Virus infection risk assessment method based on procambarus clarkii TRIM23 analysis

Abstract

The invention discloses a virus infection risk assessment method based on procambarus clarkia TRI M23 analysis, which comprises the following steps: acquiring TRI M23 gene and protein expression data of procambarus clarkia in a culture cycle, performing correlation analysis through an expression sequence and a contrast sequence, assessing infection probability, and judging infection aggregation of each region based on a Moran's I index. The infection migration and the infection risk of each region are predicted, and meanwhile, a monitoring strategy and an isolation scheme are dynamically set. The method solves the problems that a traditional method depends on experience evaluation and risk analysis is high in hysteresis quality, the biological detection technology is combined, the risk prediction accuracy is remarkably improved, and a scientific basis is provided for aquaculture virus prevention and control.

Description

Technical Field

[0001] This invention relates to the field of bioanalysis and infection risk assessment, and more specifically, to a method for assessing viral infection risk based on TRIM23 analysis of Procambarus clarkii. Background Technology

[0002] The red swamp crayfish (Procambarus clarkii) is an important aquaculture species globally, but white spot syndrome virus (WSSV) infection causes severe annual economic losses. Current technologies, such as bioassays, can determine the infection status to some extent. TRIM23, as an innate immune regulator, shows a significant correlation between its expression dynamics and the incubation and outbreak phases of WSSV infection; however, current technologies lack a quantitative model for its relationship with WSSV transmission risk and an effective analytical process, resulting in weak predictive and assessment capabilities for WSSV infection and hindering precise aquaculture control and risk mitigation. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and proposes a method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii.

[0004] The first aspect of this invention provides a method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii, comprising:

[0005] S1: During the first breeding cycle, samples of Procambarus clarkii were collected from multiple breeding areas and gene and protein expression were measured to obtain gene and protein measurement data based on TRIM23.

[0006] S2: Using a breeding area as the unit of analysis, expression levels are extracted based on gene sequencing data and protein sequencing data, and a first expression sequence and a second expression sequence are formed.

[0007] S3: Conduct statistics on the aquaculture status of Procambarus clarkii in the aquaculture area and dynamically set risk weights;

[0008] S4: Sequence prediction based on a generalized linear model is performed using the first and second expressed sequences, and the obtained first and second predicted sequences are correlated with the WSSV comparison sequence for evaluation. The infection probability of the breeding area is set based on the degree of sequence correlation and risk weight.

[0009] S5: Predict virus distribution based on infection probability, determine infection clustering in each region based on Moran's I index, screen out virus migration areas through infection clustering, and set regional monitoring strategies and infection risk prediction distribution status.

[0010] S6: Apply the regional monitoring strategy to the second breeding cycle and perform expression sequence analysis of Procambarus clarkii. Analyze the actual infection distribution through expression sequence analysis. If the actual infection distribution matches the predicted infection risk distribution within the expected range, then set the monitoring priority and isolation plan for the breeding area based on the predicted infection risk distribution.

[0011] In this scheme, S1 includes:

[0012] Set the breeding cycle length and set multiple breeding areas within the overall area;

[0013] During the first breeding cycle, samples were collected and in vivo components were extracted from multiple breeding areas of Procambarus clarkii. The expression of genes and proteins in the extracts was detected, and TRIM23-based gene and protein assay data were obtained.

[0014] In this solution, S2 specifically refers to:

[0015] A breeding area was selected as the unit of analysis. Based on gene and protein assay data, expression values ​​were calibrated and extracted in the form of relative expression levels to obtain gene and protein expression data corresponding to multiple detection time points.

[0016] Gene expression data and protein expression data are sequenced based on the sampling spatial dimension and the temporal dimension to form the first expression sequence and the second expression sequence, respectively;

[0017] Data cleaning was performed on the first and second expression sequences, and outlier removal was performed based on moving average filtering.

[0018] In this solution, S3 specifically refers to:

[0019] Visual and environmental monitoring analyses were conducted on the red swamp crayfish in each aquaculture area, and multi-dimensional aquaculture statistics were performed.

[0020] Aquaculture statistics include statistics on swimming patterns, abnormal behavior, feeding behavior, and water pollution.

[0021] During the first breeding cycle, breeding statistics were conducted at each testing time point, and risk assessments were set for each testing time point based on the statistical results.

[0022] The risk estimates for each aquaculture region are averaged to obtain the risk weight for each aquaculture region.

[0023] In this solution, S4 specifically refers to:

[0024] A generalized linear model is constructed, with a Gaussian distribution chosen as the family of distributions in the model. The first expression sequence and the second expression sequence are input into the model as input sequences.

[0025] The link function is determined by Gaussian distribution, and the first 80% of the data points of the input sequence are used as training data. With time as the independent variable and numerical value as the dependent variable, linear fitting is performed to obtain the fitting equation.

[0026] The last 20% of the data points in the input sequence are used as test and validation data. The fitted equation is trained for prediction. For the prediction sequence, the corresponding nonlinear part is identified based on linear regression. The nonlinear part is linearly transformed through the link function. At the same time, the mean square error is used to calculate the prediction loss and optimize the model until the loss is reduced to the expected range.

[0027] The trained model is used to predict the first and second expression sequences. The prediction length is one breeding cycle, and the first and second predicted sequences are obtained respectively.

