Although wildfires are an important ecological process in forested regions worldwide, they can cause significant economic damage and frequently create widespread health impacts. We propose a network optimization approach to plan wildfire fuel treatments that minimize the risk of fire spread in forested landscapes under an upper bound for total treated area. We used simulation modeling to estimate the probability of fire spread between pairs of forest sites and formulated a modified Critical Node Detection (CND) model that uses these estimated probabilities to find a pattern of fuel reduction treatments that minimizes the likely spread of fires across a landscape. We also present a problem formulation that includes control of the size and spatial contiguity of fuel treatments. We demonstrate the approach with a case study in Kootenay National Park, British Columbia, Canada, where we investigated prescribed burn options for reducing the risk of wildfire spread in the park area. Our results provide new insights into cost-effective planning to mitigate wildfire risk in forest landscapes. The approach should be applicable to other ecosystems with frequent wildfires. Wildfires, while being a natural ecosystem process in many biomes, can pose significant economic and social threat to human communities in forested regions –3. Land Preventive fuel treatments, such as prescribed burns or strategic thinning of forest stands, are intended to decrease the probability of fire spread and reduce fire severity and, consequently, the damage to human infrastructure. While there is a consensus that a substantial reduction of flammable biomass will reduce fire spread and severity, factors such as the location, size, maintenance, and use in fire operations may undermine fuel-treatment effectiveness ,6. If efOptimization has been widely used to support decisions about fire prevention and suppression –20. SeveSeveral of the proposed models minimized connectivity between forest patches with high wildfire risk as a way to reduce fire spread potential in a landscape . Minas aIn this paper, we employ a directional metric that quantifies the probability of fires spreading between a pair of locations in a landscape to solve a fuel treatment problem: allocating a set of prescribed burns to minimize the chance that wildfires will spread through the landscape, subject to an upper bound on the total treated area of prescribed burning or similar fuel reduction treatments (henceforth referred to as “prescribed burning”). For each pair of forest patches, we estimate the probability that a fire ignited in one patch will spread to another. We incorporate this metric into a modified Critical Node Detection (CND) problem –39, whicA forest landscape can be thought of as a connected network of flammable patches (nodes), where the connecting arcs (edges) depict possible vectors of fire spread between adjacent patches. To minimize the possibility of fires spreading widely across the area, the manager allocates a set of treatments (prescribed burns) among the nodes. Treating a node helps reduce fire intensity  to the pA popular strategy for solving this problem is to reduce the connectivity between nodes with flammable fuels in the landscape network. This strategy can be implemented by solving a CND problem, which finds the key nodes in a network whose removal maximally degrades the connectivity of the network according to a chosen metric –38,43–45G =  be a graph with a set of N nodes (vertices) and a set of edges E, E ⊂ N×N  and there is a path connecting i and j, i.e., when nodes i and j are in the same connected component . The graph obtained after a removal of R critical nodes is a subgraph(s) of G composed of the set of remaining nodes, N \ R  after removal of al nodes ,37. BeloO(|N|2) constraints and is more efficient than the original CND problem formulation with triangular inequalities . Constraints . Set Θi denotes adjacent nodes j (including node 0) that can pass flow to node i, while set Θi+ denotes adjacent nodes k that can receive flow from i. Constraint (ij is not selected and constraint (ij if no flow occurs between nodes i and j in step t. Constraint (i from other nodes (including node 0) over T steps comes through no more than one arc. This guarantees no overlap between the node selections in different steps t. Constraint  and zero otherwise. Constraint  ensures straints –12 ensurnstraint  ensures nstraint  ensures nstraint  specifienstraint  specifienstraint  ensures T planned burns. The time to find a feasible solution can be reduced by replacing objective t’ includstraints  and 7)  t’ inclut’ to account for a cumulative reduction of fire spread potential after each period. This makes problem 3 combinatorically harder than problem 2. To reduce the solving time, we used the problem 2 solutions to initialize problem 3. We first solved problem 3 with constraint (B), i.e.:χi denotes the values of decision variable xi in problem 2 solution. Under constraint  from i to j because the spread probability value pij only defines the likelihood that a fire which is ignited in location i will spread to location j and does not require specification of how the fire might spread from i to j. Evaluating the presence of a path  connecting nodes i and j is handled by the CND model constraints ..i,j in tpij between node pairs ij in landscape N. Fire simulation models generate stochastic ignition events and plausible perimeters of fires spreading from the ignition locations  from the solution with T = 3 as a warm start, and so on until we solved the model for a desired number of steps. We then used the set of decision variables from the last solution to warm start the full problem. Problem 3 was initialized from problem 2 solutions in similar fashion. The model was run for 72 hours or until reaching an optimality gap of 0.5%, whichever came first. The model was composed in the General Algebraic Modeling System Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?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.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: General comments:Your work is interesting and, for the most part, well-presented. I find no major flaws in your analysis that warrant revision to the methods, but there are a few sections of the paper that could be improved to clarify your assumptions and methods. I’ll highlight my main critiques here, followed by specific recommendations.First, I suggest you revisit the abstract to make sure it aligns with what you accomplished. Specific notes follow.Fire behavior is a general term that includes spread but also intensity, duration, and type. In most cases, I think it would be clearer to say something like burn probability or spread likelihood instead in this paper.Does the problem 3 formulation with time add value to your analysis? As you point out, you do not actually remodel the fire spread probabilities. My personal view is that there is little value planning far in advance in fire-prone landscapes because stochastic wildfire activity will alter the priorities for subsequent periods more than the treatments. If you are interested, other researchers have examined the effects of uncertainty in fire occurrence using two stage models solved with backwards induction:Konoshima M, Montgomery CA, Albers HJ, Arthur JL (2008) Spatial-endogenous fire risk and efficient fuel management and timber harvest. Land Economics 84(3), 449-468.Konoshima M, Albers HJ, Montgomery CA, Arthur JL (2010) Optimal spatial patterns of fuel management and timber harvest with fire risk. Canadian Journal of Forest Research 40, 95-108.These should probably be cited in the discussion when discussing multiple planning periods. I’m surprised you advocate for problem 3 formulation in the discussion given that you say it produced similar results as problem 2 but with added complexity. Why?If you had more space, I would recommend you compare your method to something simple and widely used, such as Finney’s treatment optimization method or specific patterns of fuel treatments. When you start to describe the contexts that your problem 3 can be applied in , I start to wonder if you really diverge from a plan that interrupts the major spread paths of an anticipated problem fire scenario.Finney MA (2004) Chapter 9, Landscape fi re simulation and fuel treatment optimization. In: J.L. Hayes, A.A. Ager, J.R. Barbour, (tech. eds). Methods for integrated modeling of landscape change: Interior Northwest Landscape Analysis System. PNW-GTR-610. p 117-131.Finney MA (2001) Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science 47(2), 219-228.Specific comments:Lines 40-42: It is fair to point out that resources to mitigate wildfire risk are limited, and it is therefore important to prioritize, but there are many tools to assist forest managers in planning fuel treatments. I suggest dropping the focus on limited tools here. Whether the tools are used by managers is another issue.Line 42: I suggest replacing “interdiction” with common language since you felt the need to define it in the paper.Line 43: Did you really cover consequences in this work? I didn’t see an effects analysis here.Lines 44-45: How about “We used simulation modeling to estimate the likelihood of fire spread between forest network nodes and we…”Line 46: fuel treatment ALLOCATION problemLines 64-68: Yes. Costs tend to rise with fire sizes, but rising suppression costs are often attributed to the expansion of human values into wildlands. It would be less controversial if you focused this statement only on the challenge of suppressing fires in rugged landscapes.Lines 69-70: I think it is appropriate to at least mention that not all forestry and fire scientists agree that fuel treatments will reduce fire spread. Your Agee reference on shaded fuelbreaks