Method for screening effectors of hyalophagous insect larvae and female insect oviposition secretions

Abstract

The application provides a screening method for effectors of foreign invasive species Hyphantria cunea larvae saliva and female insect oviposition secretions. The application collects saliva of invasive species Hyphantria cunea larvae and concentrates female insect oviposition secretions, then performs mass spectrometry analysis, performs KEGG and GO cluster analysis on the obtained protein information, and screens possible key proteins. Further, CRISPR / Cas9 and RNAi means are used to explore the functions and roles of these effectors in key damage-causing processes such as Hyphantria cunea larvae feeding and female insect oviposition, effectively realizing the screening of effectors of the foreign invasive pest, and providing effective means for preventing the maintenance and outbreak of Hyphantria cunea population and the damage of the population to agricultural 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 effector factors in the saliva of larvae of the American white moth and the egg-laying 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 gypsy moth's large egg-laying numbers, wide-ranging diet, and voracious appetite of its older larvae are the primary reasons for its significant damage to agriculture and forestry. Currently, genetic analysis is being considered to provide targeted solutions to this major invasive pest. For example, by extracting protein profiles from larval saliva and oviposit secretions, proteins that characterize genes specific to the invasive species and act on the gypsy moth could be identified as effectors, allowing the development and implementation of genetic control measures that target the invasive pest without affecting native species.

[0004] In related technologies, when analyzing effect factors, the international standard gene functional 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 through 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 effect factors obtained by screening are of 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 data for 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 the saliva of the larvae of the invasive alien species, the gypsy moth, and the oviposition secretions of female insects to solve the above technical problems. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for screening effector factors in the saliva of larvae and the oviposition secretions of female nymphs, which can solve at least one of the above-mentioned technical problems. The specific solution is as follows:

[0007] According to the specific embodiments of the present application, in a first aspect, the present application provides a method for screening effector factors in the saliva of larvae and oviposition secretions of female invasive species of the cuneiform moth, comprising:

[0008] Mass spectrometry analysis was performed on the saliva of gypsy moth larvae and the oviposition secretions of female gypsy moths to obtain protein information related to the feeding of gypsy moth larvae and the oviposition of female gypsy moths; a first sample map of the protein information was generated based on the functional classification of Kyoto Encyclopedia of Genes and Genomes (KEGG) data; wherein, the first sample map identified different proteins based on pixel coordinates, the first sample map contained three-channel data, and different channel data in the three-channel data were respectively used to identify molecular functions, cellular components, and one of the biological processes in which the protein participated; a second sample map of the protein information was generated based on the functional classification of standard gene function classification (GO) data; wherein, the second sample map identified different proteins based on pixel coordinates, the second sample map contained two-channel data, and different channel data in the two-channel data were respectively used to identify one of the metabolic pathways and the signal transduction pathways; a protein coloring map was determined based on the first and second sample maps; and an effector was determined based on the color value of each pixel position in the protein coloring map.

[0009] 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 representing color; performing image fusion on the first coloring image and the second coloring image to obtain the protein coloring image.

[0010] 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-spliced ​​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.

[0011] In one embodiment, the effect factor is determined based on the color value of each pixel position in the protein coloring map, including: screening the target pixel position in the protein coloring map whose color difference value 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.

[0012] 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, 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.

[0013] According to a specific embodiment of the present application, in a 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 ovipositors, comprising:

[0014] A processing unit is used to perform mass spectrometry analysis on the saliva of gypsy moth larvae and the egg-laying secretions of female insects to obtain 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 effector based on the color value of each pixel position in the protein coloring map.

[0015] 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 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 representing color; performing image fusion on the first coloring image and the second coloring image to obtain the protein coloring image.

[0016] 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 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-spliced ​​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.

[0017] In one embodiment, the determination unit determines the effect factor based on the color value of each pixel position in the protein coloring image in the following manner: in the protein coloring image, 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.

[0018] In one embodiment, the processing unit is further used to: in response to determining the effector factor, perform a functional mechanism analysis on the effector 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, metabolic pathways and signal transduction pathways represented by the effector in the first sample graph and the second sample graph, perform consistency verification on the functional mechanism analysis result, and output the functional mechanism analysis result if the verification results are consistent.

