Screening and dosage determination method of grape heat resistance inducer

Abstract

The invention discloses a screening and dosage determining method of a grape heat resistance inducer, and belongs to the technical field of plant stress resistance physiology and cultivation. The method comprises the following steps: soaking grape leaf wafers in inducers with different concentrations, and carrying out heat treatment under a plurality of temperature gradients; determining a chlorophyll OJIP rapid fluorescence parameter and constructing a heat resistance comprehensive index, and further calculating a normalized relative heat resistance improvement value; fitting a quantitative relation with the concentration by using a single-peak dose-effect model containing a high-dose inhibition item; statistical inference is carried out by adopting Bootstrap resampling, and a stable and effective recommended application concentration in a wide temperature range is finally determined by analyzing a common intersection or a statistical median of effective concentration intervals under multiple temperatures. The method realizes indoor rapid and quantitative screening, can realize quantitative comparison of induction intensity and dose sensitivity of different inducers under a unified evaluation system, and can be used for identifying heat resistance of grapes.

Description

Technical Field

[0001] This invention relates to the field of plant stress resistance physiology and cultivation technology, specifically to a method for screening and determining the dosage of a grape heat resistance inducer. Background Technology

[0002] Grapes grown in greenhouses or open fields in hot regions often encounter intermittent heat waves or continuous high-temperature stress. High temperatures can easily damage the photosystem of grape leaves. Damage to the donor side, disruption of the electron transport chain, and the dissipation of excess excitation energy as heat all contribute to a decrease in photosynthetic efficiency. If this heat damage continues to accumulate, it will severely impact tree vigor, fruit yield, and quality.

[0003] To mitigate the damage caused by high temperatures, research and practice often involve applying exogenous inducers to enhance the heat resistance of grapes, such as γ-aminobutyric acid (GABA), abscisic acid (ABA), polyamines (e.g., spermidine, SPD), and salicylic acid (SA). However, in practical applications, the same inducer often exhibits inconsistent effective concentration ranges, unstable optimal concentrations, and potential inhibitory effects at high concentrations under varying high temperature intensities, durations, and leaf physiological states. The dose-response relationship generally shows a phenomenon of "promotion at low concentrations and inhibition at high concentrations."

[0004] Currently, the evaluation and screening of inducers largely rely on field spraying or whole-plant treatment. While these methods can reflect the actual production environment, the experimental results are easily affected by multiple factors such as light, rainfall, soil moisture, tree differences, and natural temperature fluctuations, leading to poor repeatability, long experimental cycles, and high costs. Even under controlled indoor conditions, most studies only compare a few discrete concentration points and select the "best-performing" concentration based on experience, making it difficult to quantitatively provide parameters such as the maximum effect enhancement or the half-maximum effective concentration (SMC). Key parameters include the confidence interval for the optimal concentration and the risk interval for overdose. Furthermore, some schemes, in order to improve comparability, strictly limit leaf age (e.g., using only leaves approximately 30 days old). This increases operational difficulty and limitations when dealing with a large number of breeding materials, as the leaf age is difficult to completely standardize.

[0005] Furthermore, in traditional technologies, the effects of different heat resistance inducers are usually compared at their respective single or empirical concentrations. It is difficult to conduct a unified and objective comparative evaluation under the optimal concentration of each inducer and its corresponding optimal induction effect, thus limiting the accurate judgment of the strength of different inducers and their application value.

[0006] Meanwhile, in grape breeding and germplasm resource evaluation, the identification of heat tolerance among different varieties, hybrid offspring, or lines often relies on field measurements and observations under whole-plant conditions, which faces problems such as significant environmental interference, insufficient repeatability, long cycles, and high costs. Although OJIP chlorophyll fluorescence can be used to reflect the effects of high temperatures on the photosystem, a rapid identification method that can be completed under controlled indoor conditions, with clear procedures, comparable results, and ease of large-scale implementation is still needed.

[0007] Therefore, existing technologies lack a method that can integrate the above requirements and achieve rapid, quantitative screening and dosage recommendation of grape heat resistance inducers in an indoor environment, adaptable to multiple temperature scenarios. This application aims to solve this problem. Summary of the Invention

[0008] The primary objective of this invention is to overcome the aforementioned deficiencies of the prior art and provide a method for rapidly screening and determining the recommended dosage of grape heat resistance inducers under controlled indoor conditions. This method can quantitatively analyze the dose-effect of inducers and perform cross-temperature comparisons, and can also be used for the heat resistance identification of grape varieties.

[0009] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0010] The present invention aims to provide a method for screening and determining the recommended dosage of grape heat resistance inducers, comprising the following steps:

[0011] S1. Leaf disc treatment and high temperature stress: Leaf discs were prepared from grape functional leaves. The leaf discs were pretreated by immersing them in solutions of candidate inducers of different concentrations and then subjected to water bath high temperature stress under multiple temperature gradients.

[0012] S2. OJIP Parameter Measurement and Comprehensive Index Calculation: After recovery from stress, the OJIP rapid chlorophyll fluorescence parameters of the leaf discs were measured, and a comprehensive heat tolerance index was constructed based on at least one positive parameter and at least one negative parameter. ;

[0013] S3. Calculation of relative improvement value: for each high-temperature stress temperature Based on the untreated control at temperature The comprehensive indicators at a suitable temperature of 25°C Calculate the concentration of each inducer for treatment. The relative heat resistance improvement value ;

[0014] (As mentioned above, grapevines exhibit high photosynthetic efficiency and excellent growth at around 25°C, this temperature is generally considered their physiological optimum.)

