A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Round 1

Reviewer 1 Report

The manuscript describes the predictive study for exploring the changes of EGCG content in tea under abiotic stress conditions, though collecting tea and corresponding soil and altitude data and using the measured data for single factor analysis. Meanwhile, the modeling factors were selected by LASSO regression to construct the prediction model, and the AIC standard was introduced to compare the goodness of fit.

 

The scope is adequate and the methods are novel and appropriate.

 

However, reproducibility is low because no experimental details were given in 2.2, and the modelling details are only mentioned and given in Results section.

 

Result presentation needs to be significantly improved as tight now all figures have low readability.

 

Hence a major revision is recommended.

 

Details for consideration:

 

Introduction: the authors failed to introduce the most important property of Pu'er tea, high radical scavenging potency as explored in doi:10.1039/C9OB02007A

 

Fig. 4 is not necessary.

 

Discussion should be carried out in the context of the literature. Table 2, how do you define correct and wrong? What do lower bound and upper bound mean?

 

There is no proper conclusion.

fine

Author Response

Thanks very much for your time to review this manuscript. I really appreciate your comments and suggestions. We have considered these comments carefully and try our best to address every one of them.

1. reproducibility is low because no experimental details were given in 2.2, and the modelling details are only mentioned and given in Results section.

Modification instructions: Thank you for your advice. The errors in the original manuscript have been corrected. We added experimental details in line 100, specifying that the physicochemical composition detection experiments were conducted five times, and the average of three data was taken. In line 157, included modeling details, stating that based on the calculation results, when the area is greater than 0.75, we can consider the model's predictive capability as good; when the area falls between 0.5 and 0.75, the model's predictive capability is still acceptable.

2. Result presentation needs to be significantly improved as tight now all figures have low readability.

Modification instructions: Thank you for your suggestions. The errors in the original manuscript have been corrected. We have enhanced the clarity of the images, allowing for a more accurate observation and interpretation of details within the images, thus improving the readability of the data.

3. Introduction: the authors failed to introduce the most important property of Pu'er tea, high radical scavenging potency as explored in doi:10.1039/C9OB02007A

Modification instructions: Thank you for your suggestions. We have added citations to relevant literature in the introduction at line 36 and in the ninth reference in the bibliography and marked them in red font.

4. Fig. 4 is not necessary.

Modification instructions: According to the actual application, the efficiency of predicting EGCG content by nomogram model alone is low. Therefore, this research has developed a visualization system specifically for this problem, and has obtained relevant software copyright. The system can achieve faster and more convenient EGCG content prediction. In order to highlight the practicality of the research, we use Figure 4 to show the system, and Figure 4 also shows the EGCG content prediction curve we developed. Compared with the nomogram method used in traditional medicine, the prediction curve we developed can display the EGCG content prediction score more accurately, and can better achieve accurate screening of planting sites. Thank you very much for your opinion. If you still don 't think Figure 4 is necessary, we can consider simplifying or deleting it.

5. Discussion should be carried out in the context of the literature. Table 2, how do you define correct and wrong? What do lower bound and upper bound mean?

Modification instructions: Thank you for your suggestions. The errors in the original manuscript have been corrected. We have included references in lines 363, 383, and in the reference section to provide support and credibility for our work. We define the correctness of external validation predictions as the comparison between the proportion of EGCG content predicted by the prediction system's final score and the actual value in the dry matter. If they match, it is considered correct. The "Lower bound" represents the minimum possible value of the predicted result affecting EGCG content variables, while the "Upper bound" represents the maximum possible value of the predicted result for EGCG content variables. As for ' Lower bound ' and ' Upper bound ' are only part of the data output during the model training process, we have deleted them in order to facilitate readers to more clearly compare the predicted scores with the real results.

6. There is no proper conclusion.

Modification instructions: Thank you for your suggestions. In line 360, we have further refined the LASSO model selection process to provide a basis for constructing the column chart model, thereby enhancing the model's predictive performance and interpretability.