includes discussion of this. Agee and Skinner (2005) and Reinhardt et al. (2008) argue strongly that most fuel treatments are, or should be, aimed at reducing fire intensity and severity.Lines 70-72: Is the saving costs statement supported by these citations? It was a modeling study, but the Thompson et al. (2013) ‘Quantifying the Potential Impacts of Fuel Treatments on Wildfire Suppression Costs’ article provides the clearest estimates of how fuel treatments could reduce costs via their effects on fire sizes.Lines 76-77: Again, I think it is appropriate to acknowledge that some of these models were aimed at reducing the severity of effects instead of large fire spread.Lines 115-116: I would drop the subscripts here and save them for the methods.Lines 127-130: Again, I think you should temper this statement to make it clear that it is more of an assumption supported by rules of thumb than a clear conclusion of the research. It is also prudent to acknowledge that fuel treatments do not generally achieve 100% protection, especially in the case of extreme weather. The Kalies and Kent (2016) review on fuel treatment effectiveness may be worth mentioning here.Line 134: You already introduced the CND abbreviation.Line 156-157: Would it not be simpler to introduce the model as area limited since you don’t account for variable costs? I see the future value of accounting for this, but it adds slight confusion to the paper. For example, you describe the model as having an upper bound for Rx fire area in the abstract and introduction.Lines 183-185: And fire suppression?Line 197: “depict well” to “represent”?Line 234: “Consecutively” or “consequently”?Lines 313-314: I do not think it is a good idea to use T and t for both steps in problem 2 and time periods in problem 3. I suggest changing a different letter for the steps in problem 2 to avoid confusion.Lines 405-407: Rephrase for clarity.Lines 437-442: You should clarify exactly how the information you mention was used. Were treatments limited to a particular vegetation type? Did you use the ignition probability, but not spread probability components of Burn P3? Is this later what you refer to as prioritizing on ignitions?Lines 443-452: This is where it would help to know the difference between T steps and T time periods.Lines 464-474: As noted in my general comments, this is an important enough change in methods that you should clarify which results it applies to  and describe it fully in the methods section instead of supplementary material referenced from the results.Lines 488-489: “ignoring spatial contiguity rules”? or “ignoring the simulated connectivity measures”?Line 494: Suggest changing fire behavior to fire spread.Lines 520-525: Why is this scenario suddenly popping up in the results? This should be introduced earlier with justification for what it tells you. Prescribed fires likely reduce ignition risk for a short period after treatment, but this will not last long as fuels reaccumulate. Reducing ignitions with rules and enforcement may require different methods in some landscapes.Lines 579-582: I’m confused about what scenarios are being compared here.Lines 598-611: I’m wondering how much the small/large fire size tradeoff that is important at this site pertains to the use of probability vs. binary fireshed weighting versus the specific pattern of fire sizes and occurrence on your landscape? What do you think you would find on a landscape with high probability of fire spread from less frequent but large fires?Lines 615-619: Did the approximation you made to get at spread paths within fires really “address” the problem? I don’t have a brilliant solution to do better without complicating the simulation. I would be tempering my language here to reflect that some approximations were made to prototype a model framework.Line 625: With a shortest path approximation…Figure 3: You should probably include a scale bar and north arrow in the study site panel.Reviewer #2: GENERAL COMMENTThe manuscript entitled “Detecting critical nodes in forest landscape networks to reduce wildfire spread” aims to propose a modeling approach to optimize preventive fuel treatments for minimizing the wildfire spread likelihood and consequences. The methodological approach presented in this manuscript was tested in a study area located in Kootenay National Park, British Columbia (Canada).Overall, I do think the work is interesting and has the potential to provide insights and methods for future studies or analysis that would investigate the potential effects of spatial locations of fuel treatments on wildfire spread, while considering the maximization of the benefit/cost ratio. The present study could also provide relevant information for policy makers and stakeholders to adapt or improve future management plans and strategies in Canada as well as in other areas.https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/03-5210; Davies et al. 2015, http://dx.doi.org/10.1071/WF15055; Salis et al. 2018, https://www.sciencedirect.com/science/article/pii/S0301479718301191; Prichard et al. 2020 https://pubmed.ncbi.nlm.nih.gov/32086976/ doi:10.3390/f6062148). This is a shortcoming that can be improved.The Introduction section is well written and provides a generally good overview of the works that investigated this topic. I only have a remark. The authors limit the Introduction section focusing on previous works carried out in forest areas and using prescribed burnings, while the applicability of the approach they propose could be expanded also to semiarid or rural areas, as well as to fuel management strategies different than prescribed fires . Even if manuscripts published in Plos One can be any length, there is need to reduce this part and omit some redundant sentences. Some specific points to improve this section will be provided in later rows.The Results are in my opinion fine. I would suggest making some improvements in Figures 3 and 4.The Discussion section needs to be improved, as the comparison between the results and approach presented in this work are not compared with those obtained in other similar works.In the end, the manuscript is overall of good quality and well-written, but should be improved by reducing the length of the text and improving the quality of the discussion sections.SPECIFIC COMMENTShttps://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/03-5210; Davies et al. 2015, http://dx.doi.org/10.1071/WF15055; Salis et al. 2018, https://www.sciencedirect.com/science/article/pii/S0301479718301191; Prichard et al. 2020 https://pubmed.ncbi.nlm.nih.gov/32086976/ doi:10.3390/f6062148).Introduction: The Introduction section seem too much focused on works carried out in forest areas and application of prescribed burnings, while the approach proposed in this study could be expanded also to semiarid or rural areas, as well as to fuel management strategies different than prescribed fires L399-402: Considering that no information is provided on crown fire and spot fire settings, I suppose the authors applied Burn-P3 model to simulate surface fire spread. In case the authors simulated crown fires and spot fires, I would recommend including more details on this.L408-410: Please include a table, in the Supplementary data, to summarize the main input data used for fire simulations L435-439: Problems 1-4 related to the critical nodes detection (CND) were introduced in the first equations, several pages before this part. I would recommend helping readers and clarifying that these problems refer to the first equations and the detection of critical nodes.L533: Starting a sentence with “Recall that” might be inappropriate, please checkL613-666: The Discussion section summarizes relatively well the principles and generalizations from results as well as the significance of results. On the other hand, it does not discuss the results and methods presented in this work in relation to those of others. This is a limitation, so I recommend improving the Discussion in this sense.Figures 3-4: Please include the scale bar.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  5 Aug 2021PONE-D-21-15640 “DETECTING CRITICAL NODES IN FOREST LANDSCAPE NETWORKS TO REDUCE WILDFIRE SPREAD” – A Reply to Reviewers’ Comments.Academic Editor’s comments:Both reviewers have indicated that the manuscript requires revisions to the Discussion, specifically that you should compare your method against existing methods to show the improvements or disadvantages.We have added text comparing our technique with some previously proposed treatment methods.One common strategy is to treat sites with the highest likelihoods of ignition . However, this strategy does not optimally reduce the risk of spread of escaped fires nor does it address the uncertainty of determining the sites with the highest fire ignition potential. By comparison, our probabilistic fireshed strategy compartmentalizes regions with high ignition potential, thus providing a hedge against the possibility of fires escaping to spread elsewhere.Several other fuel treatment strategies have used site-specific priority weights. Minas et al. (2015) linked site treatment and deployment resources to minimize the number of sites covered by these activities. Each site was assigned a weight by ignition probability and the value under risk if a fire originating in that site is not contained by the initial response. Rachmawati et al. (2015) focused on rapid fuel accumulation after treatment and used site-based combinations of vegetation type and age since fire to find an optimal multi-period sequence of fuel treatments. Wei (2012) applied optimization of fuel treatment at a very small scale (7x7 rows) without embedding a fire simulation model but examined the geometry of the treated areas. Finney et al.  proposed the assessment of fuel treatments