[0015] S4. Dose-effect model fitting: for each temperature With inducer concentration As the independent variable, with Using the high-dose inhibition term as the dependent variable, fit a unimodal dose-response model;

[0016] S5. Multi-temperature dose optimization: Based on the dose-response relationship obtained in step S4 at each temperature, determine a recommended application concentration that is stable at all of the multiple temperatures. .

[0017] In this invention, further, in step S2, the formula for calculating the comprehensive heat resistance index S is: Wherein, the positive parameter is PI abs The negative parameter is W k .

[0018] In this invention, further, in step S3, the relative heat resistance improvement value The calculation formula is: ,in, Treatment with inducing agent at temperature The following comprehensive indicators This is a comprehensive indicator for a control group at the same temperature without any added medication. This represents a comprehensive index for the control group at a suitable temperature of 25℃ without any added drugs. In this invention... Expressed as a percentage (×100%).

[0019] In this invention, further, in step S4, the expression of the dose-response relationship model is: ;

[0020] in, This represents the relative improvement in heat resistance predicted by the model. For stress temperature, The molar concentration (mM) of the inducer. This model consists of two parts: a promoting term and an inhibiting term. The Hill promoting term describes the process of enhancement where the effect tends to saturate as the concentration increases; This is the high-dose inhibition term, describing the inhibition process where the effect decays under high concentration conditions. The product of these two terms forms a single-peak curve.

[0021] The model parameters are defined and their meanings are as follows:

[0022] The theoretical maximum effect enhancement parameter (dimensionless) is used to characterize the theoretical upper limit of the model, reflecting the maximum induction intensity that the inducer can achieve under given temperature conditions.

[0023] The half-maximum effective concentration parameter (unit: mM) represents the concentration required for an inducer to achieve half of its maximum effect. It is used to characterize its dose sensitivity, and the smaller the value, the higher the dose sensitivity.

[0024] The dose-response slope parameter (dimensionless) reflects the slope of the curve and is used to characterize the steepness of the effect as the concentration changes.

[0025] High-dose inhibition parameters (unit: mM) -1 (), used to characterize the strength of the inhibitory effect produced by the inducer under high concentration conditions, The larger the value, the more pronounced the inhibition.

[0026] Furthermore, at temperature The maximum effect boost predicted by the model is defined as:

[0027]

[0028] Its corresponding optimal induction concentration is defined as:

[0029]

[0030] in This represents the range of possible inducer concentrations. It should be noted that... This represents the theoretical upper limit of the model parameters, while The two are related but not identical, representing the maximum achievable improvement level under the constraints of the model.

[0031] Furthermore, step S4a is included between steps S4 and S5: statistically estimating the parameters of the model and the optimal concentration predicted by the model using the Bootstrap resampling method, with the number of resampling times B being 500 to 2000.

[0032] In this invention, further, in step S5, the determination of the stable recommended application concentration... The methods include:

[0033] (1) Set the first threshold Determine the temperature The near-optimal concentration range below, the concentration within the range satisfy ;

[0034] (2) If there is a common intersection among the near-optimal concentration ranges at each temperature, then the midpoint or range of this intersection shall be taken as the average concentration. ;

[0035] (3) If there is no common intersection, the optimal concentration predicted by the model at each temperature shall be used. Statistical median As a candidate, and verify whether it satisfies the requirements at all temperatures. If satisfied, then determine. ,in The second threshold;

[0036] Wherein, the first threshold The second threshold is between 0.80 and 0.98. It ranges from 0.85 to 0.95.

[0037] Furthermore, this invention also includes step (4): determining the upper limit concentration of excess inhibition. Its satisfaction ,in The third threshold is between 0.85 and 0.95.

[0038] In this invention, further, in step S1, the plurality of different temperature gradients include at least three consecutive stress temperatures selected from 40°C, 41°C, 42°C, 43°C and 44°C.

[0039] In this invention, the candidate inducer is further defined as salicylic acid, abscisic acid, etc. Or at least one of spermidine. It should be noted that the candidate inducers are not limited to the types listed above. Any exogenous substance that can improve heat resistance by inducing the plant's own stress response can be screened and its dosage determined using the method provided in this invention.

[0040] Another objective of this invention is to provide a method for identifying the heat resistance of grape varieties or strains. Without adding an inducing agent, the method employs steps S1, S2, and S3 described above, comparing the comprehensive indicators of leaf discs from different grape varieties or strains after exposure to high-temperature stress. and based on To enable rapid identification and grading of heat resistance.

[0041] Another object of the present invention is to provide a method for improving the heat resistance of grapes, comprising: applying a recommended concentration determined by any of the methods described above. Heat tolerance inducers were applied to grapevines.

[0042] Another objective of this invention is to provide a quantitative comparison method for the induction intensity and dose sensitivity of different grape heat resistance inducers, comprising: analyzing two or more heat resistance inducers using the above method to obtain their corresponding model parameters or model prediction results, wherein the parameters include at least the parameter of theoretical maximum effect enhancement. Half-maximum effective concentration Dose response slope parameter High-dose inhibition parameters The model prediction results include at least the maximum effect boost value predicted by the model. Optimal induction concentration Or stable recommended application concentration Based on the model parameters and model prediction results, the heat resistance induction intensity, dose sensitivity and application stability of different inducers were quantitatively compared.

