Dear Dr. Chen,

• Improve the literature review and make it more focused
• Ensure that all mathematical equations are correctly defined and are clear to readers
• Provide a detailed comparison with the state-of-the-art algorithms in the literature to provide validation
• Please add a sensitivity analysis section to demonstrate the robustness
• Provide a table to list every symbol/parameter in your work
• Closely follow the suggestions provided by reviewers to improve the revised version

plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols....

We look forward to receiving your revised manuscript.

Kind regards,

Babak Aslani, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf....

2. Please note that PLOS One has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse.

3. We note that you have indicated that there are restrictions to data sharing for this study. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

Before we proceed with your manuscript, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., a Research Ethics Committee or Institutional Review Board, etc.). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see

https://journals.plos.org/plosone/s/recommended-repositories. You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible.

We will update your Data Availability statement on your behalf to reflect the information you provide.

4. In the online submission form you indicate that your data is not available for proprietary reasons and have provided a contact point for accessing this data. Please note that your current contact point is a co-author on this manuscript. According to our Data Policy, the contact point must not be an author on the manuscript and must be an institutional contact, ideally not an individual. Please revise your data statement to a non-author institutional point of contact, such as a data access or ethics committee, and send this to us via return email. Please also include contact information for the third party organization, and please include the full citation of where the data can be found.

5. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

Reviewer #1: Overall Assessment

This paper addresses a highly relevant problem in bridge monitoring—the refinement of massive datasets for efficient use in large-scale models for resilience control. The motivation is clear and well-justified: as bridge monitoring systems become more sophisticated and generate increasingly large volumes of data, the need for intelligent data reduction strategies becomes critical. The authors demonstrate solid understanding of both structural engineering principles and computational methods, and their effort to integrate domain knowledge from bridge engineering into data processing algorithms represents a valuable research direction. The mathematical formalization of the problem shows rigor and careful thinking about how to represent bridge data systematically. The three-pronged approach—knowledge-based refinement, time-domain refinement, and sparse data handling—is comprehensive and addresses different aspects of the data management challenge. The simulation results, while preliminary, show promising refinement rates for time-domain data, which could have significant practical implications if validated.

Critical Issues Requiring Immediate Attention

1. Literature Review (Section 1.2)

The literature review is too broad and unfocused. Many references (e.g., [8] rumor detection, [12] text-to-image generation, [14] art generation) are tangentially related at best. Reduce this section and focus exclusively on: (a) data processing in structural health monitoring, (b) machine learning in bridge engineering, and (c) time-series compression methods. Clearly identify the research gap your work fills.

2. Lack of Validation and Comparison

This is the most serious deficiency. The paper presents NO comparisons with existing methods (PCA, autoencoders, wavelets, SAX, PAA) and uses ONLY synthetic data.

3. Methodological Clarity

Several methodological elements need clarification:

• Equation (10): The notation d(1-e^ξ/(1+e^ξ))/d(ξ) is ambiguous. Write it clearly as a derivative.

• Algorithm on page 13: "Apply a large-scale model to solve the data loop Dc…" is too vague. Specify the model architecture, training procedure, and loss function.

• Parameters like ε=0.05 and window size W need justification through sensitivity analysis.

4. Reproducibility

The statement "available upon reasonable request" is unacceptable for computational work in 2025. Create a public GitHub repository or archive it on Zenodo for a permanent DOI.

5. Results and Analysis

Current results are insufficient:

• Figure 1: Only 5 I-beam sections—need at least 50 varied cases

• Section 4.2: Only one simple T-beam. Test on continuous spans, cable-stayed, and arch bridges

• 5% Gaussian noise is unrealistic. Real bridges experience non-Gaussian noise from traffic, wind, and thermal effects

• No analysis of whether 97% compression preserves damage detection capability

6. Terminology Issues

The term "large-scale model" is used inconsistently throughout the manuscript, creating confusion about what type of computational approach is actually being employed. It is often unclear whether you are referring to Large Language Models such as GPT-like systems, deep learning models in general, or simply large neural networks with many parameters. This ambiguity is problematic because these are fundamentally different types of models with different architectures, training paradigms, and appropriate applications. To resolve this issue, you should use standard terminology from the machine learning and structural engineering literature. Specifically, use "deep learning models" when referring to multilayer neural networks, "neural networks" or "feedforward networks" for simpler architectures, and "convolutional networks" or "recurrent networks" when those specific architectures are employed. The term "large language models" or "LLMs" should be reserved exclusively for those instances where you are using transformer-based language models like GPT, BERT, or similar architectures, which appears to occur primarily in your graph classification step. This terminological precision will make your methodology clearer to readers and align your work with standard conventions in both the civil engineering and machine learning communities.