[0019] Compared with the prior art, the above solution of the embodiment of the present application has at least the following beneficial effects:

[0020] The present application provides a method and system for screening effect factors of saliva of larvae of the American white moth and secretions of female ovipositors. 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 feature analysis, merging and output of the sample graphs through a 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 respectively. Through the difference in pixel color values, the differences in protein information in terms of structure, function, metabolism and conduction pathway can be expressed, thereby realizing easy-to-read and easy-to-find protein information, so that effect factors can be distinguished based on machines or humans, increasing flexibility. At the same time, because the method is directly characterized by accurate color values, it can effectively improve the problem in the related art of normalizing similar data through error thresholds, resulting in large errors in the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for screening saliva of hystrix moth larvae and oviposition secretions of female moths according to an embodiment of the present application is shown;

[0022] Figure 2 shows inputting the first sample graph and the second sample graph into a pre-trained cluster analysis model;

[0023] Figure 3 The flowchart of the method for coloring a target grayscale image to obtain a target colored image is shown;

[0024] Figure 4 A unit block diagram of an invasive species effect factor screening system according to an embodiment of the present application is shown;

[0025] Figure 5 The figure is a block diagram of an electronic device for screening effect factors of invasive species according to an exemplary embodiment. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of this application more clear, this application will be further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0027] The terms used in the examples of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in the examples of this application and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0028] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0029] 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.

[0030] 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 the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0031] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or system. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or system comprising the element.

[0032] It should be noted in particular that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0033] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0034] The embodiments provided in this application are embodiments of the method for screening the saliva of larvae of the American white moth and the egg-laying secretions of female insects.

[0035] The following combination Figure 1The embodiments of the present application are described in detail.

[0036] Figure 1 The flowchart of the method for screening saliva of invasive species gypsy moth larvae and female ovipositor secretions 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.

[0037] In step S101, mass spectrometry analysis is performed on the saliva of the invasive species cuneiform moth larvae and the secretions of female insects laying eggs to obtain protein information related to the feeding of cuneiform moth larvae and the laying of eggs by female insects.

[0038] This example uses mass spectrometry analysis on the saliva of larvae and the oviposition secretions of female gypsy moths, an invasive species. This helps accurately identify and analyze protein information directly related to the feeding habits of larvae and the oviposition behavior of female gypsy moths.

[0039] During their growth and development, nymphal moth larvae typically consume a variety of protein sources. Among them, the silk fibroin gene of nymphal moth larvae exhibits different expression patterns in different tissues.

[0040] For example, the three silk fibroin genes HcP25, HcFib-H and HcFib-L are expressed at the highest levels in the silk gland tissue of the third-instar gypsy moth larvae, and are significantly higher than in other tissues.

[0041] Generally speaking, the expression profiles of these genes also show significant differences after feeding on different host plants.

[0042] For example, the relative expression levels of HcP25 and HcFib-H genes in gypsy moth larvae feeding on poplar trees were significantly higher than those in populations of other hosts, while the relative expression level of HcFib-L in gypsy moth larvae feeding on mountain cherry trees was significantly higher than that in other populations.

[0043] From the above, it can be seen that when the gypsy moth larvae feed on different plants, they generally adjust their protein intake and metabolism according to the plant species to meet their growth and development needs.

[0044] 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.

[0045] 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.

[0046] Taking the gypsy moth (Cyprinus cuneiformis) as an example, this example utilizes Kyoto Encyclopedia of Genes and Genomes (KEGG) data to functionally classify protein information from the moth and generate a first sample graph. This graph details the distribution of gypsy moth proteins in biological processes such as metabolic pathways, gene expression regulation, and signal transduction, revealing their complex biological functions. Furthermore, this example further classifies protein information from the moth based on standard gene function (GO) data and generates a second sample graph, which focuses on the specific roles of proteins in molecular functions, cellular components, and biological processes.

[0047] The first sample image identifies different proteins based on pixel coordinates and contains three-channel data, with each channel used to identify a molecular function, a cellular component, or a biological process in which the protein participates. The second sample image identifies different proteins based on pixel coordinates and contains two-channel data, with each channel used to identify a metabolic pathway or a signal transduction pathway.

[0048] In step S103, a protein coloring image is determined based on the first sample image and the second sample image.

[0049] For example, using the cuneiform moth as an example, the first sample image could be a picture of the striking leopard-patterned upper wings of a male cuneiform moth, while the second sample image could be a picture of the elegant, pristine white wingspan of a female. This example uses these first and second sample images to perform protein staining analysis. Image analysis identifies protein expression regions on the moth's wings and generates a protein staining map based on the grayscale and optical density values ​​of these regions. This protein staining map reveals the distribution of proteins on the moth's wings, contributing to further understanding of the moth's biological characteristics and ecological habits.

[0050] In step S104, the effect factor is determined based on the color value of each pixel position in the protein coloring image.