[0028] In this solution, S4 further includes:

[0029] Dimensional processing was performed on gene expression values ​​and protein expression values ​​to map the data of the two dimensions to the same numerical range and establish a standardized mapping relationship.

[0030] Based on the standardized mapping relationship, the first and second prediction sequences are dimensionalized.

[0031] Gene and protein expression were determined based on TRIM23 in *Procambarus clarkii* confirmed to be infected with WSSV, and comparative sequences were generated.

[0032] Based on the grey relational analysis method, the correlation degree between the first predicted sequence and the comparison sequence, and the correlation degree between the second predicted sequence and the comparison sequence are calculated, and the first correlation degree and the second correlation degree are obtained respectively.

[0033] The infection probability of the breeding area is generated by comprehensively evaluating the first correlation degree, the second correlation degree, and the risk weight.

[0034] In this solution, S5 specifically refers to:

[0035] Virus distribution analysis and risk prediction were conducted based on the infection probability of each breeding area. Based on Moran's I index, the spatial autocorrelation of the infection probability in each breeding area was calculated, and the infection clustering was assessed. Areas with high clustering were screened out and marked as virus migration areas.

[0036] The Moran's I index is used to set monitoring priorities for each virus migration area, and regional monitoring strategies are set based on these priorities.

[0037] By analyzing the spatial autocorrelation and clustering of infection probabilities, the overall regional infection risk prediction distribution is established.

[0038] In this solution, S6 specifically refers to:

[0039] The regional monitoring strategy was applied to the second breeding cycle, and the expression sequence of Procambarus clarkii was analyzed. The infection probability was calculated and the actual infection distribution was analyzed based on the expression sequence.

[0040] The overall regional prediction accuracy is assessed by comparing the actual infection distribution with the predicted infection risk distribution. The accuracy assessment process is based on a comprehensive evaluation of the infection probability, risk, and migration trend of each aquaculture area to determine the prediction accuracy.

[0041] If the consistency is within the expected range, then the monitoring priority of the breeding area is set and the isolation plan is set based on the distribution status of the infection risk prediction.

[0042] A second aspect of the present invention also provides a virus infection risk assessment system based on TRIM23 analysis of Procambarus clarkii. The system includes a memory and a processor. The memory includes a virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii. When the processor executes the virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii, it performs the following steps:

[0043] S1: During the first breeding cycle, samples of Procambarus clarkii were collected from multiple breeding areas and gene and protein expression were measured to obtain gene and protein measurement data based on TRIM23.

[0044] S2: Using a breeding area as the unit of analysis, expression levels are extracted based on gene sequencing data and protein sequencing data, and a first expression sequence and a second expression sequence are formed.

[0045] S3: Conduct statistics on the aquaculture status of Procambarus clarkii in the aquaculture area and dynamically set risk weights;

[0046] S4: Sequence prediction based on a generalized linear model is performed using the first and second expressed sequences, and the obtained first and second predicted sequences are correlated with the WSSV comparison sequence for evaluation. The infection probability of the breeding area is set based on the degree of sequence correlation and risk weight.

[0047] S5: Predict virus distribution based on infection probability, determine infection clustering in each region based on Moran's I index, screen out virus migration areas through infection clustering, and set regional monitoring strategies and infection risk prediction distribution status.

[0048] S6: Apply the regional monitoring strategy to the second breeding cycle and perform expression sequence analysis of Procambarus clarkii. Analyze the actual infection distribution through expression sequence analysis. If the actual infection distribution matches the predicted infection risk distribution within the expected range, then set the monitoring priority and isolation plan for the breeding area based on the predicted infection risk distribution.

[0049] A third aspect of the present invention also provides a computer-readable storage medium comprising a virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii, wherein when the virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii is executed by a processor, the program implements the steps of the virus infection risk assessment method based on TRIM23 analysis of Procambarus clarkii as described in any of the preceding claims.

[0050] This invention discloses a method for assessing viral infection risk based on TRIM23 analysis in *Procambarus clarkii* (red swamp crayfish). The method includes: collecting TRIM23 gene and protein expression data of *Procambarus clarkii* during the aquaculture cycle; conducting association analysis between expressed sequences and control sequences to assess the infection probability; determining infection clustering in each region based on Moran's I index; predicting infection migration and infection risk in each region; and dynamically setting monitoring strategies and isolation plans. This invention solves the problems of traditional methods relying on experience-based assessments and having significant lag in risk analysis. By combining biodetection technology, it significantly improves the accuracy of risk prediction, providing a scientific basis for virus prevention and control in aquaculture. Attached Figure Description

[0051] Figure 1 A flowchart of a viral infection risk assessment method based on TRIM23 analysis of red swamp crayfish according to the present invention is shown;

[0052] Figure 2 A block diagram of a virus infection risk assessment system based on TRIM23 analysis of Procambarus clarkii is shown. Detailed Implementation

[0053] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0055] Figure 1 A flowchart of a viral infection risk assessment method based on TRIM23 analysis of red swamp crayfish according to the present invention is shown.

[0056] like Figure 1 As shown, the first aspect of this invention provides a method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii, comprising:

[0057] S1: During the first breeding cycle, samples of Procambarus clarkii were collected from multiple breeding areas and gene and protein expression were measured to obtain gene and protein measurement data based on TRIM23.