by dividing the landscape into rectangular strips oriented normal to the predominant wind directions. Then, fire growth was simulated, starting with the strip farthest upwind, to identify key fire spread routes and their intersections with the potential treatment areas. The process was repeated after moving each strip in the direction of the wind to impact downwind travel routes and subsequent treatment areas. This method finds fuel treatment configurations for a set of likely fire spread routes but overlooks the combinatorial aspects when allocating multiple treatments under a limited budget. Another network-based approach aimed to minimize the connectivity between sites with high fuel loads . Pais et al. (2021) used a network flow model to control the spatial contiguity of the treated area and prioritized treatments using a site-based fire risk metric . The DPV metric assigns treatment priority ranks to sites by modeling fire propagation through a forest landscape as a tree graph and accounts for the potential of a fire ignited at a given site to burn other sites. In contrast, our model makes decisions using the fire spread probabilities between pairs of locations, which enables control based on the presence of possible fire spread paths between these locations. ______________________________________________________Reviewer 2 has indicated that the length of the methods should be reduced, but expects additions around describing the study area and Burn-P3 parameterization.We wanted to note that the Methods section includes the formulation and description of the Critical Node Detection model. The new model formulation is a result on its own but, in keeping with tradition, is presented in the Methods section. This explains the larger-than-normal section size. We believe that the CND model requires a detailed description to understand its principles, and so keeping the larger section size felt justified. However, the fire behaviour simulation model was already published previously in Reimer at al. (2019), so we have only included a brief summary of this model in the main text and moved the description of the its parameters to Supplement S1.______________________________________________________ Reviewer 1 has offered some specific suggestions for improvements in the methods section and the authors should consider moving some text to supplementary materials as suggested by Reviewer 2. Though I found the methods to be a lot to wade through, much of it is necessary in my mind.We agree that much of the descriptive material in the Methods section is necessary to understand how the model works. To reduce the section size, we have moved a portion of text describing the Burn-P3 model and a table with the model parameters to Supplement S1.______________________________________________________ 2. We note that Figure 3 in your submission contain map images which may be copyrighted. <…>We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:Figure 3 is our own design. While it resembles the figure from Reimer et al. (2019), it was created in GIS from scratch using Burn-P3 inputs and data layers in the public domain, and therefore does not require permission to publish.______________________________________________________ Reviewer’s 1 comments:Reviewer #1: General comments:First, I suggest you revisit the abstract to make sure it aligns with what you accomplished. Specific notes follow.We have edited the abstract to make it more succinct.______________________________________________________Fire behavior is a general term that includes spread but also intensity, duration, and type. In most cases, I think it would be clearer to say something like burn probability or spread likelihood instead in this paper.We have used fire spread in the text to avoid confusion. ______________________________________________________Does the problem 3 formulation with time add value to your analysis? As you point out, you do not actually remodel the fire spread probabilities. My personal view is that there is little value planning far in advance in fire-prone landscapes because stochastic wildfire activity will alter the priorities for subsequent periods more than the treatments.We agree with the reviewer about there being little value to long-term planning in fire-prone landscapes and perceive our approach as a short-term planning tool. Nevertheless, even short-term treatments are typically implemented in a stepwise fashion over a defined time period. The idea behind the problem 3 formulation is to account for the cumulative nature of multi-step planning per se; that is, the actions taken in the first step have the most impact on the system and may thus affect the actions taken in the subsequent steps. The primary difference between the problem 2 and 3 formulations is that the problem 3 formulation allocates the treatments with the greatest impact on fire spread probabilities first, followed by less impactful treatments. We have edited the text to make this point clearer.______________________________________________________If you are interested, other researchers have examined the effects of uncertainty in fire occurrence using two stage models solved with backwards induction:Konoshima M, Montgomery CA, Albers HJ, Arthur JL (2008) Spatial-endogenous fire risk and efficient fuel management and timber harvest. Land Economics 84(3), 449-468.Konoshima M, Albers HJ, Montgomery CA, Arthur JL (2010) Optimal spatial patterns of fuel management and timber harvest with fire risk. Canadian Journal of Forest Research 40, 95-108.These should probably be cited in the discussion when discussing multiple planning periods. The suggested references and a brief section of text referring to two-stage models have been added to the Introduction section:Konoshima et al.  integrated a fire simulation model into a two-period stochastic dynamic model to find spatial allocations of timber harvest and fuel management in the face of spatially endogenous fire risk. Their approach used a fire simulation model to enumerate all possible fire occurrence patterns in all plausible treatment decisions and considered the trade-offs between fire risk, timber harvest value and fuel treatment cost.______________________________________________________I’m surprised you advocate for problem 3 formulation in the discussion given that you say it produced similar results as problem 2 but with added complexity. Why?Problems 2 and 3 produced similar spatial results but different allocation of treatments in time. Problem 3 helps find the optimal sequence of treatments , whereas problem 2 does not address the issue of optimal timing because it solves only one CND network for an entire planning horizon. In a sense, problem 3 is more realistic because it provides guidance to forest managers about a stepwise implementation of the fuel reduction strategy.______________________________________________________If you had more space, I would recommend you compare your method to something simple and widely used, such as Finney’s treatment optimization method or specific patterns of fuel treatments. When you start to describe the contexts that your problem 3 can be applied in , I start to wonder if you really diverge from a plan that interrupts the major spread paths of an anticipated problem fire scenario.Finney MA (2004) Chapter 9, Landscape fire simulation and fuel treatment optimization. In: J.L. Hayes, A.A. Ager, J.R. Barbour, (tech. eds). Methods for integrated modeling of landscape change: Interior Northwest Landscape Analysis System. PNW-GTR-610. p 117-131.Finney MA (2001) Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science 47(2), 219-228.The only limitation for problem 3 is high numerical complexity. The model can be applied to larger landscapes at a coarser spatial resolution. We have added a brief discussion comparing our approach with other methods – see our reply to the first comment from the Academic Editor.______________________________________________________ Specific comments:Lines 40-42: It is fair to point out that resources to mitigate wildfire risk are limited, and it is therefore important to prioritize, but there are many tools to assist forest managers in planning fuel treatments. I suggest dropping the focus on limited tools here. Whether the tools are used by managers is another issue.The text discussing limited tools has been dropped following the reviewer’s suggestion.______________________________________________________ Line 42: I suggest replacing “interdiction” with common language since you felt the need to define it in the paper.We have dropped the mention of interdiction – critical node detection is already a good description of the approach.______________________________________________________ Line 43: Did you really cover consequences in this work? I didn’t see an effects analysis here.Yes – essentially, this is what the CND formulation in problems 1-3 does.______________________________________________________ Lines 44-45: How about “We used simulation modeling to estimate the likelihood of fire spread between forest network nodes and we…”The text has been edited as suggested by the reviewer.______________________________________________________Line 46: fuel treatment ALLOCATION problemThis text fragment was deleted.______________________________________________________ Lines 64-68: Yes. Costs tend to rise with fire sizes, but rising suppression costs are often attributed to the expansion of human values into wildlands. It would be less controversial if you focused this statement only on the challenge of suppressing fires in rugged landscapes.We have edited the text to focus the statement on managing fires in rugged landscapes.