[0043] Compared with existing technologies, this invention achieves a systematic upgrade to the screening and dosage determination methods for heat resistance inducers: First, by introducing a pharmacological dose-response model including an inhibitory term and Bootstrap resampling statistical inference, the maximum effect enhancement and half-maximum effective concentration are obtained. Key pharmacodynamic parameters such as dose response slope, high-dose inhibition parameters, optimal concentration and its confidence interval were identified, enabling precise quantitative screening from discrete point empirical screening to continuous model-driven, parameter-verifiable screening. Furthermore, through multi-temperature scenario integration and robust decision-making strategies, the effective concentration range and its intersection or statistical representative value were comprehensively determined under different high-temperature intensities, overcoming the technical deficiency of poor adaptability of traditional recommended doses under fluctuating temperatures, thus obtaining a stable and effective recommended concentration over a wide temperature range.

[0044] In summary, due to the adoption of the above technical solutions, the present invention has at least the following beneficial effects:

[0045] 1. Highly efficient and controllable, free from environmental dependence: This invention employs detached leaf disc treatment combined with standardized water bath high-temperature stress, simplifying complex field trials into a standardized process that can be completed in the laboratory on the same day. This method effectively isolates the interference of uncontrollable field factors such as light, water, and soil differences, shortening the screening cycle from days or weeks to hours, and enabling high-throughput parallel testing (multiple temperatures × multiple concentrations), significantly reducing manpower, material resources, and time costs. Furthermore, it has broader requirements for leaf maturity, facilitating rapid evaluation of a large number of breeding materials.

[0046] 2. Quantitative Evaluation: This invention constructs a comprehensive mathematical index. Relative improvement value compared to normalization This study is the first to transform the heat resistance effect of inducers into continuous quantitative data that can be directly compared across temperatures. More importantly, by incorporating a dose-response model that includes a high-dose inhibition term for fitting, the theoretical maximum effect enhancement can be accurately calculated. Half-maximum effective concentration Dose response slope High-dose inhibition parameters Optimal induction concentration and recommended application concentration range Key efficacy parameters, etc. This completely changes the limitations of traditional methods that rely on only a few discrete data points for fuzzy comparisons of "good or bad effects," and realizes a functional and graphical precise description of the "concentration-effect" relationship, providing a solid quantitative foundation for scientific evaluation.

[0047] 3. Optimized Recommended Dosage and Stable Administration: This invention is the first to combine Bootstrap resampling statistical inference with multi-temperature stress scenario analysis for inducer dosage decision-making. This method not only provides a confidence interval for the optimal concentration at a single temperature, but also determines a recommended application concentration range that maintains a stable effect under fluctuating high-temperature environments by analyzing the common intersection of effective concentration ranges at multiple temperatures or statistically validated median concentrations. This addresses the shortcomings of existing technologies, such as providing a single recommended dosage, being unable to adapt to complex real-world climates, and lacking overdose risk warnings. It shifts dosage recommendations from "experience-based guesswork" to "data-driven, risk-controllable" scientific decision-making.

[0048] 4. Quantitative comparison of the strength and sensitivity of different inducers within a unified evaluation framework: The method of this invention can not only screen heat-resistant inducers and determine their optimal dosage, but also, under a unified evaluation system, quantitatively compare the maximum induction strength (e.g., ...) of different inducers. ), dose sensitivity (e.g.) ) and application stability / fault tolerance (e.g. Quantitative comparisons were conducted on the width of heat-resistant inducing agents to provide a basis for the optimal selection, combination design, and application of these agents in different production scenarios.

[0049] 5. High scalability and broad application prospects: The evaluation system of this method is based on the universal response mechanism of PSII, the core photosynthetic organelle, thus possessing extremely high universality. It is not only applicable to the screening of various chemical or biological inducers such as γ-aminobutyric acid (GABA), abscisic acid (Abscisic acid), spermidine, and salicylic acid, but its core process, with slight adjustments, can be directly used for the rapid identification and ranking of the inherent heat tolerance of different grape varieties, germplasm resources, and even hybrid progeny populations, providing an efficient pre-screening tool for heat-tolerant breeding. Furthermore, the principles and framework of this method also have significant reference and extension value for heat tolerance research on other horticultural or field crops.

[0050] In summary, this invention, through methodological innovation, solves the three core problems that have long existed in the field of grape heat resistance inducer screening: low efficiency, inconsistent evaluation criteria, and blind dosage. It forms a complete technical solution that is rapid, quantitative, stable, and versatile, significantly improving the ability to translate research results into reliable field applications. It has important practical value for grape stress-resistant cultivation and breeding practices. Attached Figure Description

[0051] Figure 1This figure shows the dose-response relationship and optimal concentration statistical distribution of γ-aminobutyric acid (GABA) under different high-temperature stresses. Figure 1 A shows the improvement in heat resistance at different temperatures. Model fitting curve as concentration changes; Figure 1 B shows the optimal concentrations at each temperature obtained based on Bootstrap resampling. The statistical distribution (box plot).

[0052] Figure 2 This diagram illustrates the effective concentration range and recommended stable concentration range of GABA under different high-temperature conditions.

[0053] Figure 3 This is a dose-response relationship and optimal concentration statistical distribution chart for abscisic acid (ABA) under different high-temperature stresses. Figure 3 A shows the improvement in heat resistance at different temperatures. Model fitting curve as concentration changes; Figure 3 B shows the optimal concentrations at each temperature obtained based on Bootstrap resampling. The statistical distribution (box plot).

[0054] Figure 4 This diagram illustrates the effective concentration range and recommended stable concentration range of ABA under different high-temperature conditions.

