustration and description. The foregoing description is not exhaustive nor is it intended to limit the present application to the exact form disclosed, and various variations and modifications may exist according to the above teachings or may be obtained from the practice of the present application. These embodiments are selected and described in order to illustrate the principles of the present application and its practical application, so that those skilled in the art can utilize the present application in various embodiments and various modifications suitable for the specific purpose contemplated.

[0084] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0085] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0086] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present application, it should not be understood as requiring the operations to be performed in the specific order or serial order shown, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0087] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to encompass any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the field of the present application that are not disclosed in the present application. The specification and embodiments are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following scope of rights.

[0088] It should be understood that the present application is not limited to the precise structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the scope of the appended claims.

[0089] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for screening effect factors of saliva of larvae and oviposition secretions of female nymphs, comprising the following steps: Mass spectrometry analysis of the saliva of nymphs and the oviposition secretions of females was performed to obtain protein information related to the feeding of nymphs and oviposition of females; Based on the functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) data, a first sample graph of the protein information is generated; wherein the first sample graph identifies different proteins based on pixel coordinates, and the first sample graph includes three-channel data, and different channel data in the three-channel data are respectively used to identify one of the molecular function, the cellular component, and the biological process in which the protein participates; Based on the functional classification of standard gene functional classification GO data, a second sample graph of the protein information is generated; wherein the second sample graph identifies different proteins based on pixel coordinates, and the second sample graph includes dual-channel data, and different channel data in the dual-channel data are respectively used to identify one of a metabolic pathway and a signal transduction pathway; Determine a protein coloring map based on the first sample map and the second sample map; Based on the color value of each pixel position in the protein coloring map, an effect factor is determined.

2. The method for screening effect factors of saliva of larvae of the American white moth and oviposition secretions of female insects according to claim 1, characterized in that: The step of determining a protein coloring graph based on the first sample graph and the second sample graph comprises: Performing channel fusion on the first sample image and the second sample image respectively to obtain a first grayscale image of the first sample image and a second grayscale image of the second sample image; Coloring the first grayscale image and the second grayscale image respectively to obtain a first colored image of the first grayscale image and a second colored image of the second grayscale image, wherein the colored images include three-channel data for representing colors; The first colored image is fused with the second colored image to obtain the protein colored image.

3. The method for screening effect factors of saliva of larvae and oviposition secretions of female nymphs according to claim 2, characterized in that: The target grayscale image is colored as follows to obtain the target colored image: Based on two different sub-networks in the cluster analysis model, feature extraction is performed on the target grayscale image to obtain two feature images each having single-channel data; The receptive fields of the two different sub-networks are different, and the feature maps correspond one to one with the sub-networks; Perform channel splicing on the target grayscale image and the feature image to obtain the target colored image with three-channel data; Among them, the target grayscale image is a first grayscale image, and the target colored image is a first colored image; or the target grayscale image is a second grayscale image, and the target colored image is a second colored image.

4. The method for screening effect factors of saliva of larvae of the American white moth and oviposition secretions of female insects according to any one of claims 1 to 3, characterized in that: Determining the effect factor based on the color value of each pixel position in the protein coloring map includes: In the protein coloring map, a target pixel position whose color difference with the target color value is less than a preset color difference threshold is screened, and the protein information represented by the target pixel position is used as an effect factor.

5. The method for screening effect factors of saliva of larvae and oviposition secretions of female nymphs according to claim 4, characterized in that: The method further comprises: In response to determining the effector, performing a functional mechanism analysis on the effector based on a target treatment method to obtain a functional mechanism analysis result; wherein the target treatment method includes at least one of CRISPR / Cas9 treatment, RNAi treatment, overexpression treatment, and transgenic treatment; Based on the molecular functions, cellular components, biological processes, metabolic pathways and signal transduction pathways involved in the effector represented by the first sample graph and the second sample graph, the functional mechanism analysis results are checked for consistency, and the functional mechanism analysis results are output if the verification results are consistent.

6. A screening system for effector factors of saliva of larvae of the American white moth and oviposition secretions of female moths, comprising the following steps: A processing unit is used to collect saliva of invasive species of gypsy moth larvae and oviposition secretions of female gypsy moths for mass spectrometry analysis to obtain protein information related to feeding of gypsy moth larvae and oviposition of female gypsy moths; A generating unit is used to generate a first sample graph of the protein information based on the functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) data; wherein the first sample graph identifies different proteins based on pixel coordinates, and the first sample graph includes three-channel data, and different channel data in the three-channel data are respectively used to identify one of the molecular function, the cellular component, and the biological process in which the protein participates; based on the functional classification of the standard gene function classification GO data, a second sample graph of the protein information is generated; wherein the second sample graph identifies different proteins based on pixel coordinates, and the second sample graph includes two-channel data, and different channel data in the two-channel data are respectively used to identify one of the metabolic pathway and the signal transduction pathway; The processing unit is further used to determine a protein coloring map based on the first sample map and the second sample map; The determination unit is used to determine the effect factor based on the color value of each pixel position in the protein coloring map.

7. The screening system for effector factors of saliva of larvae of the American white moth and oviposition secretions of female insects according to claim 6, characterized in that: The processing unit uses the first sample graph and the second sample graph as inputs to a pre-trained cluster analysis model in the following manner to obtain a protein coloring graph output by the cluster analysis model and containing three-channel data: Performing channel fusion on the first sample image and the second sample image respectively to obtain a first grayscale image of the first sample image and a second grayscale image of the second sample image; Coloring the first grayscale image and the second grayscale image respectively to obtain a first colored image of the first grayscale image and a second colored image of the second grayscale image, wherein the colored images include three-channel data for representing colors; The first colored image is fused with the second colored image to obtain the protein colored image.

8. The screening system for effector factors of saliva of larvae of the American white moth and oviposition secretions of female insects according to claim 7, characterized in that: The processing unit colors the target grayscale image in the following manner to obtain a target colored image: Based on two different sub-networks in the cluster analysis model, feature extraction is performed on the target grayscale image to obtain two feature images each having single-channel data; The receptive fields of the two different sub-networks are different, and the feature maps correspond one to one with the sub-networks; Perform channel splicing on the target grayscale image and the feature image to obtain the target colored image with three-channel data; Among them, the target grayscale image is a first grayscale image, and the target colored image is a first colored image; or the target grayscale image is a second grayscale image, and the target colored image is a second colored image.

9. The screening system for effector factors of saliva of larvae of the American white moth and oviposition secretions of female insects according to any one of claims 6 to 8, characterized in that: The determining unit determines the effect factor based on the color value of each pixel position in the protein coloring map in the following manner: In the protein coloring map, a target pixel position whose color difference with the target color value is less than a preset color difference threshold is screened, and the protein information represented by the target pixel position is used as an effect factor.

10. The screening system for effector factors of saliva of larvae of the American white moth and oviposition secretions of female insects according to claim 9, characterized in that: The processing unit is also used for: In response to determining the effector, performing a functional mechanism analysis on the effector based on a target treatment method to obtain a functional mechanism analysis result; wherein the target treatment method includes at least one of CRISPR / Cas9 treatment, RNAi treatment, overexpression treatment, and transgenic treatment; Based on the molecular functions, cellular components, biological processes, metabolic pathways and signal transduction pathways involved in the effector represented by the first sample graph and the second sample graph, the functional mechanism analysis results are checked for consistency, and the functional mechanism analysis results are output if the verification results are consistent.

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