[0051] In the present application, the three-channel data of the protein coloring map is suitable for color characterization and can be output to the display end in a color development manner. On this basis, the machine end can directly screen the effect factors with corresponding color values ​​based on the three-channel data of the protein coloring map, or the color map based on the three-channel data of the protein coloring map can be manually analyzed to complete the screening of the effect factors. This method has the characteristics of flexibility and convenience. In addition, since the entire 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.

[0052] 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 respectively, an architecture adapted to the feature layer is given. On this basis, according to the output characteristics, three output layer sub-networks are set for the three-channel data required to output the protein coloring graph, and finally the protein coloring graph with three-channel data is output in a superimposed form.

[0053] For example, a trained cluster analysis model, given a first sample image and a second sample image—that is, input data including molecular function, cellular components, and the biological processes, metabolic pathways, and signaling pathways in which proteins participate—can output color values ​​corresponding to different protein information. Based on this, the color processing model can be further integrated to classify or group the protein information based on the resulting values ​​from the cluster analysis model.

[0054] To facilitate understanding, the data processing process within the cluster analysis model is explained below.

[0055] Figure 2 A flow chart 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.

[0056] 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.

[0057] 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.

[0058] In step S203, the first colored image and the second colored image are fused to obtain a protein colored image.

[0059] For example, first, channel fusion can be performed on a first sample image (e.g., a larval stage photo) and a second sample image (e.g., an adult stage photo) of the cuneiform moth to obtain a first grayscale image of the first sample image and a second grayscale image of the second sample image. Channel fusion ensures the integrity and accuracy of image information.

[0060] Next, the first and second grayscale images can be colorized based on the biological characteristics of the cuneiform moth, resulting in a first colorized image of the first grayscale image (green larvae) and a second colorized image of the second grayscale image (white adult moths). For example, the biological characteristics of the cuneiform moth may include the green body of the larvae and the white wings of the adult moths. The first and second colorized images contain three-channel data for color representation, making the images more realistically resemble the actual biological characteristics.

[0061] Finally, the first and second colored images can be fused to create a complete protein coloration map of the cuneiform moth. This protein coloration map shows the morphological changes of the cuneiform moth from larva to adult, and also emphasizes its biological characteristics through color information, providing visual support for subsequent scientific research analysis.

[0062] In the present application, the first colored image and the second colored image respectively represent color images with three-channel data. When the first colored image and the second colored image are fused, the data fusion can be performed in a color channel corresponding manner.

[0063] In the present application, a grayscale image can be processed into a colored image in the following manner. Herein, for ease of description, the present application defines a target grayscale image for representing the first grayscale image or the second grayscale image, and a target colored image for representing 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. 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.

[0064] Figure 3 The flowchart of the method for coloring the target grayscale image to obtain the target colored image is shown in FIG. Figure 3 As shown, the process includes the following steps S301 to S302.

[0065] 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.

[0066] In step S302, the target grayscale image and the feature image are channel-joined to obtain a target colored image with three-channel data.

[0067] In this application, the receptive fields of the two different sub-networks are different, and the two feature maps obtained correspond one-to-one to the two different sub-networks 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 more accurate color values ​​within a small range, thereby ensuring the accuracy of the colorization result.

[0068] In one embodiment, the colors of the protein coloring image can be expressed in YUV format. The first and second coloring images are also expressed in YUV format. Accordingly, the first and second grayscale images can be considered Y color channel images in a YUV format image. This application implements protein-based image coloring by padding the Y color channel image with the U and V color channels.

[0069] In the present application, the effect factor can be determined by the color value of each pixel position in the protein shading 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 shading map, and the protein information represented by the target pixel position is used as the effect factor. This method can ensure that 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 based on the integrity of the model expression to ensure both the screening rate and the screening accuracy.

[0070] 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.

[0071] On this basis, when the effector is determined, a functional mechanism analysis can be performed on the effector through target processing to obtain the functional mechanism analysis results. Then, based on the molecular functions, cellular components, biological processes, metabolic pathways, and signal transduction pathways represented by the effector in the first and second sample graphs, the functional mechanism analysis results are checked for consistency. If the verification results are consistent, the functional mechanism analysis results are output.

[0072] 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.

[0073] 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 the information represented by the first sample graph and the second sample graph is determined to be inaccurate after verification, the inaccurate first sample graph and the second sample graph will be set as the negative cheat of the cluster analysis model, and the cluster analysis model will be updated and trained to further improve the accuracy of the cluster analysis model.