[0055] Figure 5 This diagram shows the dose-response relationship and optimal concentration statistical distribution of spermidine (SPD) under different high-temperature stresses. Figure 5 A shows the improvement in heat resistance at different temperatures. Model fitting curve as concentration changes; Figure 5 B shows the optimal concentrations at each temperature obtained based on Bootstrap resampling. The statistical distribution (box plot).

[0056] Figure 6 This diagram illustrates the effective concentration range and recommended stable concentration range of SPD under different high-temperature conditions.

[0057] Figure 7 This is a dose-response relationship and optimal concentration statistical distribution chart for salicylic acid (SA) under different high-temperature stresses. Figure 7 A shows the improvement in heat resistance at different temperatures. Model fitting curve as concentration changes; Figure 7 B shows the optimal concentrations at each temperature obtained based on Bootstrap resampling. The statistical distribution (box plot).

[0058] Figure 8 This diagram illustrates the effective concentration range and recommended stable concentration range of SA under different high temperature conditions.

[0059] Figure 9 This diagram illustrates the trade-off between dose sensitivity and application stability for different heat-resistant inducers, used to compare the differences in induction intensity, dose response characteristics, and stable recommended application concentrations among different inducers. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are specific implementations of a part of this invention, but not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0061] I. General Implementation Method

[0062] In a preferred embodiment of the present invention, the method for screening and determining the recommended dosage of the grape heat resistance inducer mainly includes the following steps:

[0063] 1. Experimental Materials and Processing

[0064] 1.1 Plant Material and Leaf Round Preparation: Healthy, disease-free grapevines were selected, and functional leaves of similar physiological maturity were collected from the middle of the canopy. Dust was gently wiped away from the leaf surface with a soft, damp cloth. Using a 1.0 cm diameter metal punch, avoiding the midrib, leaf rounds were cut from the middle region of the leaf. The leaf rounds were randomly grouped, with 10 or more biological replicates per group, and used immediately for treatment.

[0065] 1.2 Inducer Treatment and High-Temperature Stress: Leaf discs were completely immersed in sealed containers (e.g., covered aluminum boxes) containing treatment solutions of different concentrations of inducer. The treatment solutions were prepared with deionized water or mineral water, with 0.005% (v / v) Tween-20 added as a surfactant. The blank control (CK) consisted of a solvent containing only an equal amount of surfactant (e.g., 0.005% Tween-20). Immersion was carried out at room temperature (approximately 25°C) in the dark for 2 hours to ensure sufficient absorption of the inducer. After immersion, the entire container was placed in a precision constant-temperature water bath for high-temperature stress. The temperature gradient should cover the sublethal to lethal high-temperature range, for example: 25°C (suitable temperature control), 40°C, 41°C, 42°C, 43°C, 44°C. All high-temperature treatments were performed for a uniform duration of 20 minutes, ensuring the container remained submerged in the water bath during treatment to ensure uniform heating of the samples.

[0066] 1.3 Post-stress recovery: After the high-temperature treatment, the treatment solution was quickly discarded, and any remaining droplets on the leaf disc surface were blotted dry with clean filter paper. Subsequently, the leaf discs were laid flat on a tray lined with moist filter paper and placed in the dark at 25°C for 6 hours to allow the leaf moisture and physiological state to stabilize in preparation for fluorescence measurement.

[0067] 2. OJIP fluorescence parameter determination and heat resistance index calculation

[0068] 2.1 OJIP fluorescence curve determination

[0069] After the recovery period, the OJIP rapid fluorescence induction curve of the leaf discs was measured using a chlorophyll fluorometer. Before measurement, the samples needed to be dark-acclimatized in the leaf clip for at least 30 minutes. Under standardized saturated red light excitation, the dynamic change curve of fluorescence intensity was recorded within 2 seconds of the start of irradiation. Initial fluorescence was extracted and recorded using the instrument's built-in software or the standard JIP-test analysis method. Maximum fluorescence Photochemical performance index (PI) abs ),reflect K-point relative variable fluorescence (W) in the donor-side oxygen-releasing complex state k Key parameters such as )

[0070] 2.2 Comprehensive Heat Resistance Index calculate

[0071] To integrate multi-dimensional information and enhance sensitivity to thermal stress response, a comprehensive heat resistance index is constructed. The calculation formula is as follows:

[0072]

[0073] In this embodiment, For example, in the formula, PI abs As a positive parameter sensitive to stress, a decrease in its value indicates Overall functionality is impaired; W k As a negative parameter, an increase in its value directly indicates... Thermal damage on the donor side. Taking the logarithm can make the data distribution closer to a normal distribution and effectively integrate information from two inversely varying parameters.

[0074] 2.3 Relative heat resistance improvement value Calculation and Normalization

[0075] To ensure comparable performance under different intensities of high-temperature stress, a relative heat resistance enhancement value is introduced. Normalization is performed. First, the temperature for each stress is calculated. Below, CK The mean value, denoted as ; and CK at a suitable temperature of 25℃ The mean value, denoted as Then, for any inducer concentration C, treatment is performed according to its temperature... Below Value Mean Calculate using the following formula:

[0076] ;

[0077] The significance of this indicator is clear: This indicates that there is no improvement effect. This indicates that the heat resistance has fully recovered to the level of the optimal temperature control. This indicates an "overcompensation" effect that exceeds the optimal temperature control.

[0078] In this invention, It can be expressed as a percentage (i.e., calculated value × 100%); correspondingly, the results obtained from subsequent model fitting and prediction. and It maintains the same dimensions as it.

[0079] To quantitatively reveal the relationship between inducer concentration and the above-mentioned normalized effect value, a dose-response model fitting is required.