Essential Additions

The manuscript requires several critical additions to meet publication standards. First, you must add a comprehensive section titled "Comparison with State-of-the-Art" that includes a detailed table comparing your method against established techniques. This comparison table should include key metrics as well.

A second essential addition is a "Sensitivity Analysis" section that demonstrates the robustness of your proposed method across various conditions. This analysis should systematically vary noise levels different thresholds to show how the algorithm performs under different data quality scenarios. Additionally, you should test the method with missing data percentages ranging from 10% to 70% to establish the limits of your sparse data refinement approach. The sensitivity analysis must also include different bridge types. Finally, explore how variations in window size W and threshold ε affect the refinement rate and accuracy to guide future users in parameter selection.

Third, you must add a "Limitations" section that honestly discusses the constraints and assumptions of your method. This should explicitly acknowledge that the method assumes stationary Gaussian noise and regular sensor grids, which may not hold in all real-world scenarios. You must also acknowledge that validation has been limited to synthetic data and that the method's performance for subtle damage detection in early stages of deterioration remains unknown.

The abstract and contributions sections also require substantial improvement. The abstract should begin by clearly establishing the problem, such as "Bridge monitoring generates massive datasets that challenge current data management and analysis capabilities," before introducing your solution. You must clearly state what is genuinely novel rather than simply stating that you "propose" something. The percentages of 80% and 97% refinement rates need proper context explaining what these numbers mean for practical bridge management. Finally, you must distinguish clearly between your novel contributions, such as the specific application to bridges and the integration of deterioration models into the refinement algorithm, and your adaptations of existing techniques like windowing and interpolation methods.

Language and Presentation

The manuscript requires professional English editing to meet publication standards. Throughout the text, there are numerous instances of awkward phrasing that suggest direct translation from another language. For example, phrases like "with the increase of bridge operation time" should be simplified to "with increased operational time," and expressions such as "cannot be simply integrated" would read more naturally as "cannot be directly unified." Beyond these specific examples, the manuscript suffers from inconsistent mathematical notation, with variables sometimes written as d_i and other times as d_{i,j} without clear explanation of when each form is appropriate, and approximation symbols alternating between ≈ and ≃ apparently interchangeably. These inconsistencies, while seemingly minor, can confuse readers and detract from the technical content.

Reviewer #2: This work examines data preprocessing for bridge-resilience control supported by large models. As bridge-resilience control technology advances, the sheer volume of data has begun to undermine control efficiency. To address this issue, the author proposed a preprocessing method that incorporates domain knowledge and time-domain features, and demonstrated its use in simulation analyses. The problem tackled is worthwhile; however, the manuscript does not yet demonstrate sufficient scientific rigor or value and would require major revision before it could be considered for publication. The specific modification suggestions are as follows:

1. Please restructure the Introduction according to the standard logic of the research paper; the current logic does not make the theoretical or technical contribution clear.

2. Move the literature review currently in Section 1.2 to a new '2. Related Work' section.

3. There are a large number of inaccurate paragraph divisions in the manuscript. For example,

“Correlation refers to the correlation between data. The correlations studied in this article include geometric correlations and temporal correlations.

Geometric correlation refers to the geometric shapes or structures composed of data within the subset D_k of a dataset, D_k\subset D. When D_k==D , D_k constitutes the entire bridge structure. Time domain correlation refers to the correlation generated in the time domain of a certain data of a bridge structure over time. For example, the acceleration data and displacement data collected by the bridge health monitoring system all satisfy time-domain correlation.”

should be a single paragraph. Please correct all such paragraph divisions throughout the manuscript.

4. Provide a nomenclature table that lists every symbol/parameter used in the paper.

5. All algorithm processes are currently presented in a non-standard format: they lack numbering, explicit Input/Output blocks, etc. In addition to presenting the algorithms in the standard way, it is recommended to use more descriptive language for the algorithm processes. For example, using '→' directly in an algorithm without explanation can confuse readers (in mathematical expression, '→' can mean 'tends to'; in pseudocode, it is often understood as 'assignment').