Author Response File: Author Response.docx

Reviewer 2 Report

Dear Authors,

The manuscript entitled "A Predictive Study on the Content of EGCG in Yunnan Large Leaf Tea Trees Based on the Nomogram Model," effectively conveys the key elements of the research Identifying and discussing the limitations and gaps in a research study is a crucial aspect of scholarly work. It demonstrates a clear understanding of the scope and boundaries of the research while also highlighting areas for potential future research. Here are some limitations and gaps you might consider for your study:

One of the key limitations may have been the availability of data. Were there any data gaps or challenges in obtaining specific types of data (e.g., historical climate data, specific soil properties) that may have limited the scope of the study?

Consider discussing the size of your dataset. Was the sample size sufficient to draw robust conclusions, or were there constraints on data collection that limited the number of observations?

Assess the generalizability of your findings. Were the tea samples and environmental conditions in Yunnan representative of broader tea-producing regions? Were there specific conditions unique to Yunnan that might limit the applicability of your results elsewhere?

Discuss any assumptions made in your predictive models. Were there any simplifications or assumptions that might have influenced the accuracy of your predictions?

While LASSO regression was used for variable selection, were there any variables that were excluded but might have had an impact on EGCG content? Discuss potential omitted variables and their implications.

Describe the limitations of model validation. Were there any challenges or potential sources of bias in the validation process?

It's important to acknowledge that environmental factors are complex and interconnected. Discuss the limitations of isolating specific factors (e.g., altitude, soil properties) when natural systems often involve multiple interacting variables.

Consider the long-term effects of abiotic stress on tea quality. Investigating the impact of stressors over multiple growing seasons or years could provide a more comprehensive understanding.

Given the increasing concern over climate change, future research could explore how anticipated climate shifts might affect tea quality and EGCG content.

Investigate the role of microbial communities in tea leaves and soil. Microbes can significantly influence tea quality, and understanding their dynamics could be a valuable area of research.

Explore the genetic factors that contribute to variations in EGCG content. Genetic studies on tea plant varieties may shed light on specific genetic markers associated with higher EGCG production.

Research on precision agriculture techniques tailored to tea cultivation in abiotic stress conditions could help tea growers optimize conditions for EGCG content.

Investigate consumer preferences and perceptions related to tea quality, including EGCG content. Understanding market demands can guide producers in meeting consumer expectations.

 

Explore sustainable agricultural practices that can mitigate the impact of abiotic stressors on tea plants and improve EGCG content without compromising environmental sustainability.

Requried english grammar has to be refined  

Author Response

Thanks very much for your time to review this manuscript. I really appreciate your comments and suggestions. We have considered these comments carefully and try our best to address every one of them.

1. One of the key limitations may have been the availability of data. Were there any data gaps or challenges in obtaining specific types of data (e.g., historical climate data, specific soil properties) that may have limited the scope of the study?

Modification instructions: Thank you for your question. This study mainly focuses on the Hekai base in Xishuangbanna, which is located in a high altitude area with complex terrain. Relevant studies have also shown that soil nutrients, rhizosphere microorganisms, and physical and chemical components of tea in different ancient tea gardens have great differences. In view of these problems, it is extremely difficult to establish a model that is generally applicable to all tea mountains in Yunnan. Therefore, this study is more inclined to establish a prediction model based on local conditions. Although the model has high prediction accuracy only for Hekai, Banzhang and other regions, for other regions, only the training set changes can also achieve more accurate content prediction.

2. Consider discussing the size of your dataset. Was the sample size sufficient to draw robust conclusions, or were there constraints on data collection that limited the number of observations?

Modification instructions: Thank you for your question. The tea and soil data of this experiment were collected from Yuecheng base in Xishuangbanna, Yunnan Province. The collection points were set as 21 small areas. In each small area, four groups of samples were collected at different times and different years ( a total of 84 groups of samples, as shown in Fig.1 ). In order to solve the problem of small modeling data set, this study uses Bootstrap resampling 3000 times to expand the data set. It is based on the same data or sample, independent self-reliance, random re-sampling repeatedly, a large number of expanded samples. In addition, this study further introduces Bootstrap to expand the data set when evaluating the accuracy and stability of the model.

3. Assess the generalizability of your findings. Were the tea samples and environmental conditions in Yunnan representative of broader tea-producing regions? Were there specific conditions unique to Yunnan that might limit the applicability of your results elsewhere?