______________________________________________________Lines 69-70: I think it is appropriate to at least mention that not all forestry and fire scientists agree that fuel treatments will reduce fire spread. Your Agee reference on shaded fuelbreaks includes discussion of this. Agee and Skinner (2005) and Reinhardt et al. (2008) argue strongly that most fuel treatments are, or should be, aimed at reducing fire intensity and severity.We agree with the reviewer that the focus should not entirely be placed on spread and have added references to fire severity. Although limiting fire spread defines the proposed strategies, we should have better acknowledged the benefits of fuel treatments in reducing fire intensity and severity with respect to the intended purpose of the fuel treatments. In addition to the probability of fire spread, our approach could incorporate other fire behaviour parameters (such as fire intensity) in conjunction with spread as long as such data could be generated with the fire simulation models. We added some explanatory text about this to the Discussion. ______________________________________________________Lines 70-72: Is the saving costs statement supported by these citations? It was a modeling study, but the Thompson et al. (2013) ‘Quantifying the Potential Impacts of Fuel Treatments on Wildfire Suppression Costs’ article provides the clearest estimates of how fuel treatments could reduce costs via their effects on fire sizes.We have added text referring to Thompson et al. (2013) as the reviewer suggested.______________________________________________________ Lines 76-77: Again, I think it is appropriate to acknowledge that some of these models were aimed at reducing the severity of effects instead of large fire spread.We have added text acknowledging that some models were also designed to reduce the severity of future fires in the landscape. ______________________________________________________ Lines 115-116: I would drop the subscripts here and save them for the methods.Done.______________________________________________________ Lines 127-130: Again, I think you should temper this statement to make it clear that it is more of an assumption supported by rules of thumb than a clear conclusion of the research. It is also prudent to acknowledge that fuel treatments do not generally achieve 100% protection, especially in the case of extreme weather. The Kalies and Kent (2016) review on fuel treatment effectiveness may be worth mentioning here.The statement has been edited to make clear that this assumption is a simplification and we acknowledge that fuel treatments are not usually 100% effective. A citation to Kalies and Kent (2016) has been added to the text.______________________________________________________ Line 134: You already introduced the CND abbreviation.Dropped.______________________________________________________ Line 156-157: Would it not be simpler to introduce the model as area limited since you don’t account for variable costs? I see the future value of accounting for this, but it adds slight confusion to the paper. For example, you describe the model as having an upper bound for Rx fire area in the abstract and introduction.We have replaced the budget limit with a treatment area limit as suggested by the reviewer.______________________________________________________ Lines 183-185: And fire suppression?Edited.______________________________________________________ Line 197: “depict well” to “represent”?Edited______________________________________________________ Line 234: “Consecutively” or “consequently”?Consequently.______________________________________________________ Lines 313-314: I do not think it is a good idea to use T and t for both steps in problem 2 and time periods in problem 3. I suggest changing a different letter for the steps in problem 2 to avoid confusion.We have denoted time periods and the full timespan in problem 3 as t’ and T’, respectively, while keeping t (a planning step) and T  for problem 2. We opted to use the same letter to highlight the analogies between the problem 2 and problem 3 formulations.______________________________________________________ Lines 405-407: Rephrase for clarity.The sentence was deleted.______________________________________________________ Lines 437-442: You should clarify exactly how the information you mention was used. Were treatments limited to a particular vegetation type? Did you use the ignition probability, but not spread probability components of Burn P3? Is this later what you refer to as prioritizing on ignitions?No – this scenario only used a binary map of flammable / non-flammable land cover types  and the flammable land classes were expected to support the spread of fires. Comparatively, the scenarios using fire behaviour information utilized fire spread probabilities pij calculated with the fire simulation model.The only scenario that used ignition probabilities instead of pij values was the solution that allocated treatments to minimize the ignition probability in Fig 8. We have edited the text to make this clearer.______________________________________________________Lines 443-452: This is where it would help to know the difference between T steps and T time periods.We have changed the notation for time periods in the problem 3 formulation to t’ (and the notation for the full timespan to T’) where appropriate. ______________________________________________________ Lines 464-474: As noted in my general comments, this is an important enough change in methods that you should clarify which results it applies to  and describe it fully in the methods section instead of supplementary material referenced from the results.The calculation of the wij values is not related to the CND model per se – these values were used only to map the fire spread hotspots between multiple pairs of locations. Direct mapping of pij arcs makes the map cluttered and difficult to read. The new mapping procedure of fire spread probabilities is a novelty on its own and could help better understand the fire spread patterns in complex landscapes. The full description of the wij mapping procedure is beyond the scope of the current study – this is the focus of another manuscript. The text provides only basic description germane to understanding the fire spread probability maps. The text has been edited to make this aspect clearer.______________________________________________________ Lines 488-489: “ignoring spatial contiguity rules”? or “ignoring the simulated connectivity measures”?Ignoring spatial contiguity constraints for prescribed treatments – the sentence has been edited.______________________________________________________ Line 494: Suggest changing fire behavior to fire spread.Edited.______________________________________________________Lines 520-525: Why is this scenario suddenly popping up in the results? This should be introduced earlier with justification for what it tells you. Prescribed fires likely reduce ignition risk for a short period after treatment, but this will not last long as fuels reaccumulate. Reducing ignitions with rules and enforcement may require different methods in some landscapes.We have introduced a scenario minimizing ignition probabilities  in the Methods section, after the description of the scenario based on land cover information only. In our case, we have only done a basic comparison of methods with the same treatment area. Implementing a practical enforcement scenario that reduces the probability of ignitions would require adapting both the CND and ignition-minimizing scenarios to the current practical standards and is considered as a theme for another manuscript.______________________________________________________Lines 579-582: I’m confused about what scenarios are being compared here.These sentences were dropped to avoid confusion.______________________________________________________ Lines 598-611: I’m wondering how much the small/large fire size tradeoff that is important at this site pertains to the use of probability vs. binary fireshed weighting versus the specific pattern of fire sizes and occurrence on your landscape? What do you think you would find on a landscape with high probability of fire spread from less frequent but large fires?Given that the model behaviour depends on the spatial configuration of fire spread patterns in the landscape, this question may best be answered by testing the CND model on real landscapes with high probabilities of large fires . This could be the focus of future work. To target a particular range of fire sizes, the pij value for each pair of locations i and j could be adjusted in the objective equation by a user-defined coefficient based on the distance between i and j. This would make the approach adaptable to other fire regime conditions and management objectives. The extent of this adaptability to different fire regimes will be examined in further studies.______________________________________________________Lines 615-619: Did the approximation you made to get at spread paths within fires really “address” the problem? I don’t have a brilliant solution to do better without complicating the simulation. I would be tempering my language here to reflect that some approximations were made to prototype a model framework.We did not use approximation to calculate the pij probabilities of fire spread between pairs of locations. As shown in Supplement 1, the pij calculations used raw fire simulation model outputs . A pij value only defines the probability that a fire ignited in location i will spread to location j, but does not specify how exactly the fire could spread from i to j; in short, the CND model does not require exact specification of the fire spread paths between i and j. In our study, we have used the ignition points and perimeters of the simulated fires, but did not track specific (and possibly dynamic) fire spread paths within individual fires. Tracking daily or hourly fire spread within individual fire perimeters could potentially refine the pij values, particularly for long spread distances, but would require a more sophisticated fire simulation model that can output the expansion of individual fires on an hourly or daily basis. This would require updating the Burn-P3 simulation model and could be a topic for future research.