[0058] S2: Using a breeding area as the unit of analysis, expression levels are extracted based on gene sequencing data and protein sequencing data, and a first expression sequence and a second expression sequence are formed.

[0059] S3: Conduct statistics on the aquaculture status of Procambarus clarkii in the aquaculture area and dynamically set risk weights;

[0060] S4: Sequence prediction based on a generalized linear model is performed using the first and second expressed sequences, and the obtained first and second predicted sequences are correlated with the WSSV comparison sequence for evaluation. The infection probability of the breeding area is set based on the degree of sequence correlation and risk weight.

[0061] S5: Predict virus distribution based on infection probability, determine infection clustering in each region based on Moran's I index, screen out virus migration areas through infection clustering, and set regional monitoring strategies and infection risk prediction distribution status.

[0062] S6: Apply the regional monitoring strategy to the second breeding cycle and perform expression sequence analysis of Procambarus clarkii. Analyze the actual infection distribution through expression sequence analysis. If the actual infection distribution matches the predicted infection risk distribution within the expected range, then set the monitoring priority and isolation plan for the breeding area based on the predicted infection risk distribution.

[0063] It is worth mentioning that existing WSSV virus risk assessment methods mostly rely on single indicators (such as mortality rate, environmental factors) or empirical judgments, without combining host gene expression characteristics, and cannot reflect the molecular mechanism of virus-host interaction. Moreover, the breeding environment and virus transmission have spatiotemporal heterogeneity, making it difficult for traditional methods to adjust the assessment process in real time, lacking dynamic analysis capabilities, and making it difficult to achieve precise breeding control and risk avoidance.

[0064] Based on this, the present invention uses sampling and detection of Procambarus clarkii, performs sequence analysis on gene and protein expression information in a dual dimension, evaluates the association between the expressed sequences and WSSV infection characteristic sequences, quantifies the infection correlation between regions, sets the infection probability distribution, and further performs continuous cycle migration prediction and infection distribution confirmation through spatiotemporal probability distribution analysis and prediction. Migration areas are screened out and breeding isolation plans are set, which effectively improves the rationality of the allocation of prevention and control resources. Through cross-cycle strategy iteration, the accuracy of the prediction model is effectively verified, solving the technical problems of strong lag and low accuracy in traditional methods of infection risk assessment.

[0065] According to an embodiment of the present invention, S1 includes:

[0066] Set the breeding cycle length and set multiple breeding areas within the overall area;

[0067] During the first breeding cycle, samples were collected and in vivo components were extracted from multiple breeding areas of Procambarus clarkii. The expression of genes and proteins in the extracts was detected, and TRIM23-based gene and protein assay data were obtained.

[0068] It should be noted that the breeding cycle is set based on the virus analysis requirements of the red swamp crayfish. The cycle length can be set based on historical breeding data for more refined virus monitoring and analysis. Internal components can be measured from crayfish tissues such as glands, muscles, and gills. Genetic analysis can be based on qPCR of the TRIM23 gene, and protein expression can be detected using Western blotting.

[0069] According to an embodiment of the present invention, step S2 specifically includes:

[0070] A breeding area was selected as the unit of analysis. Based on gene and protein assay data, expression values ​​were calibrated and extracted in the form of relative expression levels to obtain gene and protein expression data corresponding to multiple detection time points.

[0071] Gene expression data and protein expression data are sequenced based on the sampling spatial dimension and the temporal dimension to form the first expression sequence and the second expression sequence, respectively;

[0072] Data cleaning was performed on the first and second expression sequences, and outlier removal was performed based on moving average filtering.

[0073] It should be noted that this invention uses both gene expression data and protein expression data to track, detect, compare, and assess the infection risk of a specific virus (WSSV), enabling more accurate infection prediction. Each farming cycle includes multiple detection time points, and multiple crayfish samples can be collected for testing and data sequencing at each time point. The sampling spatial dimension, i.e., the sampling order of consecutive points in a farming area, sorts the detection data, followed by the temporal dimension sorting.

[0074] According to an embodiment of the present invention, S3 specifically includes:

[0075] Visual and environmental monitoring analyses were conducted on the red swamp crayfish in each aquaculture area, and multi-dimensional aquaculture statistics were performed.

[0076] Aquaculture statistics include statistics on swimming patterns, abnormal behavior, feeding behavior, and water pollution.

[0077] During the first breeding cycle, breeding statistics were conducted at each testing time point, and risk assessments were set for each testing time point based on the statistical results.

[0078] The risk estimates for each aquaculture region are averaged to obtain the risk weight for each aquaculture region.

[0079] It should be noted that the visual monitoring refers to aquaculture monitoring based on camera devices to determine the behavioral patterns and states of the red swamp crayfish, such as swimming trajectory and planktonic behavior. Feeding status includes feed conversion rate and uneaten feed ratio. Risk weights are used to initially reflect the general probability of viral infection. Risk weights can be set based on statistical analysis at multiple detection time points (this process requires multiple data collections), or based on a single time point in the first aquaculture cycle (this process involves a single statistical data collection). The specific risk weight setting depends on the analytical requirements.