[0080] 3. Dose-response model fitting and analysis

[0081] 3.1 Model Selection and Parameter Significance

[0082] To quantitatively describe the effect of "low concentration promoting, high dose inhibiting," a single-peak dose-response model including a high-dose inhibition term was used to analyze the measured relative heat resistance improvement value. The fitting is performed, and its expression is:

[0083] ;

[0084] in, This represents the relative improvement in heat resistance predicted by the model. For stress temperature, The molar concentration (mM) of the inducer. This model consists of two parts: a promoting term and an inhibiting term. The Hill promoting term describes the process of enhancement where the effect tends to saturate as the concentration increases; This is the high-dose inhibition term, describing the inhibition process where the effect decays under high concentration conditions. The product of these two terms forms a single-peak curve.

[0085] The model parameters are defined and their meanings are as follows:

[0086] The theoretical maximum effect enhancement parameter (dimensionless) is used to characterize the theoretical upper limit of the model, reflecting the maximum induction intensity that the inducer can achieve under given temperature conditions.

[0087] The half-maximum effective concentration parameter (unit: mM) represents the concentration required for an inducer to achieve half of its maximum effect. It is used to characterize its dose sensitivity, and the smaller the value, the higher the dose sensitivity.

[0088] The dose-response slope parameter (dimensionless) reflects the slope of the curve and is used to characterize the steepness of the effect as the concentration changes.

[0089] High-dose inhibition parameters (unit: mM) -1 The value of α is used to characterize the strength of the inhibitory effect of the inducer under high concentration conditions; the larger the value of α, the more obvious the inhibition.

[0090] Furthermore, at temperature The maximum effect boost predicted by the model is defined as:

[0091]

[0092] Its corresponding optimal induction concentration is defined as:

[0093]

[0094] in This represents the range of possible inducer concentrations. It should be noted that... This represents the theoretical upper limit of the model parameters, while The two are related but not identical, representing the maximum achievable improvement level under the constraints of the model.

[0095] 3.2 Model Fit and Goodness-of-Fit Evaluation

[0096] For each specific stress temperature , in concentration As the independent variable, the experimentally measured The value is the dependent variable. The above model is fitted using the nonlinear least squares method to obtain the parameters at the corresponding temperature. Constraints can be imposed on the fitting process: The model's goodness can be assessed using the coefficient of determination R0. 2 Evaluation is conducted using methods such as residual analysis.

[0097] 3.3 Statistical Inference and Confidence Interval Estimation

[0098] To improve the reliability of parameter estimation, the Bootstrap resampling method is used for statistical inference. This is applied to the original dose-response dataset. Perform repeated sampling with replacement (e.g., B=1000 times), refit the model after each resampling, and calculate the optimal concentration. Based on the distribution results of a large number of Bootstrap samples, the parameters of the model can be further calculated. and optimal concentration The 95% confidence interval is used to assess the stability and accuracy of the estimate.

[0099] 4. Multi-temperature dose optimization and stable concentration recommendations

[0100] 4.1 Determination of near-optimal concentration range

[0101] Based on extensive preliminary experiments and statistical conventions, a high-effect threshold is set. (For example For each temperature , will satisfy The continuous concentration range is defined as the "near-optimal concentration range" at that temperature.

[0102] 4.2 Stable Recommended Concentration Decision-making process

[0103] Scenario 1 (Common Intersection Exists): If the "near-optimal concentration ranges" of all target temperatures (e.g., 40℃, 42℃, 44℃) have a common intersection on the horizontal axis (concentration axis). If so, the intersection is determined to be a stable and valid range.

[0104] Scenario 2 (No common intersection): If there is no common intersection, calculate the optimal concentration estimated by Bootstrap at each temperature. Statistical median Also based on experience, an acceptable validity threshold is set. .verify Does it satisfy the requirements at all temperatures? If satisfied, then determine. .

[0105] 4.3 Delineation of Excessive Inhibition Limits and Risk Ranges

[0106] To prevent the risk of overuse, an overuse threshold is defined. To the right of the peak of the model curve (the high concentration side), the solution satisfies... concentration As temperature The excessive inhibition threshold concentration below. Concentrations higher than this... The area is considered an "excess risk zone".

[0107] II. Specific Implementation Cases

[0108] Implementation Case 1:

[0109] The following uses inducers The implementation process of this invention will be specifically explained using the induction of heat resistance in 'Jingyan' grapes as an example.

[0110] 1. Experiment Execution and Data Acquisition

[0111] Following the aforementioned method, leaf discs were treated with a series of GABA solutions ranging from 0 to 10 mM, and subjected to high-temperature stress at 40℃, 41℃, 42℃, 43℃, and 44℃, respectively. OJIP curves were measured, and S-values ​​were calculated. Values. Taking the data at 42℃ as an example, some results are shown in Table 1 below.

[0112] Table 1. Some OJIP parameters and heat tolerance indices of grape leaf discs under GABA treatment at 42℃ stress.

[0113]

[0114] Note: The table contains comprehensive indicators. .

[0115] 2. Dose-response model fitting

[0116] To quantitatively reveal the dose-response relationship of GABA, different concentrations were used... and The data was fitted to a model, and the results are as follows: Figure 1 As shown. Figure 1 The scatter plots represent measured data, and the smooth curve represents the fitted model. The predicted curve clearly shows a typical single-peak shape, intuitively confirming the "low-promotion, high-inhibition" effect of GABA.