______________________________________________________Line 625: With a shortest path approximation…No, there is no shortest path approximation in the calculation of the pij values. The pij values were calculated directly from raw fire simulation model outputs . The CND model only needs to know the probability that a fire ignited in location i will spread to location j and does not require specification of a spread path from i to j. Accounting for the presence of a path connecting a pair of locations i and j is handled by the CND model constraints (3) and (4). Note that we did use a shortest path approximation to calculate the wij values, but this metric is not used in the CND model and was utilized only for mapping the fire spread patterns.______________________________________________________ Figure 3: You should probably include a scale bar and north arrow in the study site panel.We have added a scale bar and north arrow to Figure 3.______________________________________________________ Reviewer’s 2 comments: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/03-5210; Davies et al. 2015, http://dx.doi.org/10.1071/WF15055; Salis et al. 2018, https://www.sciencedirect.com/science/article/pii/S0301479718301191; Prichard et al. 2020 https://pubmed.ncbi.nlm.nih.gov/32086976/ doi:10.3390/f6062148). This is a shortcoming that can be improved.The Introduction section is well written and provides a generally good overview of the works that investigated this topic. I only have a remark. The authors limit the Introduction section focusing on previous works carried out in forest areas and using prescribed burnings, while the applicability of the approach they propose could be expanded also to semiarid or rural areas, as well as to fuel management strategies different than prescribed fires . Even if manuscripts published in Plos One can be any length, there is need to reduce this part and omit some redundant sentences. Some specific points to improve this section will be provided in later rows.We wanted to note that the methods section includes the formulation and description of the new Critical Node Detection model. The model formulation represents a new result on its own but is traditionally presented in the Methods section. The model formulation also required detailed explanations to understand its principles. Nevertheless, we have edited the Methods section, reducing textual descriptions wherever possible, eliminating redundancies and moving a portion of the Burn-P3 fire model description to Supplement S1.______________________________________________________ The Discussion section needs to be improved, as the comparison between the results and approach presented in this work are not compared with those obtained in other similar works.We have also added a short discussion describing the other treatment strategies – see our reply to the first comment from the Academic Editor.______________________________________________________Specific comments:https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/03-5210; Davies et al. 2015, http://dx.doi.org/10.1071/WF15055; Salis et al. 2018, https://www.sciencedirect.com/science/article/pii/S0301479718301191; Prichard et al. 2020 https://pubmed.ncbi.nlm.nih.gov/32086976/ doi:10.3390/f6062148).Introduction: The Introduction section seem too much focused on works carried out in forest areas and application of prescribed burnings, while the approach proposed in this study could be expanded also to semiarid or rural areas, as well as to fuel management strategies different than prescribed fires .______________________________________________________L386-389: Please include the size of the study area, as well as the total size of the modeling domain .The study area corresponds to the size of the modelling domain (approximately 834 km2). The network included both a core area and a buffer area. We have noted the size of the study area in the text.______________________________________________________L399-402: Considering that no information is provided on crown fire and spot fire settings, I suppose the authors applied Burn-P3 model to simulate surface fire spread. In case the authors simulated crown fires and spot fires, I would recommend including more details on this.We’ve added the following text explaining how Burn-P3 simulates fires: Burn-P3 fully implements the crown fire scheme of the Canadian Fire Behaviour Prediction System (FBP), modelling surface fires as well as the transition to crown fires (and the rate of crown fire spread itself). We have also provided a short Burn-P3 summary in the online Supplement S1 along with the key model parameters: The critical weather conditions under which the transition from surface to crown fire occurs are dependent on the fuel type. While spot fires are not discretely modelled within the FBP System, the empirical rate of spread equations are based on wildfire observation data for high-intensity crown fires, thus effectively incorporating the role of spot fires and ember transport into the rate of spread models.______________________________________________________ L408-410: Please include a table, in the Supplementary data, to summarize the main input data used for fire simulations .We have added a short summary and a table summarizing the main inputs for Burn-P3 simulations to online Supplement S1.______________________________________________________L435-439: Problems 1-4 related to the critical nodes detection (CND) were introduced in the first equations, several pages before this part. I would recommend helping readers and clarifying that these problems refer to the first equations and the detection of critical nodes.We have edited the text naming problems 1-3.______________________________________________________L533: Starting a sentence with “Recall that” might be inappropriate, please checkRemoved.______________________________________________________L613-666: The Discussion section summarizes relatively well the principles and generalizations from results as well as the significance of results. On the other hand, it does not discuss the results and methods presented in this work in relation to those of others. This is a limitation, so I recommend improving the Discussion in this sense.We have added text relating the presented method to other planning fuel treatment methods to Discussion section – see our reply to the first comment from the Academic Editor. To avoid repetitions and keep the size of the manuscript reasonable we have compared our approach with the most common methods that use site-based fire hazard metrics.______________________________________________________ Figures 3-4: Please include the scale bar.We have added scale bars to Figures 3 and 4.Attachmentresponse_to_comments_v2.docSubmitted filename: Click here for additional data file. 1 Sep 2021PONE-D-21-15640R1DETECTING CRITICAL NODES IN FOREST LANDSCAPE NETWORKS TO REDUCE WILDFIRE SPREADPLOS ONEDear Dr. Yemshanov,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Reviewer 1 has expressed that the manuscript can be accepted pending some minor revisions. Reviewer 2 was unavailable to review the revision, so I reviewed the authors’ revisions based on the reviewer’s comments and found that all issues have been adequately addressed. Lines 326-328, “In general, long-term planning in fire-prone landscapes has little utility because stochastic wildfire activity may override the long-term treatment plans.” is very poor phrasing and inaccurate. Long term planning is a hallmark of forest management generally and there is good work being undertaken at a range of temporal scales that specifically address stochasticity in fire regimes. There is no need for this baseless claim and it should be removed.plosone@plos.org. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: (No Response)**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?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.The Reviewer #1: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: I am pleased with the revisions. Thank you for clarifying your methods and for explaining where you think the multi-year planning scenario adds value. I still think the method of defining spread probabilities between nodes using the shortest paths from ignition points to perimeters should be framed as an approximation, but this is not central to your work, and the detailed spread paths may not matter much if the “nodes” are large.A few minor writing suggestions:L49-51: I would edit this sentence as: “Our results provide new insights into cost-effective planning to mitigate wildfire risk in forest landscapes. The approach should be applicable to other ecosystems with frequent wildfires.”L64: Drop “in places”?L78: “fuel treatments” instead of “fuel treatment measures”L132: Drop “area”L430-436: Are these sentences necessary after you simplified to an area/node count limit?**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  3 Sep 2021Manuscript PONE-D-21-15640R1 “DETECTING CRITICAL NODES IN FOREST LANDSCAPE NETWORKS TO REDUCE WILDFIRE SPREAD” – A Reply to Reviewers’ Comments:Academic Editor:Lines 326-328, “In general, long-term planning in fire-prone landscapes has little utility because stochastic wildfire activity may override the long-term treatment plans.” is very poor phrasing and inaccurate. Long term planning is a hallmark of forest management generally and there is good work being undertaken at a range of temporal scales that specifically address stochasticity in fire regimes. There is no need for this baseless claim and it should be removed.We deleted this text as suggested by the Editor.