[0080] In aquaculture statistics, swimming trajectory includes the average swimming distance per unit time, the number of planktonic animals, abnormal behavior reflected by cluster density, feeding response time, feeding status including feed conversion rate and uneaten feed ratio, and water pollution status including the concentration of pollutants in the calibrated water body.

[0081] According to an embodiment of the present invention, step S4 specifically includes:

[0082] A generalized linear model is constructed, with a Gaussian distribution chosen as the family of distributions in the model. The first expression sequence and the second expression sequence are input into the model as input sequences.

[0083] The link function is determined by Gaussian distribution, and the first 80% of the data points of the input sequence are used as training data. With time as the independent variable and numerical value as the dependent variable, linear fitting is performed to obtain the fitting equation.

[0084] The last 20% of the data points in the input sequence are used as test and validation data. The fitted equation is trained for prediction. For the prediction sequence, the corresponding nonlinear part is identified based on linear regression. The nonlinear part is linearly transformed through the link function. At the same time, the mean square error is used to calculate the prediction loss and optimize the model until the loss is reduced to the expected range.

[0085] The trained model is used to predict the first and second expression sequences. The prediction length is one breeding cycle, and the first and second predicted sequences are obtained respectively.

[0086] It should be noted that the link function uses identity linking.

[0087] The fitting equation is specifically F(t) = K·t + ​​Q, where F(t) is the dependent variable, i.e., the numerical value, t is the independent variable, and K and Q are the fitting coefficients.

[0088] The probability density function corresponding to the Gaussian distribution is:

[0089]

[0090] Where y is a variable that follows a distribution, σ is the conditional variance, μ is the conditional mean, and exp is the natural exponential function.

[0091] The identity link function is as follows:

[0092] g(μ) = μ,

[0093] Where μ is the conditional mean and g(μ) is the link function.

[0094] According to an embodiment of the present invention, step S4 further includes:

[0095] Dimensional processing was performed on gene expression values ​​and protein expression values ​​to map the data of the two dimensions to the same numerical range and establish a standardized mapping relationship.

[0096] Based on the standardized mapping relationship, the first and second prediction sequences are dimensionalized.

[0097] Gene and protein expression were determined based on TRIM23 in *Procambarus clarkii* confirmed to be infected with WSSV, and comparative sequences were generated.

[0098] Based on the grey relational analysis method, the correlation degree between the first predicted sequence and the comparison sequence, and the correlation degree between the second predicted sequence and the comparison sequence are calculated, and the first correlation degree and the second correlation degree are obtained respectively.

[0099] The infection probability of the breeding area is generated by comprehensively evaluating the first correlation degree, the second correlation degree, and the risk weight.

[0100] It should be noted that the standardized mapping relationship includes mapping and transforming the values ​​in gene expression sequences and protein expression sequences (e.g., the first expression sequence and the second expression sequence) to obtain data within a unified data range, such as a unified expression level set from 0 to 100%.

[0101] In the comparative sequence analysis, two sequences based on TRIM23 gene and protein expression assays were dimensionally processed and then weighted to obtain a comparative sequence. This comparative sequence was used to average TRIM23 expression levels and for sequence correlation analysis. The infection probability was proportional to the first association degree, the second association degree, and the risk weight.

[0102] According to an embodiment of the present invention, step S5 specifically includes:

[0103] Virus distribution analysis and risk prediction were conducted based on the infection probability of each breeding area. Based on Moran's I index, the spatial autocorrelation of the infection probability in each breeding area was calculated, and the infection clustering was assessed. Areas with high clustering were screened out and marked as virus migration areas.

[0104] The Moran's I index is used to set monitoring priorities for each virus migration area, and regional monitoring strategies are set based on these priorities.

[0105] By analyzing the spatial autocorrelation and clustering of infection probabilities, the overall regional infection risk prediction distribution is established.

[0106] It should be noted that virus migration areas are selected from aquaculture areas, typically including multiple areas. The Moran's I index effectively reflects infection clustering, and monitoring priorities are set based on the strength of clustering; areas with stronger clustering have higher monitoring priority. High-clustering areas are determined and selected based on a preset index threshold using the Moran's I index. The monitoring strategy includes a priority-based biological detection sequence for biological detection and distribution status assessment in the next aquaculture cycle. By analyzing the spatial autocorrelation of infection probability, infection clustering, and the magnitude of the Moran's I index, infection migration and infection probability distribution can be predicted for the entire region (i.e., the sum of all aquaculture areas), and an infection risk prediction distribution status can be set. This infection risk prediction distribution status is used for prediction consistency matching in the next aquaculture cycle and for setting efficient isolation measures.

[0107] According to an embodiment of the present invention, S6 specifically includes:

[0108] The regional monitoring strategy was applied to the second breeding cycle, and the expression sequence of Procambarus clarkii was analyzed. The infection probability was calculated and the actual infection distribution was analyzed based on the expression sequence.

[0109] The overall regional prediction accuracy is assessed by comparing the actual infection distribution with the predicted infection risk distribution. The accuracy assessment process is based on a comprehensive evaluation of the infection probability, risk, and migration trend of each aquaculture area to determine the prediction accuracy.

[0110] If the consistency is within the expected range, then the monitoring priority of the breeding area is set and the isolation plan is set based on the distribution status of the infection risk prediction.