[0117] The specific fitting parameters at 40℃ are: parameter A = 81.4203, EC50 = 0.2886mM, parameter h = 1.1211, and suppression coefficient k = 0.0788mM. -1 Coefficient of determination R 2 = 0.9852, indicating an excellent model fit. The optimal predicted concentration at 40℃ was obtained through model solving. .

[0118] The specific fitting parameters at 41℃ are: parameter A = 116.0055, EC50 = 1.0755mM, parameter h = 0.9421, and suppression coefficient k = 0.0989mM.-1 Coefficient of determination R 2 = 0.9702, indicating an excellent model fit. The optimal predicted concentration at 41℃ was obtained through model solving. .

[0119] The specific fitting parameters at 42℃ are: parameter A = 105.9290, EC50 = 1.3051mM, parameter h = 0.6000, and suppression coefficient k = 0.1208mM. -1 Coefficient of determination R 2 = 0.9681, indicating an excellent model fit. The optimal predicted concentration at 42℃ was obtained through model solving. .

[0120] The specific fitting parameters at 43℃ are: parameter A = 45.8974, EC50 = 0.1677mM, parameter h = 1.2676, and suppression coefficient k = 0.0781mM. -1 Coefficient of determination R 2 = 0.9761, indicating an excellent model fit. The optimal predicted concentration at 43℃ was obtained through model solving. .

[0121] The specific fitting parameters at 44℃ are: parameter A = 47.6215, EC50 = 0.1288mM, parameter h = 0.6351, and suppression coefficient k = 0.1107mM. -1 Coefficient of determination R 2 =0.9897, indicating an excellent model fit. The optimal predicted concentration at 44℃ was obtained through model solving. .

[0122] The inhibition coefficient k obtained from the fitting was positive under all temperature conditions, which further confirmed from a quantitative perspective that GABA has an inhibitory effect on the heat resistance improvement in the higher concentration range, rather than a simple linear enhancement relationship.

[0123] To further evaluate the model's predicted optimal concentration To ensure statistical robustness, bootstrap resampling analysis was performed on dose-response data under various temperature conditions (B = 1000). The results are as follows: Figure 1 As shown in Figure B, under various temperature conditions The bootstrap distributions all exhibit a relatively concentrated unimodal distribution, with a limited 95% confidence interval, indicating that the optimal concentration predicted by the model has good repeatability and stability.

[0124] C at different temperatures *The median values ​​showed a certain systematic difference with temperature: the predicted optimal concentration was generally higher at 41-42℃, while it decreased significantly at 43-44℃. This result is consistent with... Figure 1 The consistent trend in the peak position of the dose-response curve in A indicates that the intensity of high temperature affects the optimal dose level for GABA-induced heat resistance.

[0125] The above results indicate that the optimal concentration determined at a single temperature is difficult to directly apply to different high-temperature conditions. Therefore, it is necessary to conduct comprehensive analysis under multiple temperature conditions to further screen for concentration ranges that have a stable induction effect over a wide temperature range.

[0126] 3. Multi-temperature optimization and determination of stable concentration

[0127] Threshold coefficients were set under different high-temperature conditions (40℃, 41℃, 42℃, 43℃, and 44℃). Calculate the corresponding GABA treatments respectively The "near-optimal concentration range" at each temperature was obtained.

[0128] like Figure 2 As shown, the horizontal axis represents concentration, and the vertical axis represents temperature. The shaded areas at different temperatures represent their respective near-optimal concentration ranges. The horizontal color bands indicate the concentrations that meet the requirements at each temperature. The effective concentration range is shown; the vertical semi-transparent shaded area represents the intersection of the effective concentration ranges within the high temperature range of 40-44℃. It can be seen that the shaded areas of the three temperature ranges share a common intersection on the horizontal axis (approximately 1.00mM to 3.00mM). Based on the decision rule of Scenario 1, the stable recommended concentration range is: .this The value is already there Figure 2 The process of finding and determining a stable concentration from multiple effective temperature ranges is clearly indicated and visually demonstrated.

[0129] 4. Application Validation

[0130] The recommended concentration (GABA, 2 mM) determined in this invention was applied to potted 'Sunshine Rose' grape seedlings. Foliar spraying was performed 24 hours before the onset of simulated extreme high-temperature weather (45°C for 4 hours, then 30°C for the remaining time). Spraying with water served as a control. Key indicators were analyzed after two days of continuous high-temperature treatment. (PSII maximum photochemical efficiency) and PI abs (Photosynthetic performance index). Treatment group The average value is 0.6979, PI abs The average value was 0.3385, which was significantly higher than that of the control group. The average value is 0.6273, PI absThe average value was 0.2399, demonstrating that the recommended dosage effectively alleviates photosynthetic damage caused by high temperatures. This verifies the practicality and reliability of the recommended dosage regimen.

[0131] Implementation Case 2:

[0132] To further verify the broad applicability of the method of the present invention to different types of inducers, this embodiment also uses abscisic acid (ABA), spermidine (SPD) and salicylic acid (SA) as examples, and the analysis is carried out strictly according to the same procedure.

[0133] 1. Method

[0134] Following the same method described in the first general embodiment of this invention, abscisic acid (ABA), spermidine (SPD), and salicylic acid (SA) were subjected to leaf disc treatment, high-temperature stress, OJIP measurement, and subsequent data analysis.

[0135] 2. Results and Discussion

[0136] 2.1 Dose-response model fitting

[0137] Model fitting was performed on the experimental data of ABA, SPD, and SA, and the results are as follows: Figure 3 A, Figure 5 A and Figure 7 As shown in Figure A, the dose-response curves of the three inducers all exhibit a typical single-peak shape, and the coefficient of determination R0 is... 2 All values ​​are above 0.95, indicating that the single-peak model with high-dose inhibition term used in this invention can well describe its "low-promotion, high-inhibition" effect.