Modification instructions: Thank you for your question. Climate change is closely related to agriculture. Although the climate of Xishuangbanna belongs to tropical and subtropical monsoon climate, the altitudes are different, and Yunnan has the climate characteristics of ' one mountain is divided into four seasons, ten miles are different days ', and the terrain is complex. Therefore, this study is more inclined to ' adjust measures to local conditions ' to establish prediction models. In this study, we trained with the data of No.1 site of Yuecheng Company in Xishuangbanna, and verified with the data of No.2 base. The accuracy can still reach more than 0.7, only slightly lower than the prediction accuracy of No.1 base. In the future, we will continue to expand the research, and carry out more detailed research on different varieties of tea trees and different tea producing areas.

4. Discuss any assumptions made in your predictive models. Were there any simplifications or assumptions that might have influenced the accuracy of your predictions?

Modification instructions: Thank you for your question. The LASSO method is based on the penalty method to select the variables of the sample data. By compressing the coefficients of the original ordinary linear regression model, the original small coefficients are directly compressed to 0, so that the variables corresponding to this part of the coefficients are regarded as non-significant variables, and the non-significant variables are directly discarded to simplify the model. After our dozens of modeling experiments and previous studies, the prediction accuracy of the filtered model is indeed lower than that of all strong correlation factors. However, too many impact factor modeling will not only affect the time required for model prediction, but also easily lead to over-fitting of the model, and LASSO can solve this problem to a large extent. Although excluding some factors that are not very relevant will indeed reduce the accuracy of the model to a small extent, it can also greatly reduce the cost of soil detection when the model is applied, which is undoubtedly very beneficial to the application of the model.

5. While LASSO regression was used for variable selection, were there any variables that were excluded but might have had an impact on EGCG content? Discuss potential omitted variables and their implications.

Modification instructions: Thank you for your question. The LASSO method is based on the penalty method to select the variables of the sample data. By compressing the coefficients of the original ordinary linear regression model, the original small coefficients are directly compressed to 0, so that the variables corresponding to this part of the coefficients are regarded as non-significant variables, and the non-significant variables are directly discarded to simplify the model. After our dozens of modeling experiments and previous studies, the prediction accuracy of the filtered model is indeed lower than that of all strong correlation factors. However, too many impact factor modeling will not only affect the time required for model prediction, but also easily lead to over-fitting of the model, and LASSO can solve this problem to a large extent. Although excluding some factors that are not very relevant will indeed reduce the accuracy of the model to a small extent, it can also greatly reduce the cost of soil detection when the model is applied, which is undoubtedly very beneficial to the application of the model.

6. Describe the limitations of model validation. Were there any challenges or potential sources of bias in the validation process?

Modification instructions: Thank you for your question. In the study, we found that different heights of tea trees, the height of tea trees themselves, and the changes in atmospheric environment all had a certain impact on the content of EGCG in tea. However, because our prediction model was more about soil evaluation and planting site selection before tea planting, it was not included in our modeling factor screening. At present, our research group is also carrying out research on tea garden soil, atmospheric environment and tea growth rate and quality. In the follow-up study, we will fully integrate these data into our research.

7. It's important to acknowledge that environmental factors are complex and interconnected. Discuss the limitations of isolating specific factors (e.g., altitude, soil properties) when natural systems often involve multiple interacting variables.

Modification instructions: Thank you for your suggestion. We are very sorry that our introduction is not clear enough to give you a misunderstanding. The LASSO regression is based on the penalty method to select the variables of the sample data, not only based on the influence of the single factor on the EGCG content to select the modeling factors. Through our single-factor and multi-factor analysis tables, it is not difficult to see that LASSO is actually considered from the perspective of overall modeling rather than based on the correlation between individual factors and EGCG content.

8. Consider the long-term effects of abiotic stress on tea quality. Investigating the impact of stressors over multiple growing seasons or years could provide a more comprehensive understanding.

Modification instructions: Thank you for your suggestion. At present, the tea in Yunnan is mainly spring tea, and its quality and physical and chemical components are better than those in other seasons. Therefore, we mainly choose spring tea as the experimental material. In the follow-up experiment, we will take into account the seasonal factors, and add the corresponding summer tea and autumn tea prediction in our prediction system, so that our prediction model is more systematic and convincing. We are very sorry that we did not explain in the text that our tea is collected in multiple years, this is our negligence, very sorry.

9. Given the increasing concern over climate change, future research could explore how anticipated climate shifts might affect tea quality and EGCG content.