[0111] It should be noted that the actual infection distribution is obtained through a comprehensive analysis of the overall region based on the infection probability of each breeding area. In the prediction consistency assessment, the actual state of the predicted infection status in each breeding area can be analyzed to determine the prediction accuracy of each breeding area, and a prediction consistency based on a comprehensive analysis of the entire region can be obtained.

[0112] The isolation plan is set based on monitoring priorities and infection and migration status. It is used to regulate aquaculture conditions, reduce the risk of WSSV infection outbreaks, and has a certain degree of foresight and predictability.

[0113] According to an embodiment of the present invention, based on the grey relational analysis method, the correlation degree between the first predicted sequence and the comparison sequence, and the correlation degree between the second predicted sequence and the comparison sequence are calculated, and the first correlation degree and the second correlation degree are obtained respectively, specifically including:

[0114] Calculate the absolute difference, minimum difference, and maximum difference between the first predicted sequence, the second predicted sequence, and the comparison sequence;

[0115] The first and second predicted sequences are used as comparison sequences, and the comparison sequence is used as a reference sequence.

[0116] Based on the grey relational analysis method, the correlation coefficient of each data point between the comparison sequence and the reference sequence is calculated. The risk valuation is used as the weight value, and the correlation coefficients of all data points are weighted and averaged to obtain the correlation degree.

[0117] The first correlation degree and the second correlation degree were obtained through two correlation analyses.

[0118] It should be noted that the first correlation and the second correlation correspond to the analysis processes of the first and second prediction sequences, respectively. In the expression sequence, each data point corresponds to a detection time node, and each detection time node corresponds to a risk assessment.

[0119] Figure 2 A block diagram of a virus infection risk assessment system based on TRIM23 analysis of Procambarus clarkii is shown.

[0120] A second aspect of the present invention also provides a virus infection risk assessment system 2 based on TRIM23 analysis of Procambarus clarkii. The system includes a memory 21 and a processor 22. The memory 21 includes a virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii. When the processor 22 executes the virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii, it performs the following steps:

[0121] S1: During the first breeding cycle, samples of Procambarus clarkii were collected from multiple breeding areas and gene and protein expression were measured to obtain gene and protein measurement data based on TRIM23.

[0122] S2: Using a breeding area as the unit of analysis, expression levels are extracted based on gene sequencing data and protein sequencing data, and a first expression sequence and a second expression sequence are formed.

[0123] S3: Conduct statistics on the aquaculture status of Procambarus clarkii in the aquaculture area and dynamically set risk weights;

[0124] S4: Sequence prediction based on a generalized linear model is performed using the first and second expressed sequences, and the obtained first and second predicted sequences are correlated with the WSSV comparison sequence for evaluation. The infection probability of the breeding area is set based on the degree of sequence correlation and risk weight.

[0125] S5: Predict virus distribution based on infection probability, determine infection clustering in each region based on Moran's I index, screen out virus migration areas through infection clustering, and set regional monitoring strategies and infection risk prediction distribution status.

[0126] S6: Apply the regional monitoring strategy to the second breeding cycle and perform expression sequence analysis of Procambarus clarkii. Analyze the actual infection distribution through expression sequence analysis. If the actual infection distribution matches the predicted infection risk distribution within the expected range, then set the monitoring priority and isolation plan for the breeding area based on the predicted infection risk distribution.

[0127] It is worth mentioning that existing WSSV virus risk assessment methods mostly rely on single indicators (such as mortality rate, environmental factors) or empirical judgments, without combining host gene expression characteristics, and cannot reflect the molecular mechanism of virus-host interaction. Moreover, the breeding environment and virus transmission have spatiotemporal heterogeneity, making it difficult for traditional methods to adjust the assessment process in real time, lacking dynamic analysis capabilities, and making it difficult to achieve precise breeding control and risk avoidance.

[0128] Based on this, the present invention uses sampling and detection of Procambarus clarkii, performs sequence analysis on gene and protein expression information in a dual dimension, evaluates the association between the expressed sequences and WSSV infection characteristic sequences, quantifies the infection correlation between regions, sets the infection probability distribution, and further performs continuous cycle migration prediction and infection distribution confirmation through spatiotemporal probability distribution analysis and prediction. Migration areas are screened out and breeding isolation plans are set, which effectively improves the rationality of the allocation of prevention and control resources. Through cross-cycle strategy iteration, the accuracy of the prediction model is effectively verified, solving the technical problems of strong lag and low accuracy in traditional methods of infection risk assessment.

[0129] According to an embodiment of the present invention, S1 includes:

[0130] Set the breeding cycle length and set multiple breeding areas within the overall area;

[0131] During the first breeding cycle, samples were collected and in vivo components were extracted from multiple breeding areas of Procambarus clarkii. The expression of genes and proteins in the extracts was detected, and TRIM23-based gene and protein assay data were obtained.

[0132] It should be noted that the breeding cycle is set based on the virus analysis requirements of the red swamp crayfish. The cycle length can be set based on historical breeding data for more refined virus monitoring and analysis. Internal components can be measured from crayfish tissues such as glands, muscles, and gills. Genetic analysis can be based on qPCR of the TRIM23 gene, and protein expression can be detected using Western blotting.