__________________________________Reviewer #1: I am pleased with the revisions. Thank you for clarifying your methods and for explaining where you think the multi-year planning scenario adds value. I still think the method of defining spread probabilities between nodes using the shortest paths from ignition points to perimeters should be framed as an approximation, but this is not central to your work, and the detailed spread paths may not matter much if the “nodes” are large.We want to clarify that the calculation of fire spread probabilities pij between pairs of nodes (which were used in our optimization model) did not involve a shortest path approximation. Recall that the spread probability value pij depicts the likelihood that a fire ignited in node i spreads to node j without indication of how  the fire might spread from i to j. Information about the ignition locations came from the geographic coordinates for individual fires simulated by the Burn-P3 model, and then we used the perimeters of the simulated fires to identify the locations j to which a particular fire ignited in i could spread.The only place where we utilized a shortest path approximation was in the calculation of the spread probabilities wij between adjacent nodes for visualizing the fire spread patterns. For each simulated fire, we used the shortest path approximation to project the possible fire spread path (with the probability pij) between locations i and j over the network of arcs connecting the adjacent nodes. This map only served to illustrate the fire spread patterns and was not used in optimization modelling. We have edited two sections of the main text, “Calculating the fire spread probabilities pij” and “Mapping the fire spread probabilities”, as well as Supplement S4 to make this aspect clearer.__________________________________A few minor writing suggestions:L49-51: I would edit this sentence as: “Our results provide new insights into cost-effective planning to mitigate wildfire risk in forest landscapes. The approach should be applicable to other ecosystems with frequent wildfires.”Edited as the reviewer suggested.__________________________________L64: Drop “in places”?Dropped.__________________________________L78: “fuel treatments” instead of “fuel treatment measures”Corrected.__________________________________L132: Drop “area”Dropped.__________________________________L430-436: Are these sentences necessary after you simplified to an area/node count limit?These sentences provide background about why we simplified the budget calculations to an area/node count limit and so should stay in the text. Note that the budget constraint [2] would require a variable cost component if the site treatments were solely managed by ground crews, in which case the total treatment cost would depend on the time required to access the treatment sites (which could be a function of complex terrain and proximity to roads).Attachmentresponse_to_comments_Sept_v2.docSubmitted filename: Click here for additional data file. 17 Sep 2021DETECTING CRITICAL NODES IN FOREST LANDSCAPE NETWORKS TO REDUCE WILDFIRE SPREADPONE-D-21-15640R2Dear Dr. Yemshanov,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.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.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 Kind regards,Paul Pickell, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 21 Sep 2021PONE-D-21-15640R2 DETECTING CRITICAL NODES IN FOREST LANDSCAPE NETWORKS TO REDUCE WILDFIRE SPREAD Dear Dr. Yemshanov:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Paul Pickell Academic EditorPLOS ONE


MSTN) negatively regulates muscle development and positively regulates metabolism through various pathways. Although MSTN function in cattle has been widely studied, the changes in the gut microbiota due to MSTN mutation, which contribute to host health by regulating its metabolism, remain unclear. Here, high-throughput sequencing of the 16S rRNA gene was conducted to analyze the gut microbiota of wild-type (WT) and MSTN mutant (MT) cattle. A total of 925 operational taxonomic units (OTUs) were obtained, which were classified into 11 phyla and 168 genera. Alpha diversity results showed no significant differences between MT and WT cattle. Beta diversity analyses suggested that the microbial composition of WT and MT cattle was different. Three dominant phyla and 21 dominant genera were identified. The most abundant bacterial genus had a significant relationship with the host metabolism. Moreover, various bacteria beneficial for health were found in the intestines of MT cattle. Analysis of the correlation between dominant gut bacteria and serum metabolic factors affected by MSTN mutation indicated that MSTN mutation affected the metabolism mainly by three metabolism-related bacteria, Ruminococcaceae_UCG-013, Clostridium_sensu_stricto_1, and Ruminococcaceae_UCG-010. This study provides further insight into MSTN mutation regulating the host metabolism by gut microbes and provides evidence for the safety of gene-edited animals.Myostatin ( Yellow cattle are a characteristic resource of China, having 52 breeds, of which Qinchuan, Luxi, Nanyang, Jinnan, and Yanbian cattle have been domesticated and bred for thousands of years. They contain rich genetic resources and have rough feeding tolerance, strong stress resistance ability, strong adaptability, and tender meat. However, due to their long-term use as service cattle, common defects in these cattle, such as slow growth rate, underdeveloped rear-drive, slow fattening, weight gain, and low carcass production, cannot meet the requirements of international beef cattle.MSTN), a member of the transforming growth factor β (TGF-β) superfamily, is highly expressed in skeletal muscle tissue and negatively regulates muscle growth _coprostanoligenes_group, Clostridium_sensu_stricto_1, uncultured_bacterium_f_Ruminococcaceae, Ruminococcaceae_UCG-014, uncultured_bacterium_f_Muribaculaceae, Alistipes, Prevotellaceae_UCG-003, uncultured_bacterium_o_Mollicutes_RF39, Ruminococcaceae_UCG-013, dgA-11_gut_group, Bacteroides, uncultured_bacterium_f_Lachnospiraceae, Prevotellaceae_UCG-004, uncultured_bacterium_o_Clostridiales, uncultured_bacterium_f_p-2534-18B5_gut_group, and uminococcaceae_UCG-009  in WT cattle (1.23 ± 0.16%); however, its relative abundance in MT cattle was <1% (0.80 ± 0.05%).The bacterial composition of the samples was analyzed at the genus level. A total of 168 bacterial genera from the six fecal samples were identified, of which 21 showed an average relative abundance above 1%. These were _UCG-009 . The domMSTN mutation affects the gut bacterial communities of the cattle. Substantial differences were observed in gut flora between WT and MT cattle. At the genus level, the presence of Caproiciproducens, Erysipelatoclostridium, Prevotellaceae_Ga6A1_group, uncultured_bacterium_f_Bifidobacteriaceae, uncultured_bacterium_o_Clostridiales, uncultured_bacterium_f_Erysipelotrichaceae, Acetanaerobacterium, Aeriscardovia, Candidatus_Saccharimonas, Bifidobacterium, Sphingomonas, and Rikenellaceae_RC9_gut_group were substantially higher in MT cattle than in WT cattle. However the presence of Ruminococcaceae_UCG-013, Clostridium_sensu_stricto_1, Solibacillus, Lysinibacillus, Ruminococcaceae_UCG-009, Family_XIII_AD3011_group, Paraclostridium, Blautia, Porphyromonas, uncultured_bacterium_f_Christensenellaceae, Terrisporobacter, Pseudoflavonifractor, XBB1006, Paeniclostridium, Ruminococcaceae_UCG-004, [Eubacterium]_nodatum_group, and uncultured_bacterium_o_Rhodospirillales were substantially lower in MT cattle than in WT cattle (p < 0.05).The gut microbiota composition of WT and MT cattle was analyzed to identify whether T cattle  (LDA > 2The functions of the bacteria with significant abundance (>1% in each group) of the MT and WT cattle were predicted using the KEGG database. The primary functions of these bacteria were related to metabolism . SeveralClostridium_sensu_stricto_1 and Ruminococcaceae_UCG-009 (p < 0.05) were significantly positively correlated to AST. Clostridium_sensu_stricto_1 (p < 0.05) showed a significant positive relationship with ALT. Clostridium_sensu_stricto_1, Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG-013, and Ruminococcaceae_UCG-009 (p < 0.05) were significantly correlated to AMY, of which Rikenellaceae_RC9_gut_group was negatively correlated, whereas the others were positively correlated. Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG-009, and Ruminococcaceae_UCG-013 (p < 0.05) were significantly related to LDHL, of which Rikenellaceae_RC9_gut_group was negatively correlated, whereas the others were positively correlated. Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG-013, and Ruminococcaceae_UCG-009 (p < 0.05) were significantly related to LACT, of which Rikenellaceae_RC9_gut_group were negatively correlated, and the others were positively correlated.The relevance between microbial species abundances and serum biochemical indicators was examined to identify marker species linking microbiota and serum biochemical indicators. Spearman’s rank correlation coefficients between abundant microbial species (>1% in each group) and serum biochemical indicators of the MT and WT cattle were determined . ClostriMSTN, a TGF-β superfamily member, functions as a negative regulator during muscle growth in many species [MSTN expression for achieving muscle-improved animals [MSTN knockout animals have developed skeletal muscle development. Continued in-depth study of regulation mechanisms has shown that MSTN regulates the metabolism of the host, including muscle development, fat metabolism [ species –6. There animals –25. To otabolism , glucosetabolism , 9, bonetabolism , and othMSTN mutation was discussed. This study provides a research basis for evaluating the health status of MT cattle intestinal microflora and the effect of MSTN mutation on the gut microbial composition.The bacterial microbiota in the host plays vital roles in the energy and nutrition metabolism, reproduction, and immune homeostasis of the host . ConcernMSTN mutation had no significant influence on the richness and diversity of gut microbiota, which is consistent with the results on the MT pigs [MSTN mutation influenced the gut microbiota.The diversity and richness of the observed OTUs showed no significant difference between the fecal samples of MT and WT cattle, indicating that the  MT pigs . NMDS baMSTN mutation promoted fat metabolism and inhibited its formation as reported before [MSTN mutations can regulate metabolism by regulating changes in the gut flora community.At the phylum level, 11 phyla in the six fecal samples were identified, and Firmicutes, Bacteroidetes, and Tenericutes showed an average relative abundance above 1%. Firmicutes and Bacteroidetes were core microbiota of both MT and WT cattle, which is consistent with the results on MT pigs . Previoud before . In otheRuminococcaceae_UCG-005 [Ruminococcaceae_UCG-014, Ruminococcaceae_UCG-010, Ruminococcaceae_UCG-013, uncultured_bacterium_o_Mollicutes_RF39 [uncultured_bacterium_o_Clostridiales [Ruminococcaceae_UCG-005, Rummeliibacillus [uncultured_bacterium_f_Ruminococcaceae, and Prevotellaceae_UCG-004 [Bacteroides, associated with human diseases, such as colorectal inflammation [Bacteroides are a commonly occurring flora in the living environment of animals [At the genus level, 168 genera in the six fecal samples were identified, of which 21 showed an average relative abundance above 1%. Most of them were related to metabolism by function prediction, of which some were butyrate-producing gut bacteria, such as _UCG-005 , Ruminoctes_RF39 , and uncridiales ; others bacillus , uncultu_UCG-004 . These bammation , were ob animals , indicatCaproiciproducens [Acetanaerobacterium [Sphingomonas [uncultured_bacterium_f_Bifidobacteriaceae [Aeriscardovia [Bifidobacterium [MSTN mutation leads to a decrease in fat content and an increase in the lean meat rate, which enhances the metabolism efficiency and reduces type 2 diabetes risk [Pseudoflavonifractor, a type 2 diabetes-related flora, was decreased in MT cattle, indicating that MSTN can regulate metabolism by regulating intestinal flora. Some flora associated with intestinal inflammation was increased in MT cattle, such as Erysipelatoclostridium [Prevotellaceae_Ga6A1_group [Candidatus_Saccharimonas [Paraclostridium, Porphyromonas, Terrisporobacter, and Paeniclostridium. Therefore, in this study, the effect of MSTN mutation on intestinal inflammation could not be determined. The results in MT pigs showed that MSTN mutation leads to a relative reduction in the inflammatory response [The abundance of some bacteria was significantly higher in MT cattle than those in WT cattle. Most of the abundant bacteria such as roducens , Acetanaacterium , and Sphngomonas  are relaeriaceae , Aeriscacardovia , and Bifacterium , which pacterium , and ourtes risk . In our stridium , PrevoteA1_group , and Canarimonas , whereasresponse ; howeverMSTN mutation was correlated with serum metabolic factors. All serum biochemical factors were related to enhanced metabolism, indicating that MSTN mutation leads to increased metabolism, which was consistent with a previous study [Rikenellaceae_RC9_gut_group was positively correlated with HFD-induced “harmful indicators” and negatively correlated with “beneficial indicators” [Rikenellaceae_RC9_gut_group was negatively correlated with AMY, LDHL, and LACT, suggesting that MSTN mutation was negatively correlated with HFD-induced “harmful indicators” and positively correlated with “beneficial indicators.” In addition, Clostridium_sensu_stricto_1 was positively correlated with AST, ALT, and AMY, Ruminococcaceae_UCG-013 was positively correlated with AMY, LDHL, and LACT, and Ruminococcaceae_UCG-010 was positively correlated with AST, AMY, LDHL, and LACT. The three abovementioned bacteria were all indicators for improving metabolic intensity [MSTN mutation and indicates that MSTN mutation mainly affected the metabolism by regulating these three bacteria.In this study, we found that us study . Previouicators” . In our ntensity , which iMSTN mutation had no remarkable influence on the diversity and richness of the gut microbiota in Luxi cattle. However, MSTN mutation influenced the composition of gut microbiota. The most abundant bacterial genus had a significant relationship with host metabolism. Moreover, the abundance of microorganisms beneficial for the health in the intestine of MT cattle was higher than in the WT cattle. Analysis of the correlation between bacteria and serum metabolic factors affected by MSTN mutation indicated that MSTN mutation affected the metabolism mainly by three metabolism-related bacterial, Ruminococcaceae_UCG-013, Clostridium_sensu_stricto_1, and Ruminococcaceae_UCG-010. The findings of this study provide further insight into MSTN mutation regulating host metabolism via gut microbes and theoretical evidence for the connection between MSTN and gut microbes. In addition, this study demonstrates a novel way to evaluate the safety of gene-edited animals.In this study, high-throughput sequencing of the 16S rRNA gene was performed to analyze fecal samples of WT and MT cattle. S1 Fig(DOCX)Click here for additional data file.S1 Appendix(GZ)Click here for additional data file.S2 Appendix(GZ)Click here for additional data file.S3 Appendix(GZ)Click here for additional data file.S4 Appendix(GZ)Click here for additional data file.S5 Appendix(GZ)Click here for additional data file.S6 Appendix(GZ)Click here for additional data file.S7 Appendix(GZ)Click here for additional data file.S8 Appendix(GZ)Click here for additional data file.S9 Appendix(GZ)Click here for additional data file.S10 Appendix(GZ)Click here for additional data file.S11 Appendix(GZ)Click here for additional data file.S12 Appendix(GZ)Click here for additional data file.S1 Raw images(PDF)Click here for additional data file.


Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system. Quantitative structure–activity relationship (QSAR) models can reduce the time and cost of molecular screening through mathematical prediction models of regression or classification of properties and activities of a chemical compound based on their chemical structure and statistically significant corresponding physicochemical/toxicological properties with other methods such as homology modeling, molecular docking, and molecular dynamics (MD) simulation ,32,33,34receiver operating characteristic area under the curve (ROC_AUC) of the prediction models for 59 MIE targets in validation, test, and foldout datasets indicated 0.818 ± 0.056, 0.803 ± 0.063, and 0.792 ± 0.076, respectively [A DL-based QSAR system, called DeepSnap-DL, was reported to capture molecular features from molecular images photographed on a 3D-chemical structure . In the ectively . Furtherectively .transforming growth factor (TGF)-beta/Smad (PubChem assay AID:1347032_TGF_beta_ant), and thyrotropin-releasing hormone receptor (PubChem assay AID:1347030_TRHR_ago), by optimizing parameters in the DeepSnap-DL system. According to the previously reported MIE molecules, agonist, or antagonist prediction models in the three MIE molecules constructed using the modified DeepSnap-DL with Python showed that it would be essential tools in a novel QSAR system in computer-aided molecular design.In this study, we used the modified DeepSnap-DL with Python and basic DeepSnap-DL with DIGITS systems to construct prediction models in three of MIEs, glucocorticoid receptor (PubChem assay AID:720725_GR_ant), To analyze the influence of different angles on the snapshot generation of DeepSnap_Python and DeepSnap_DIGITS as 256 × 256 pixel PNG files, we used 31 and 23 from 65°, 65°, 65° to 350°, 350°, 350° in Python and from 70°, 70°, 70° to 345°, 345°, 345° in DIGITS of 720725_GR_ant, 15 and 17 from 95°, 95°, 95° to 325°, 325°, 325° in Python and from 95°, 95°, 95° to 355°, 355°, 355° in DIGITS of 1347030_TRHR_ago, 16 and 16 from 75°, 75°, 75° to 350°, 350°, 350° in Python and from 75°, 75°, 75° to 350°, 350° 350° in DIGITS of 1347032_TGF_beta_ant, different angles . AdditioAs results, DeepSnap_Python and DeepSnap_DIGITS in the three MIE targets achieved the following prediction-performance. The mean ROC_AUC, BAC, MCC, and Acc values in the valid dataset were 0.832 ± 0.048 for ROC_AUC_Python in 720725_GR_ant, 0.856 ± 0.029 for ROC_AUC_DIGITS in 720725_GR_ant, 0.875 ± 0.031 for ROC_AUC_Python in 1347030_TRHR_ago, 0.886 ± 0.028 for ROC_AUC_DIGITS in 1347030_TRHR_ago, 0.879 ± 0.015 for ROC_AUC_Python in 1347032_TGF_beta_ant, 0.907 ± 0.020 for ROC_AUC_DIGITS in 1347032_TGF_beta_ant, 0.762 ± 0.044 for BAC_Python in 720725_GR_ant, 0.791 ± 0.023 for BAC_DIGITS in 720725_GR_ant, 0.811 ± 0.032 for BAC_Python in 1347030_TRHR_ago, 0.829 ± 0.023 for BAC_DIGITS in 1347030_TRHR_ago, 0.805 ± 0.015 for BAC_Python in 1347032_TGF_beta_ant, 0.849 ± 0.030 for BAC_DIGITS in 1347032_TGF_beta_ant, 0.248 ± 0.065 for MCC_Python in 720725_GR_ant, 0.282 ± 0.030 for MCC_DIGITS in 720725_GR_ant, 0.141 ± 0.017 for MCC_Python in 1347030_TRHR_ago, 0.155 ± 0.022 for MCC_DIGITS in 1347030_TRHR_ago, 0.309 ± 0.025 for MCC_Python in 1347032_TGF_beta_ant, 0.384 ± 0.044 for MCC_DIGITS in 1347032_TGF_beta_ant, and 0.790 ± 0.058 for Acc_Python in 720725_GR_ant, 0.812 ± 0.044 for Acc_DIGITS in 720725_GR_ant, 0.781 ± 0.030 for Acc_Python in 1347030_TRHR_ago, 0.769 ± 0.060 for Acc_DIGITS in 