Modification instructions: Thank you for your suggestion. It is well known that changes in climate resources such as light, temperature, and precipitation have a great impact on tea planting layout, growth and development, yield, and quality. We have established a 5G base station in the base ( see below ) to collect data such as wind direction, wind speed, atmospheric pressure, illuminance, carbon dioxide concentration, air concentration, and rainfall in the tea mountain. Later, we will deepen our research on this aspect. If we have the opportunity, we hope to have the opportunity to carry out cooperative research with you. We will be grateful for this.

10. Investigate the role of microbial communities in tea leaves and soil. Microbes can significantly influence tea quality, and understanding their dynamics could be a valuable area of research.

Modification instructions: Thank you for your suggestion. Soil microorganism is an important component and the most active biological factor of tea garden soil ecosystem, which is directly involved in soil ecological processes such as litter decomposition, nutrient cycling and absorption. It has an important impact on the healthy growth of tea trees, tea quality, and the maintenance of soil ecosystem function stability. In the later period, we will increase the detection of metabolic components in tea garden soil, and make the prediction system more precise.

11. Explore the genetic factors that contribute to variations in EGCG content. Genetic studies on tea plant varieties may shed light on specific genetic markers associated with higher EGCG production.

Modification instructions: Thank you for your suggestion. Tea germplasm resources are the original materials for the breeding of new tea varieties. In the study of tea tree diversity, biochemical markers can be used for genetic diversity analysis, resistance identification and variety identification of tea germplasm resources. The genetic diversity of tea germplasm resources in Yunnan is rich. Epigallocatechin gallate, an ester-type catechin, is the main component of catechin compounds in tea. In addition, the use of our prediction system reduces the cost of the whole test to a certain extent, and can achieve good results. In the later stage, we will expand the tea production area and screen out the excellent varieties with high EGCG content.

12. Research on precision agriculture techniques tailored to tea cultivation in abiotic stress conditions could help tea growers optimize conditions for EGCG content.

Modification instructions: Thank you for your suggestion. The research of precision agriculture technology can help tea growers adjust and control these abiotic stress conditions according to different tea varieties and growth environments, so as to optimize the EGCG content in tea. It can also predict the physical and chemical components of tea produced in the future according to the soil conditions in advance, and help tea farmers and enterprises to choose the planting land. Next, we will use the sensor network to monitor the growth environment of tea trees and related environmental factors in real time, including temperature, humidity, light, etc., to deepen the research on this aspect.

13. Investigate consumer preferences and perceptions related to tea quality, including EGCG content. Understanding market demands can guide producers in meeting consumer expectations.

Modification instructions: Thank you for your suggestion. We will increase the investigation of tea consumers in the follow-up. Since the taste of tea soup is the result of the combined action of multiple physical and chemical components, catechin is the highest content component and the main flavor substance in tea. Non-ester catechin is the main factor leading to the bitterness of tea soup, while ester catechin is the main astringent component. In the future, we will increase the investigation of tea consumers and develop a system that can help them choose according to the needs of tea consumers as much as possible.

14. Explore sustainable agricultural practices that can mitigate the impact of abiotic stressors on tea plants and improve EGCG content without compromising environmental sustainability.

Modification instructions: Thank you for your suggestion. In the future, we will try to control the physical and chemical components of tea by changing the abiotic environment, so as to improve the quality of tea and promote the healthy growth of tea. We hope to have the opportunity to consult you in depth about relevant methods, which we will be grateful for.

Author Response File: Author Response.docx

Reviewer 3 Report

In the work entitled “A Predictive Study on the Content of EGCG in Yunnan Large Leaf Tea Trees Based on the Nomogram Model”, the authors explored the changes of EGCG content in tea under abiotic stress conditions based on the Nomogram Model. The manuscript is well organized with necessary data and within the scope of this Journal. The introduction and background are reasonable given the promise of the paper. Figures and tables are comprehensive and helpful. In general, the manuscript needs corrections to be published and need to be addressed before acceptance. My comments regarding this article are as follows,

 

1.       The manuscript has grammatical errors and needs improvement.

2.       Correct title! I think it should be Predictive Study not Pedictive Study

3.       The abstract should be revised to address the development and novelty of this work, especially the superiority or enhancement when compared with other advances.