[0074] In addition, as some feasible embodiments, the present application can use methods such as CRISPR / Cas9 and RNAi to further explore the functions and mechanisms of action of these effector factors in key infestation processes of the invasive species, such as feeding and oviposition. Based on this, the expression of specific genes can be specifically inhibited or activated, thereby studying the impact of these genes on the infestation process of the cuneiform moth. At the same time, overexpression and transgenic techniques can be used to analyze the impact of these factors on the interaction between the cuneiform moth and its host plant. Through overexpression or transgenic techniques, specific genes can be overexpressed or silenced in host plants, thereby studying the impact of these genes on the interaction between the cuneiform moth and its host plant. In addition, various molecular interaction analysis methods, such as yeast two-hybrid, immunoprecipitation, and fluorescence resonance energy transfer, are simultaneously used to clarify the regulatory mechanisms of these factors on host plants and the patterns and mechanisms of their effects on the interaction between the cuneiform moth and its host plant. These molecular interaction analysis methods can help us gain a deeper understanding of the interactions between these factors and their host plants, thereby providing important clues for further research on the infestation mechanism of the cuneiform moth. Finally, bioinformatics analysis methods are used to study the evolution of these factors and their relationship with the invasion process of invasive species.

[0075] On this basis, bioinformatics analysis can be used to compare the sequence and structural characteristics of these factors in different species, thereby studying their evolutionary history. At the same time, bioinformatics analysis can also be used to 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.

[0076] The present application also provides a system embodiment that is consistent with 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.

[0077] like Figure 4 As shown, the present application provides an invasive species effect factor screening system 400, comprising:

[0078] Processing unit 402 is configured to perform mass spectrometry analysis on saliva from larvae of the gypsy moth and oviposit secretions from females to obtain protein information related to larval feeding and oviposition. Generating unit 401 is configured to generate a first sample graph of protein information based on functional classification using Kyoto Encyclopedia of Genes and Genomes (KEGG) data. The first sample graph identifies different proteins based on pixel coordinates and contains three-channel data, with each channel of the three-channel data identifying a molecular function, a cellular component, and a biological process in which the protein participates. A second sample graph of protein information is generated based on functional classification using standard gene function classification (GO) data. The second sample graph identifies different proteins based on pixel coordinates and contains two-channel data, with each channel of the two-channel data identifying a metabolic pathway and a signal transduction pathway. Processing unit 402 is further configured to determine a protein coloring map based on the first and second sample graphs. Determining unit 403 is configured to determine an effector based on the color value of each pixel position in the protein coloring map.

[0079] In one embodiment, the processing unit 402 uses the first sample image and the second sample image as input to a pre-trained cluster analysis model in the following manner to obtain a protein coloring image output by the cluster analysis model that includes three-channel data: performing channel fusion on the first sample image and the second sample image 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 to obtain a first coloring image of the first grayscale image and a second coloring image of the second grayscale image, wherein the coloring images include three-channel data for representing color. Performing image fusion on the first coloring image and the second coloring image to obtain a protein coloring image.

[0080] 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 one-to-one to the sub-networks. 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.

[0081] 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.

[0082] In one embodiment, the processing unit 402 is further configured to: in response to determining the effector, perform a functional mechanism analysis on the effector based on a target treatment method to obtain a functional mechanism analysis result. 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 involved in the protein, metabolic pathways, and signal transduction pathways represented by the effector in the first sample graph and the second sample graph, perform a consistency check on the functional mechanism analysis result, and output the functional mechanism analysis result if the check results are consistent.

[0083] 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.

[0084] Figure 5 FIG. 5 is a block diagram of an electronic device 500 for screening invasive species effect factors according to an exemplary embodiment.

[0085] 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 gypsy moth larvae and the female egg-laying secretions of the embodiment of the present application. The processor 502 is connected to the memory 501 and the I / O interface 503 respectively, for example, it can be connected via 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.

[0086] In the embodiment of the present application, the processor 502 can be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate array (FPGA), or a programmable logic array (PLA). The processor 502 can be a central processing unit (CPU) or one or a combination of other processing units with data processing capabilities and / or instruction execution capabilities.

[0087] The memory 501 in the embodiment of the present application may include one or more computer program products, which 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. The non-volatile memory may include, for example, read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0088] In the embodiment of the present application, the I / O interface 503 can be used to receive input instructions (such as digital or character information, and generate key signal input related to user settings and function control of the electronic device 500), and can also output various information to the outside (such as images or sounds). In the embodiment of the present application, the I / O interface 503 can include one or more of a physical keyboard, function keys (such as volume control keys, power keys, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, and a touch panel.