6. Supply pseudocode for every algorithm, either in the main text or in an appendix.

7. Step 7 of the time domain data refinement algorithm states 'Call the large-scale model to classify G and generate a classification quantity k'. What the large-scale model is used in this step? What is the specific classification algorithm? How are the specific parameters set? If these issues are not clearly stated in the manuscript, the scientificity and accuracy of the subsequent analysis results cannot be guaranteed.

8. The claim 'The resilience-control data of bridges is affected by factors such as the number of equipment and equipment failures' is unconvincing as an explanation for data sparsity. Commercial sensing systems are designed to fail rarely; therefore, device failure alone cannot justify the observed scarcity. Provide stronger evidence or cite published studies that substantiate this argument.

9. Justify the choice of \varepsilon=0.05 in Equation (18).

10. Compare the proposed refinement algorithms with state-of-the-art alternatives to demonstrate their superiority.

Reviewer #3: Comments to Author(s)

Article Title: The title is very ok and it flows along just that it is too long the author should find away to refine it abit.

Abstract

The abstract is good to go; everything is stated correctly, from its introduction to the problem, methodology, and result output was mentioned as related to its simulation analysis but it does not mention its performance explicitly rather it proceeded to the future research.

It is also advisable to use a grammar editor or an expert in grammar or language to assist in correcting grammatical and punctuation errors.

LITERATURE REVIEW: The review is done, properly referenced, concurrence is important as it is supposed to flow along and tailored to the current research which means that the closest research need to be considered.

- PERFOMANCE METRICS

o Algorithms are well and properly stated, The system model and the transformation of time domain data is well articulated.

METHODOLOGY:

- This is properly presented, there are a lot of information mentioned in the methods and strategy which has been explicitly described.

- Kindly re-arrange the Methodology for simplicity and easy tracking so that It can be simpler to relate the methods, results and output.

RESULT

- The result is well presented inclusive of all necessary graph.

- The concluding part is too scanty compare to too many equations and solutions provided, I will suggest it should be a more explicit way of concluding

- Supporting information is not necessary, but it can also be integrated into the conclusion in a summarized manner.

CONCLUSION:

This paper is good as it stated the key aspect of the research and it widen the scope as it continue to state explicitly the research motivation, and it is very important in the core area of Algorithm and Case Analysis so it requires a lot of attention while dissemination them.

**********

what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Dear Dr. Chen,

=====================================

• Please closely read the reviewer feedback regarding using LLM for graph classification. I believe that this part of your work can be revised with a more solid choice. So, please make sure to address this critical point in the revised version.

=====================================

plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols....

We look forward to receiving your revised manuscript.

Kind regards,

Babak Aslani

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: No

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: The author has addressed all the reviewer concerns, therefore he has fullfilled all the requirements.

Reviewer #2: The revised manuscript presents its innovation and technical details more clearly, but also exposes potential technical risks. The DeepSeek-chat model, a famous LLM, is invoked to classify the graphs generated by the curvature mod. However, LLM is probability generated. Its primary goal is to generate natural, diverse, and creative text, rather than conducting stable deterministic calculations. For tasks such as graph classification that require structural awareness and deterministic computation, specialized GNN models far outperform LLM in accuracy, efficiency, consistency, and interpretability. LLM is more suitable as an auxiliary tool for interpreting, summarizing, or generating descriptive text based on graph data. Therefore, I believe that there is technical uncertainty in this work, which may lead to potential technical risks in the actual operation process. My final suggestion is to reject the manuscript.

In addition, there is a small issue that the author can refer to: the next paragraph of equation (15) repeats the discussion after equation (14).

**********

what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Data compression of Bridge Resilience Control: Algorithm and Case

PONE-D-25-50186R2

Dear Dr. Chen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support....

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Babak Aslani, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Every comment was addressed succesfully by the author following the indications, therefore recommend its publication under its present form.

Reviewer #2: Thank you very much for the author's efforts in revising the manuscript and completing the experimental part again. The methods and techniques used in this manuscript are reasonable, and the quality of the manuscript has been greatly improved. I suggest accepting this manuscript for publication on PlOS One.

**********

what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #2: No

**********