[0138] 2.2 Stability analysis of the optimal concentration

[0139] The optimal concentrations C of ABA, SPD, and SA were statistically estimated using the Bootstrap method (B=1000), and their distributions are as follows: Figure 3 B. Figure 5 B and Figure 7 As shown in B. The three inducers... The distributions are relatively concentrated, and the 95% confidence intervals are narrow, proving that the optimal concentration predicted by the model has high statistical robustness and reliability.

[0140] 2.3 Determination of stable concentration range at multiple temperatures

[0141] Set threshold =0.8, and plotted the effective concentration ranges of ABA, SPD, and SA in the temperature range of 40-44℃ (corresponding to 0.8 respectively). Figure 4 , Figure 6 and Figure 8Analysis showed that ABA, SPD, and SA all had a clear common effective concentration overlap in the 40-44℃ range (shown as the vertical gray semi-transparent band in the figure).

[0142] Based on this, stable recommended concentration ranges for ABA, SPD, and SA were successfully determined. This result is consistent with the case of GABA, jointly demonstrating that the "multi-temperature dosing optimization" strategy provided by this invention can effectively determine a stable and effective application concentration window over a wide temperature range for different types of inducers.

[0143] 3. Summary of Key Parameters

[0144] Table 2 below summarizes the key quantitative conclusions obtained from the analysis of the three inducers ABA, SPD and SA using the method of this invention.

[0145] Table 2. Recommended stable concentration ranges for ABA, SPD, and SA (40-44℃). =0.80)

[0146]

[0147] For abscisic acid (ABA): its effective concentration range is concentrated vertically (across temperature) (reference). Figure 4 This indicates that within the testing range of 40-44℃, its induction effect is stable and minimally affected by temperature fluctuations, and the recommended temperature range is [insert temperature range here]. s The value has a high degree of accuracy.

[0148] For spermidine (SPD): its effective range may narrow at high temperatures (see reference). Figure 6 The method objectively reflects the characteristic that its induction effect is relatively sensitive to high temperatures. The determined C s The range can guide its use in practical applications to focus on using it within a suitable temperature range in order to ensure the stability of the effect.

[0149] For salicylic acid (SA): its effective concentration range is highly concentrated and stable in the range of 0.03-0.05 mM (reference). Figure 8 This indicates that SA has a consistent induction effect within the test temperature range, a clear recommended dose window, and a low risk of overdose.

[0150] The results of this implementation demonstrate that the screening and dosage determination method provided by this invention is not only applicable to γ-aminobutyric acid (GABA), but can also be successfully applied to inducers with different chemical properties, such as abscisic acid (ABA), spermidine (SPD), and salicylic acid (SA). This empirically verifies the universality of the method's process, the reproducibility of the model, and the reliability of the decision-making strategy, providing a universal quantitative technical solution for the scientific application of various exogenous inducers.

[0151] Implementation Case 3: Comparison of the heat resistance induction effect and dosage sensitivity of different heat resistance inducers

[0152] 1. Method

[0153] Following the same method described in the first general embodiment of this invention, leaf disc treatment, high temperature stress, OJIP measurement, and subsequent data analysis were performed on GABA, ABA, SA, and SPD.

[0154] 2. Results and Discussion

[0155] 2.1 Comparison of the heat-inducing effect and dose sensitivity of different inducers

[0156] Based on the comprehensive indicators of this invention The results of the relative enhancement value R(T) calculation (Table 3) were used to compare the heat resistance induction effect and dose sensitivity of four inducers—GABA, ABA, SA, and SPD—under conditions of 40-44℃. The results showed that there were significant differences in heat resistance induction intensity and dose sensitivity among the different inducers.

[0157] Table 3 Comparison of thermal induction strength and sensitivity of different inducing agents ( =0.80, 40-44℃)

[0158]

[0159] As can be seen from the results in Table 3:

[0160] (1) Induction effect strength: within the range of 40-44℃ The mean order is GABA > ABA > SA > SPD.

[0161] (2) Dose sensitivity: SA and ABA It mainly focuses on 0.03-0.10 mM (low dose onset, high sensitivity, see...). Figure 9 ), and GABA's The concentration is concentrated at 1-2 mM, while SPD is concentrated at 0.5-1 mM. Sensitivity: SA ≈ ABA > SPD > GABA.

[0162] (3) In terms of application stability: based on the intersection of multiple near-optimal temperature intervals The results show that GABA's Widest (1-3 mM), higher fault tolerance; ABA's The narrowest range (0.025-0.10 mM) requires higher precision in application concentration. Application stability: GABA > SPD > SA > ABA.

[0163] In traditional techniques, commonly used single indicators are typically selected, such as... Constructing traditional relative boost values ​​under the same data and the same α rule And based on this, it is also determined Then calculate the intersection of 40-44℃. The results show that at the discrete concentration points set in this experiment, the traditional... The fact that the "near-optimal sets" of concentrations at various temperatures cannot form a common intersection makes it difficult to directly provide stable recommended concentrations across temperatures. And the present invention can be clearly obtained. .

[0164] IV. Implementation Methods for Identifying Variety Heat Resistance

[0165] This method can be used directly for rapid comparison of heat resistance of grape germplasm resources without the use of any exogenous inducers.

[0166] (1) Material preparation: Collect functional leaves from multiple grape varieties or strains to be evaluated and prepare leaf discs according to the same standard.