[0089] In some embodiments, the present application provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, any of the methods described above is performed.

[0090] 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, it performs any of the methods described above.

[0091] 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 obtain the desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0092] The methods and systems of the present application can be implemented using standard programming techniques, using rule-based logic or other logic to implement the various method steps. It should also be noted that the terms "system" and "module" as 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.

[0093] 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 modules are implemented using a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the steps, operations or procedures described.

[0094] The foregoing description of the implementation of the present application has been provided for purposes of illustration and description. The foregoing description is not intended to be exhaustive or to limit the present application to the precise form disclosed, and various variations and modifications are possible in accordance with the above teachings or may result from the practice of the present application. These embodiments have been selected and described in order to illustrate the principles of the present application and its practical application, so as to enable those skilled in the art to utilize the present application in various embodiments and modifications as appropriate for the particular use contemplated.

[0095] 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.

[0096] It is further understood that, unless otherwise specified, “connection” includes a direct connection where there are no other components between the two elements, and also includes an indirect connection where there are other elements between the two elements.

[0097] It should be further understood that although operations are described in a particular order in the drawings in the embodiments of the present application, this should not be construed as requiring that these operations be performed in the particular order shown or in a serial order, or that all of the illustrated operations be performed to obtain the desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0098] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to encompass any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the field of the present application that are not disclosed herein. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the scope of claims below.

[0099] 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.

[0100] 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 effector factors in the saliva of larvae and the oviposition secretions of female nymphs, comprising the following steps: Mass spectrometry analysis of saliva from larvae and oviposition secretions from female cuneiform moths revealed protein information related to larval feeding and female oviposition. generating a first sample graph of the protein information based on 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, wherein different channel data of the three-channel data are used to identify molecular functions, cellular components, and biological processes involved in the proteins, respectively; generating a second sample graph of the protein information based on functional classification of standard gene function classification GO data; wherein the second sample graph identifies different proteins based on pixel coordinates, and the second sample graph includes dual-channel data, wherein different channel data in the dual-channel data are used to identify metabolic pathways and signal transduction pathways, respectively; determining a protein coloring map based on the first sample map and the second sample map; Determining a protein coloring graph based on the first sample graph and the second sample graph 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 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 color; Performing image fusion on the first colored image and the second colored image to obtain the protein colored image; Among them, the target grayscale image is colored in the following way 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 maps each having single-channel data; The receptive fields of the two different sub-networks are different, and the feature maps correspond one-to-one to 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; 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; An effect factor is determined based on the color value of each pixel position in the protein colored map.

2. The method for screening effect factors of saliva of larvae and oviposition secretions of female cuneiform moth according to claim 1, characterized in that: Determining the effect factor based on the color value of each pixel position in the protein colored 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.

3. The method for screening effector factors of saliva of larvae and oviposition secretions of female cuneiform moth according to claim 2, 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 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.

4. A screening system for effector factors in saliva of larvae and oviposition secretions of female nymphs, comprising the following steps: A processing unit is used to perform mass spectrometry analysis on the saliva of the invasive gypsy moth larvae and the oviposition secretions of the female gypsy moth to obtain protein information related to the feeding of the gypsy moth larvae and the oviposition of the female gypsy moth; A generating unit is configured 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, the first sample graph comprises three-channel data, and different channel data in the three-channel data are respectively used to identify molecular functions, cellular components, and biological processes in which proteins participate; and generate a second sample graph of the protein information based on the functional classification of standard gene function classification (GO) data; wherein the second sample graph identifies different proteins based on pixel coordinates, the second sample graph comprises two-channel data, and different channel data in the two-channel data are respectively used to identify metabolic pathways and signal transduction pathways; The processing unit is further configured to determine a protein coloring map based on the first sample map and the second sample map; The processing unit is further specifically configured to perform 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 color; Performing image fusion on the first colored image and the second colored image to obtain the protein colored image; Among them, the target grayscale image is colored in the following way 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 maps each having single-channel data; The receptive fields of the two different sub-networks are different, and the feature maps correspond one-to-one to 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; 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; the determination unit is used to determine the effect factor based on the color value of each pixel position in the protein colored image.

5. The screening system for effector factors of saliva of larvae and oviposition secretions of female cuneiform moth according to claim 4, 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.

6. The screening system for effector factors of saliva of larvae and oviposition secretions of female cuneiform moth according to claim 5, characterized in that: The processing unit is further configured to: 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 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.

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