4.       EGCG is stand for Epigallocatechin gallate, it is better to put full name (Epigallocatechin gallate) instead of abbreviation (EGCG) in the title and abstract at the first mention.

5.       I strongly recommend that authors present the data for the content of water extract, the total amount of tea polyphenols, the total amount of free amino acids and the content of catechin components and caffeine which was determined by HPLC method.

6.       All the figures need to be revised in consistent layout/marks to improve the readability. High resolution figures and revisions are also required to clearly show the details, especially Figures 2-4.

7.       In the lines 101 - 107, the authors investigated the phytochemical composition of Mengku large-leaf tea? Add the appropriate reference?

Minor editing of English language required

Author Response

Thanks very much for your time to review this manuscript. I really appreciate you’re your comments and suggestions. We have considered these comments carefully and try our best to address every one of them.

1. The manuscript has grammatical errors and needs improvement.

Modification instructions: Thank you for your reminder that the errors in the manuscript have been corrected and marked in red font. For example, the lines 19-20 have been changed to: “We collected tea samples along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis.” The lines 24-25 have been changed to: “The average area under the curve of the training set and the validation set  was 0.81, while the average area under the curve of the validation set was 0.76. Moreover, the calibration curve also indicated good consistency.” The lines 34 have been changed to: “Pu 'er tea attracts more and more attention.” The lines 128-130 have been changed to: “the determination of effective potassium was done by the combined leaching-colorimetric method, the determination of total nitrogen was done by the Kjeldahl method.”

2. Correct title! I think it should be Predictive Study not Pedictive Study

Modification instructions: Thank you for your reminder. We have already changed “Pedictive” to "Predictive".

3. The abstract should be revised to address the development and novelty of this work, especially the superiority or enhancement when compared with other advances.

Modification instructions: Thank you for your suggestion, we have added a statement about the technological advancements of this work in the abstract. The modified content is in the lines 20-22.

4. EGCG is stand for Epigallocatechin gallate, it is better to put full name (Epigallocatechin gallate) instead of abbreviation (EGCG) in the title and abstract at the first mention.

Modification instructions: Thank you for your suggestion. We have already changed “EGCG” to “Epigallocatechin gallate” in the title and abstract, and marked it in red font

5. I strongly recommend that authors present the data for the content of water extract, the total amount of tea polyphenols, the total amount of free amino acids and the content of catechin components and caffeine which was determined by HPLC method.

Modification instructions: Thank you for your suggestion. For your convenience, we send the data to the background in the form of a supplementary table.

6. All the figures need to be revised in consistent layout/marks to improve the readability. High resolution figures and revisions are also required to clearly show the details, especially Figures 2-4.

Modification instructions: Thank you for your suggestion. We have made modifications to all the figures to ensure their coherence and clarity, and we will also send you all the tables later for your reference.

7. In the lines 101 - 107, the authors investigated the phytochemical composition of Mengku large-leaf tea? Add the appropriate reference?

Modification instructions: Thank you for your question. We have already added appropriate references, as stated in the line 453-454.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have addressed the majority of the issues.

However, the figure readability is still low. Please use higher resolution,  thicker lines and larger font for axis labels and scales. If the program generated plots can not be modified, export dataset and plot with a plotting program such as sigma plot and Origin.

Author Response

Thank you very much for taking the time to review this manuscript again. We truly appreciate your suggestions and have made every possible effort to address the issues you raised.

1. However, the figure readability is still low. Please use higher resolution, thicker lines and larger font for axis labels and scales. If the program generated plots can not be modified, export dataset and plot with a plotting program such as sigma plot and Origin。

Modification instructions: Thank you for your advice. we have further improved the clarity of the images. And in order to facilitate your viewing of the article images, we have split all the pictures into smaller ones. This allows you to observe and interpret the details in the images more accurately, thereby improving the readability of the data. You can find them in the compressed folder that we have packaged into the backend.

Author Response File: Author Response.docx

Reviewer 2 Report

Here the significant grammar mistakes in the original passage and their corrections:

·         "EGCG content of tea under different altitudes and soil components" should be "EGCG content in tea under different altitudes and soil components."

·         "Yuecheng base of Xishuangbanna Prefecture, Yunnan Province was selected for analysis" is grammatically correct but could be improved for clarity: "We selected the Yuecheng base in Xishuangbanna Prefecture, Yunnan Province, for analysis."