[0133] According to an embodiment of the present invention, step S2 specifically includes:

[0134] A breeding area was selected as the unit of analysis. Based on gene and protein assay data, expression values ​​were calibrated and extracted in the form of relative expression levels to obtain gene and protein expression data corresponding to multiple detection time points.

[0135] Gene expression data and protein expression data are sequenced based on the sampling spatial dimension and the temporal dimension to form the first expression sequence and the second expression sequence, respectively;

[0136] Data cleaning was performed on the first and second expression sequences, and outlier removal was performed based on moving average filtering.

[0137] It should be noted that this invention uses both gene expression data and protein expression data to track, detect, compare, and assess the infection risk of a specific virus (WSSV), enabling more accurate infection prediction. Each farming cycle includes multiple detection time points, and multiple crayfish samples can be collected for testing and data sequencing at each time point. The sampling spatial dimension, i.e., the sampling order of consecutive points in a farming area, sorts the detection data, followed by the temporal dimension sorting.

[0138] According to an embodiment of the present invention, S3 specifically includes:

[0139] Visual and environmental monitoring analyses were conducted on the red swamp crayfish in each aquaculture area, and multi-dimensional aquaculture statistics were performed.

[0140] Aquaculture statistics include statistics on swimming patterns, abnormal behavior, feeding behavior, and water pollution.

[0141] During the first breeding cycle, breeding statistics were conducted at each testing time point, and risk assessments were set for each testing time point based on the statistical results.

[0142] The risk estimates for each aquaculture region are averaged to obtain the risk weight for each aquaculture region.

[0143] It should be noted that the visual monitoring refers to aquaculture monitoring based on camera devices to determine the behavioral patterns and states of the red swamp crayfish, such as swimming trajectory and planktonic behavior. Feeding status includes feed conversion rate and uneaten feed ratio. Risk weights are used to initially reflect the general probability of viral infection. Risk weights can be set based on statistical analysis at multiple detection time points (this process requires multiple data collections), or based on a single time point in the first aquaculture cycle (this process involves a single statistical data collection). The specific risk weight setting depends on the analytical requirements.

[0144] According to an embodiment of the present invention, step S4 specifically includes:

[0145] A generalized linear model is constructed, with a Gaussian distribution chosen as the family of distributions in the model. The first expression sequence and the second expression sequence are input into the model as input sequences.

[0146] The link function is determined by Gaussian distribution, and the first 80% of the data points of the input sequence are used as training data. With time as the independent variable and numerical value as the dependent variable, linear fitting is performed to obtain the fitting equation.

[0147] The last 20% of the data points in the input sequence are used as test and validation data. The fitted equation is trained for prediction. For the prediction sequence, the corresponding nonlinear part is identified based on linear regression. The nonlinear part is linearly transformed through the link function. At the same time, the mean square error is used to calculate the prediction loss and optimize the model until the loss is reduced to the expected range.

[0148] The trained model is used to predict the first and second expression sequences. The prediction length is one breeding cycle, and the first and second predicted sequences are obtained respectively.

[0149] It should be noted that the link function uses identity linking.

[0150] According to an embodiment of the present invention, step S4 further includes:

[0151] Dimensional processing was performed on gene expression values ​​and protein expression values ​​to map the data of the two dimensions to the same numerical range and establish a standardized mapping relationship.

[0152] Based on the standardized mapping relationship, the first and second prediction sequences are dimensionalized.

[0153] Gene and protein expression were determined based on TRIM23 in *Procambarus clarkii* confirmed to be infected with WSSV, and comparative sequences were generated.

[0154] Based on the grey relational analysis method, the correlation degree between the first predicted sequence and the comparison sequence, and the correlation degree between the second predicted sequence and the comparison sequence are calculated, and the first correlation degree and the second correlation degree are obtained respectively.

[0155] The infection probability of the breeding area is generated by comprehensively evaluating the first correlation degree, the second correlation degree, and the risk weight.

[0156] It should be noted that the standardized mapping relationship includes mapping and transforming the values ​​in gene expression sequences and protein expression sequences (e.g., the first expression sequence and the second expression sequence) to obtain data within a unified data range, such as a unified expression level set from 0 to 100%.

[0157] In the comparative sequence analysis, two sequences based on TRIM23 gene and protein expression assays were dimensionally processed and then weighted to obtain a comparative sequence. This comparative sequence was used to average TRIM23 expression levels and for sequence correlation analysis. The infection probability was proportional to the first association degree, the second association degree, and the risk weight.

[0158] According to an embodiment of the present invention, step S5 specifically includes:

[0159] Virus distribution analysis and risk prediction were conducted based on the infection probability of each breeding area. Based on Moran's I index, the spatial autocorrelation of the infection probability in each breeding area was calculated, and the infection clustering was assessed. Areas with high clustering were screened out and marked as virus migration areas.

[0160] The Moran's I index is used to set monitoring priorities for each virus migration area, and regional monitoring strategies are set based on these priorities.

[0161] By analyzing the spatial autocorrelation and clustering of infection probabilities, the overall regional infection risk prediction distribution is established.