[0167] (2) Standardized stress: All varieties of leaf discs were subjected to uniform high temperature stress and recovery treatment at the same set temperature (e.g., 42℃ or 44℃).

[0168] (3) Index comparison: The comprehensive heat resistance index of leaf discs of each variety was measured and calculated. . The higher the value, the stronger the inherent heat resistance of the variety under the stress temperature.

[0169] (4) Graded evaluation: can be based on The distribution of values ​​is sorted and graded, or the value of each variety relative to a common sensitive reference variety is calculated. This allows for rapid and standardized initial screening of large quantities of breeding materials.

[0170] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for screening and determining the recommended dosage of a grape heat resistance inducer, characterized in that, Includes the following steps: S1. Leaf disc treatment and high temperature stress: Leaf discs were prepared from functional grape leaves and then immersed in different concentrations of water. After pretreatment in the candidate inducer solution, it was subjected to high-temperature water bath stress under multiple different temperature gradients; S2. OJIP Parameter Measurement and Comprehensive Index Calculation: After recovery from stress, the OJIP rapid chlorophyll fluorescence parameters of the leaf discs were measured, and a comprehensive heat tolerance index was constructed based on at least one positive parameter and at least one negative parameter. ; S3. Calculation of relative improvement value: for each high-temperature stress temperature Based on the untreated control at temperature The comprehensive indicators at a suitable temperature of 25°C Calculate the concentration of each inducer. The improvement in relative heat resistance under treatment ; S4. Dose-effect model fitting: for each temperature With inducer concentration As the independent variable, with Using the high-dose inhibition term as the dependent variable, fit a unimodal dose-response model. S5. Multi-temperature dose optimization: Based on the dose-response relationship obtained in step S4 at each temperature, determine a recommended application concentration that is stable at all of the multiple temperatures. .

2. The method according to claim 1, characterized in that, In step S2, the formula for calculating the comprehensive heat resistance index S is: Wherein, the positive parameter is PI abs , , , , ET o / CS m TR o / CS m One or more combinations thereof; the negative parameter is W k DI o / CS m One or more combinations thereof.

3. The method according to claim 1, characterized in that, In step S3, the relative heat resistance improvement value The calculation formula is: ,in, The concentration of the inducer is The processing at temperature The following comprehensive indicators This is a comprehensive indicator for a control group at the same temperature without the addition of an inducing agent. This is a comprehensive indicator for the control group at a suitable temperature of 25℃ without the addition of an inducing agent.

4. The method according to claim 1, characterized in that, In step S4, the expression for the dose-response relationship model is: ;in, This represents the relative improvement in heat resistance predicted by the model. This is the parameter representing the theoretical maximum effect enhancement. This is the half-maximum effective concentration parameter. For dose slope parameters, The parameter represents the high-dose inhibition; based on the model, the maximum effect enhancement predicted by the model at temperature φ is defined as: The corresponding optimal induction concentration is defined as: ,in Candidate inducer concentration The range of values; and between steps S4 and S5, step S4a is also included: using the Bootstrap resampling method to statistically estimate the parameters of the model and the optimal concentration predicted by the model, with the number of resampling times B being 500 to 2000 times.

5. The method according to claim 4, characterized in that, In step S5, the determination of the stable recommended application concentration The methods include: (1) Set the first threshold Determine the temperature The near-optimal concentration range below, the concentration within the range satisfy ,in , where is the maximum improvement predicted by the model at this temperature. This represents the range of values ​​for the candidate inducer concentration; (2) If there is a common intersection among the near-optimal concentration ranges at each temperature, then the midpoint or range of this intersection shall be taken as the average concentration. ; (3) If there is no common intersection, the optimal concentration predicted by the model at each temperature shall be used. Statistical median As a candidate, and verify whether it satisfies the requirements at all temperatures. If satisfied, then determine. ,in The second threshold; Wherein, the first threshold The second threshold is between 0.80 and 0.

98. It ranges from 0.85 to 0.

95.

6. The method according to claim 5, characterized in that, It also includes step (4): determining the excess inhibition limit concentration. Its satisfaction ,in The third threshold is between 0.85 and 0.

95.

7. The method according to claim 1, characterized in that, In step S1, the multiple different temperature gradients include at least three consecutive stress temperatures selected from 40°C, 41°C, 42°C, 43°C, and 44°C; the candidate inducer is at least one selected from salicylic acid, abscisic acid, γ-aminobutyric acid, or spermidine.

8. A method for identifying the heat resistance of grape germplasm resources, characterized in that, Without adding an inducing agent, using steps S1, S2, and S3 of the method described in any one of claims 1-7, the comprehensive indicators of leaf discs from different grape germplasm resources after the high-temperature stress were compared. and based on To enable rapid identification and grading of heat resistance.

9. A method for improving the heat resistance of grapes, characterized in that, include: Recommended application concentration determined by the method of any one of claims 1-7 Heat tolerance inducers were applied to grapevines.

10. A quantitative comparison method for the induction intensity and dose sensitivity of different grape heat resistance inducers, characterized in that, include: Using the method described in any one of claims 1-7, analyze two or more heat resistance inducers to obtain their corresponding model parameters or model prediction results, wherein the parameters include at least the parameter of theoretical maximum effect improvement. Half-maximum effective concentration Dose response slope parameter High-dose inhibition parameters The model prediction results include at least the maximum effect boost value predicted by the model. Optimal induction concentration Or stable recommended application concentration Based on the model parameters and model prediction results, the heat resistance induction intensity, dose sensitivity and application stability of different inducers were quantitatively compared.

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