·         "According to LASSO-cox regression model" should be "Using the LASSO-Cox regression model."

·         "The training set and validation set values in this model, both greater than 0.7" should be "Both the training set and validation set values in this model are greater than 0.7."

·         "This study innovatively provides a tool for the preliminary prediction of EGCG content" should be "This study provides an innovative tool for the preliminary prediction of EGCG content."

·         "The prediction system can not only predict the EGCG content quickly" should be "The prediction system can not only quickly predict EGCG content."

·         "Next, system users can predict other contents in tea by preliminary testing of soil before planting" should be "Next, users of the system can predict other tea contents through preliminary soil testing before planting."

·         "compromise the quality of tea" should be "comprehensively evaluate the quality of tea."

·         "By predicting the content of EGCG in soil, we can ensure that under the premise of reducing the loss of tea" should be "By predicting the EGCG content in the soil, we can ensure that while reducing tea loss."

·         "and extract EGCG from tea as raw materials for drug research and development [43], which not only ensures the quality of tea, but also improves the utilization rate of tea" should be "and extract EGCG from tea as raw materials for drug research and development [43]. This not only ensures tea quality but also improves tea utilization."

·         "Meanwhile, the quality of tea will also be affected by tea varieties [40-44]." should be "Furthermore, tea quality can also be affected by tea varieties [40-44]."

·         "we will enhance research on tea varieties to predict their quality under diverse abiotic stress conditions." should be "We will further research tea varieties to predict their quality under diverse abiotic stress conditions."

·         "furnish a scientific theoretical basis" should be "provide a scientific theoretical basis."

 

·         "The study also laid a solid foundation for further research and prediction of tea yield and quality changes under abiotic stress conditions, and provided a certain theoretical scientific basis." should be "The study also laid a solid foundation for further research and prediction of tea yield and quality changes under abiotic stress conditions and provided a certain theoretical scientific basis."

Dear Author, 

The manuscript entitled “ A Predictive Study on the Content of Epigallocatechin gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model." After thoroughly reviewing the manuscript, I am pleased to share my positive assessment and recommendations for its publication. The manuscript maintains a clear and well-defined focus on predicting the content of EGCG in Yunnan Large Leaf Tea Trees using a nomogram model. This research question is significant and relevant to both the scientific community and the tea industry. The authors have conducted a thorough analysis, including data collection, the use of a nomogram model, and validation. The inclusion of multiple factors adds depth to the research. The use of the nomogram model in the context of tea tree research is an innovative approach. This methodology allowed for the effective prediction of EGCG content and offers a practical tool for stakeholders. The reported model performance indicators demonstrate the reliability and accuracy of the nomogram model, making it a valuable tool for predicting EGCG content. The development and application of the nomogram model offer practical tools for tea tree growers and researchers in predicting EGCG content. It aligns with the principles of predictive modeling and can significantly impact tea quality and production. The authors' intention to explore further applications of the model and its potential for enhancing tea production represents a valuable direction for future research.

·         The study only focused on the changes in Epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, without considering other factors that may influence EGCG content.

·         The LASSO regression method used for modeling factors is rarely used in agriculture, which may limit the generalizability of the findings.

·         The study only assessed the goodness of fit of the prediction model using the Akaike information criterion (AIC), without considering other evaluation metrics.

·         The external verification of the prediction model only achieved an accuracy rate of 75%, indicating that there may be room for improvement in the model's performance.

·         The study did not explore the impact of tea varieties on EGCG content under abiotic stress conditions, which could be an important factor to consider in future research.

·         Include the research article in the manuscript of https://pubmed.ncbi.nlm.nih.gov/35204178/

·         "The study also laid a solid foundation for further research and prediction of tea yield and quality changes under abiotic stress conditions, and provided a certain theoretical scientific basis." should be "The study also laid a solid foundation for further research and prediction of tea yield and quality changes under abiotic stress conditions and provided a certain theoretical scientific basis." 

Reviewer 3 Report

Accept

Author Response

Thank you very much for taking the time to review this draft. We also appreciate your suggestions and questions regarding this manuscript, and we will conduct further research accordingly.

 

Author Response File: Author Response.docx