[0162] It should be noted that virus migration areas are selected from aquaculture areas, typically including multiple areas. The Moran's I index effectively reflects infection clustering, and monitoring priorities are set based on the strength of clustering; areas with stronger clustering have higher monitoring priority. High-clustering areas are determined and selected based on a preset index threshold using the Moran's I index. The monitoring strategy includes a priority-based biological detection sequence for biological detection and distribution status assessment in the next aquaculture cycle. By analyzing the spatial autocorrelation of infection probability, infection clustering, and the magnitude of the Moran's I index, infection migration and infection probability distribution can be predicted for the entire region (i.e., the sum of all aquaculture areas), and an infection risk prediction distribution status can be set. This infection risk prediction distribution status is used for prediction consistency matching in the next aquaculture cycle and for setting efficient isolation measures.

[0163] According to an embodiment of the present invention, S6 specifically includes:

[0164] The regional monitoring strategy was applied to the second breeding cycle, and the expression sequence of Procambarus clarkii was analyzed. The infection probability was calculated and the actual infection distribution was analyzed based on the expression sequence.

[0165] The overall regional prediction accuracy is assessed by comparing the actual infection distribution with the predicted infection risk distribution. The accuracy assessment process is based on a comprehensive evaluation of the infection probability, risk, and migration trend of each aquaculture area to determine the prediction accuracy.

[0166] If the consistency is within the expected range, then the monitoring priority of the breeding area is set and the isolation plan is set based on the distribution status of the infection risk prediction.

[0167] It should be noted that the actual infection distribution is obtained through a comprehensive analysis of the overall region based on the infection probability of each breeding area. In the prediction consistency assessment, the actual state of the predicted infection status in each breeding area can be analyzed to determine the prediction accuracy of each breeding area, and a prediction consistency based on a comprehensive analysis of the entire region can be obtained.

[0168] The isolation plan is set based on monitoring priorities and infection and migration status. It is used to regulate aquaculture conditions, reduce the risk of WSSV infection outbreaks, and has a certain degree of foresight and predictability.

[0169] A third aspect of the present invention also provides a computer-readable storage medium comprising a virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii, wherein when the virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii is executed by a processor, the program implements the steps of the virus infection risk assessment method based on TRIM23 analysis of Procambarus clarkii as described in any of the preceding claims.

[0170] This invention discloses a method for assessing viral infection risk based on TRIM23 analysis in *Procambarus clarkii* (red swamp crayfish). The method includes: collecting TRIM23 gene and protein expression data of *Procambarus clarkii* during the aquaculture cycle; conducting association analysis between expressed sequences and control sequences to assess the infection probability; determining infection clustering in each region based on Moran's I index; predicting infection migration and infection risk in each region; and dynamically setting monitoring strategies and isolation plans. This invention solves the problems of traditional methods relying on experience-based assessments and having significant lag in risk analysis. By combining biodetection technology, it significantly improves the accuracy of risk prediction, providing a scientific basis for virus prevention and control in aquaculture.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0172] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0174] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0175] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0176] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii, characterized in that, include: S1: During the first breeding cycle, samples of Procambarus clarkii were collected from multiple breeding areas and gene and protein expression were measured to obtain gene and protein measurement data based on TRIM23. S2: Using a breeding area as the unit of analysis, expression levels are extracted based on gene sequencing data and protein sequencing data, and a first expression sequence and a second expression sequence are formed. S3: Conduct statistics on the aquaculture status of Procambarus clarkii in the aquaculture area and dynamically set risk weights; S4: Sequence prediction based on a generalized linear model is performed using the first and second expressed sequences, and the obtained first and second predicted sequences are correlated with the WSSV comparison sequence for evaluation. The infection probability of the breeding area is set based on the degree of sequence correlation and risk weight. S5: Predict virus distribution based on infection probability, determine infection clustering in each region based on Moran's I index, screen out virus migration areas through infection clustering, and set regional monitoring strategies and infection risk prediction distribution status. S6: Apply the regional monitoring strategy to the second breeding cycle and perform expression sequence analysis of Procambarus clarkii. Analyze the actual infection distribution status through expression sequence analysis. If the actual infection distribution status matches the predicted infection risk distribution status within the expected range, then set the monitoring priority and isolation plan for the breeding area based on the predicted infection risk distribution status. Specifically, S3 is: Visual and environmental monitoring analyses were conducted on the red swamp crayfish in each aquaculture area, and multi-dimensional aquaculture statistics were performed. Aquaculture statistics include statistics on swimming patterns, abnormal behavior, feeding behavior, and water pollution. During the first breeding cycle, breeding statistics were conducted at each testing time point, and risk assessments were set for each testing time point based on the statistical results. The risk estimates for each aquaculture region are averaged to obtain the risk weight for each aquaculture region.

2. The method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii according to claim 1, characterized in that, S1 includes: Set the breeding cycle length and set multiple breeding areas within the overall area; During the first breeding cycle, samples were collected and in vivo components were extracted from multiple breeding areas of Procambarus clarkii. The expression of genes and proteins in the extracts was detected, and TRIM23-based gene and protein assay data were obtained.

3. The method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii according to claim 1, characterized in that, Specifically, S2 is: A breeding area was selected as the unit of analysis. Based on gene and protein assay data, expression values ​​were calibrated and extracted in the form of relative expression levels to obtain gene and protein expression data corresponding to multiple detection time points. Gene expression data and protein expression data are sequenced based on the sampling spatial dimension and the temporal dimension to form the first expression sequence and the second expression sequence, respectively; Data cleaning was performed on the first and second expression sequences, and outlier removal was performed based on moving average filtering.

4. The method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii according to claim 1, characterized in that, Specifically, S4 is: A generalized linear model is constructed, with a Gaussian distribution chosen as the family of distributions in the model. The first expression sequence and the second expression sequence are input into the model as input sequences. The link function is determined by Gaussian distribution, and the first 80% of the data points of the input sequence are used as training data. With time as the independent variable and numerical value as the dependent variable, linear fitting is performed to obtain the fitting equation. The last 20% of the data points in the input sequence are used as test and validation data. The fitted equation is trained for prediction. For the prediction sequence, the corresponding nonlinear part is identified based on linear regression. The nonlinear part is linearly transformed through the link function. At the same time, the mean square error is used to calculate the prediction loss and optimize the model until the loss is reduced to the expected range. The trained model is used to predict the first and second expression sequences. The prediction length is one breeding cycle, and the first and second predicted sequences are obtained respectively.

5. The method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii according to claim 4, characterized in that, The S4 further includes: Dimensional processing was performed on gene expression values ​​and protein expression values ​​to map the data of the two dimensions to the same numerical range and establish a standardized mapping relationship. Based on the standardized mapping relationship, the first and second prediction sequences are dimensionalized. Gene and protein expression were determined based on TRIM23 in *Procambarus clarkii* confirmed to be infected with WSSV, and comparative sequences were generated. Based on the grey relational analysis method, the correlation degree between the first predicted sequence and the comparison sequence, and the correlation degree between the second predicted sequence and the comparison sequence are calculated, and the first correlation degree and the second correlation degree are obtained respectively. The infection probability of the breeding area is generated by comprehensively evaluating the first correlation degree, the second correlation degree, and the risk weight.

6. The method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii according to claim 1, characterized in that, Specifically, S5 is: Virus distribution analysis and risk prediction were conducted based on the infection probability of each breeding area. Based on Moran's I index, the spatial autocorrelation of the infection probability in each breeding area was calculated, and the infection clustering was assessed. Areas with high clustering were screened out and marked as virus migration areas. The Moran's I index is used to set monitoring priorities for each virus migration area, and regional monitoring strategies are set based on these priorities. By analyzing the spatial autocorrelation and clustering of infection probabilities, the overall regional infection risk prediction distribution is established.

7. The method for assessing the risk of viral infection based on TRIM23 analysis of Procambarus clarkii according to claim 6, characterized in that, Specifically, S6 is: The regional monitoring strategy was applied to the second breeding cycle, and the expression sequence of Procambarus clarkii was analyzed. The infection probability was calculated and the actual infection distribution was analyzed based on the expression sequence. The overall regional prediction accuracy is assessed by comparing the actual infection distribution with the predicted infection risk distribution. The accuracy assessment process is based on a comprehensive evaluation of the infection probability, risk, and migration trend of each aquaculture area to determine the prediction accuracy. If the consistency is within the expected range, then the monitoring priority of the breeding area is set and the isolation plan is set based on the distribution status of the infection risk prediction.

8. A virus infection risk assessment system based on TRIM23 analysis of Procambarus clarkii, characterized in that, The system includes a memory and a processor. The memory includes a virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii. When the processor executes the virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii, it performs the following steps: S1: During the first breeding cycle, samples of Procambarus clarkii were collected from multiple breeding areas and gene and protein expression were measured to obtain gene and protein measurement data based on TRIM23. S2: Using a breeding area as the unit of analysis, expression levels are extracted based on gene sequencing data and protein sequencing data, and a first expression sequence and a second expression sequence are formed. S3: Conduct statistics on the aquaculture status of Procambarus clarkii in the aquaculture area and dynamically set risk weights; S4: Sequence prediction based on a generalized linear model is performed using the first and second expressed sequences, and the obtained first and second predicted sequences are correlated with the WSSV comparison sequence for evaluation. The infection probability of the breeding area is set based on the degree of sequence correlation and risk weight. S5: Predict virus distribution based on infection probability, determine infection clustering in each region based on Moran's I index, screen out virus migration areas through infection clustering, and set regional monitoring strategies and infection risk prediction distribution status. S6: Apply the regional monitoring strategy to the second breeding cycle and perform expression sequence analysis of Procambarus clarkii. Analyze the actual infection distribution status through expression sequence analysis. If the actual infection distribution status matches the predicted infection risk distribution status within the expected range, then set the monitoring priority and isolation plan for the breeding area based on the predicted infection risk distribution status. Specifically, S3 is: Visual and environmental monitoring analyses were conducted on the red swamp crayfish in each aquaculture area, and multi-dimensional aquaculture statistics were performed. Aquaculture statistics include statistics on swimming patterns, abnormal behavior, feeding behavior, and water pollution. During the first breeding cycle, breeding statistics were conducted at each testing time point, and risk assessments were set for each testing time point based on the statistical results. The risk estimates for each aquaculture region are averaged to obtain the risk weight for each aquaculture region.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii. When the virus infection risk assessment program based on TRIM23 analysis of Procambarus clarkii is executed by a processor, it implements the steps of the virus infection risk assessment method based on TRIM23 analysis of Procambarus clarkii as described in any one of claims 1 to 7.

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