1347030_TRHR_ago, 0.770 ± 0.029 for Acc_Python in 1347032_TGF_beta_ant, 0.833 ± 0.033 for Acc in 1347032_TGF_beta_ant, respectively .The highThe highest prediction performance values of PR_AUC on the valid dataset for the angles and data-split ratios were 0.660 at 176° and train:valid:test = 7:1:2 in AID:720725_GR_ant, 0.194 at 176° and train:valid:test = 3:1:2 in 1347030_TRHR_ago, and 0.453 at 176° and train:valid:test = 3:1:1 in 1347032_TGF_beta_ant ; Table 3These findings suggested that image augmentation is effectively worked. It has been reported that even though a small number of images was used, the DL can classify by increasing the number of images with the addition of artificial operations, such as movement, rotation, enlargement/reduction, and inversion to the original images ,56. In aHowever, since the image will be similar to the original image, the risk of overfitting, i.e., a decrease in the performance on the test dataset due to the prediction model fitting to match into the training dataset, cannot be ruled ,66,67,68To investigate the effect of hyperparameters in DeepSnap-DL with Python system on prediction-performance values of the three MIE targets, we optimized 39 LRs from 0.004 to 0.0000001 in 720725_GR_ant, 24 LRs from 0.007 to 0.000001 in 1347030_TRHR_ago, and 38 LRs from 0.002 to 0.000001 in 1347032_TGF_beta_ant using the valid dataset . The meaDeepSnap_Python in the three MIE targets achieved the following prediction-performance values of loss, PR_AUC, and F. The mean loss values on the train and valid datasets were 0.215 ± 0.231 for loss_train in 720725_GR_ant and 0.263 ± 0.186 for loss_valid in 720725_GR_ant, 0.098 ± 0.062 for loss_train in AID: 1347030_TRHR_ago and 0.122 ± 0.058 for loss_valid in 1347030_TRHR_ago, 0.125 ± 0.110 for loss_train in 1347032_TGF_beta_ant and 0.236 ± 0.062 for loss_valid in 1347032_TGF_beta_ant , Table 4Furthermore, the lowest prediction-performance values of loss on the train and valid datasets for the LRs were 0.022 at 0.00003 and 0.124 at 0.00003 in 720725_GR_ant, 0.020 at 0.00002 and 0.066 at 0.0008 in 1347030_TRHR_ago, 0.038 at 0.00003 and 0.170 at 0.000021 in 1347032_TGF_beta_ant , Table 4Finally, to investigate the effect of BS in the improved DeepSnap-DL with Python system on prediction-performance values, we optimized 84 BSs from 2 to 300 in 720725_GR_ant, 13 LRs from 2 to 26 in 1347030_TRHR_ago, and 37 LRs from 2 to 80 in 1347032_TGF_beta_ant using the valid dataset . The meaThe highest prediction-performance values of ROC_AUC on the test dataset for BS were 0.983 at 125 in 720725_GR_ant, 0.934 at 14 in 1347030_TRHR_ago, 0.925 at 28 in 1347032_TGF_beta_ant , Table 5Additionally, DeepSnap_Python in the three MIE targets achieved the following prediction-performance values of loss, PR_AUC, and F. The mean loss values on the train and test datasets were 0.045 ± 0.033 for loss_train in 720725_GR_ant and 0.119 ± 0.025 for loss_test in 720725_GR_ant, 0.322 ± 0.013 for loss_train in 1347030_TRHR_ago and 0.314 ± 0.022 for loss_test in 1347030_TRHR_ago, 0.097 ± 0.047 for loss_train in 1347032_TGF_beta_ant and 0.203 ± 0.023 for loss_test in 1347032_TGF_beta_ant , Table 5As a method often used to improve the generalization performance of DL, LR decay, meaning to lower LR in places where learning has progressed to some extent, is known to improve accuracy sharply . HoweverIt was previously reported that BS and LR are proportional, whereas BS and momentum coefficient are inversely proportional . It is cThese findings are expected to lead to drug development from the estimation and identification of new ligands for nuclear receptors.The datasets of three MIE targets, including antagonists of the glucocorticoid receptor (PubChem assay AID:720725_GR_ant), TGF-beta/Smad (PubChem assay AID:1347032_TGF_beta_ant), and agonist of the thyrotropin-releasing hormone receptor (PubChem assay AID:1347030_TRHR_ago) for the chemical structures in SMILES format and the corresponding agonist or antagonist scores defined as Pubchem_activity_scores from the Tox21 10K library in the PubChem database housing quantitative high-throughput assays to identify small molecule agonists and antagonists for MIEs, as previously reported, were downloaded ,52,53,54https://www.mn-am.com/products/corina, accessed on 25 January 2022) was used to determine a suitable form of each chemical structure. The 3D chemical structures of the compounds from SDF files were depicted as 3D ball-and-stick models with different colors corresponding to different atoms by a Jmol, an open-source Java viewer software for 3D molecular modeling of chemical structures [x-, y-, and z-axes saved as 256 × 256-pixel resolution PNG files (RGB) and split into three train, valid, and test datasets, as previously reported [We applied the SMILES format for 3D conformational import to generate the 3D chemical database with rotatable torsion and saved it as a structure data file (SDF) using molecular operating environment (MOE) 2018 scientific applications . Then, the external program, CORINA classic software , were divided into the train, valid, and test datasets. Additionally, the external test dataset is permanently fixed. TensorFlow and Keras on CentOS Linux 7.3.1611 with the CNN GoogLeNet were used all 2D PNG images produced by the DeepSnap-DL-Python system for training and fine-tuning the prediction models. Background colors in the images were changed to the color values in PyMOL, where a force field, which is a set of parameters for the bond lengths, angles, torsional parameters, electrostatic properties, and van der Waals interactions, uses the Merck Molecular Force Field (MMFF) [The improved DeepSnap-DL-Python system used a new 3D conformational import application, called SMILES_TO_SDF, to produce the SDF files from the SMILES format. We used PyMOL, an open-source molecular visualization system written in the Python programming language , to obtain high-quality 3D molecular modeling of chemical structures with 3D ball-and-stick models with different colors corresponding to different atoms. The 3D chemical structures can produce different images depending on the direction. They are captured automatically by DeepSnap as snapshots with user-defined angle increments with respect to the d (MMFF) .x, y, and z axes. The prediction models of the three MIE targets were constructed using these images of the 3D chemicals as input data for the DIGITS-based DL. Another system that is modified DeepSnap-DL by TensorFlow and Keras with Python was used. The SMILES format was used for a new 3D application, called SMILES_TO_SDF, to produce high-quality 3D molecular modeling of the chemical structures saved as a chemical database in SDF format. 2D PNG images produced from the SDF file were produced by DeepSnap, and the prediction models were constructed using these images as input data by DL with TensorFlow and Keras, called DeepSnap-DL-Python.Next, using the structural information for these chemicals derived from the SMILES format, the 3D chemical structure per compound with “rotatable torsions” was depicted using MOE application software program, and optimized to generate a single low energy conformation using CORINA classic software. These 3D chemical structures were saved in SDF format as a database file. Then, molecular images were generated as snapshots of the 3D structure from the SDF file using the DeepSnap method at different angles along the ® Pro. 14 , as previously reported [x-, y-, and z-axes directions for one molecule. Classification performance was evaluated based on a confusion matrix defined by the cutoff value (θ) from the Youden’s Index (YI) as follows [k is the diagnostic categories, wj ∈ .We analyzed the probability of the prediction results using the prediction model with the lowest minimum loss in valid value among 30 examined echoes using the DeepSnap-DL-DIGITS method. We used the medians of each predicted value as representative values for target molecules using statistical analysis software JMPreported ,51,52, b follows ,78,79:YIHowever, the DeepSnap-DL-Python system automatically obtains the probability of prediction results with the lowest minimum loss_valid value among 30 examined epochs, which are the numbers of repeats for one training dataset modulated by early stopping. Additionally, the performance of each model was automatically calculated in terms of the metrics: ROC_AUC, precision recall_AUC (PR_AUC), balanced accuracy (BAC), F, Matthew’s correlation coefficient (MCC), accuracy (Acc), and loss. These performance metrics are defined as follows. Here TP, FN, TN, and FP denote true positive, false negative, true negative, and false positive, respectively.To determine the optimal cutoff point for the definition of TP, FN, TN, and FP, we adopted a method for maximizing the sensitivity (1—specificity), called YI. This index has a value ranging from 0 to 1, where 1 represents the maximum effectiveness, and 0 represents the minimum effectiveness. Additionally, the area under the curve (AUC) for the receiver operating characteristics (ROC) is given byf, j iterates over the true points, Np is the number of true points, T is the number of thresholds, and prect is the precision at threshold t. For broader cases, let prec0 = prec1, and precT = 0 [Here, ROC_AUC denotes AUC recT = 0 . The PR recT = 0 ,54. ThisIn this study, we constructed prediction models for antagonists of the glucocorticoid receptor, TGF-beta/Smad, and agonist of the thyrotropin-releasing hormone receptor using the classic DeepSnap-DL system with DIGITS and improved DeepSnap-DL system with TensorFlow and Keras using the Tox21 10k library. We performed high-throughput and decreased computational costs using the improved DeepSnap-DL system by optimizing the parameters in DeepSnap. Consequently, we obtained that the improved DeepSnap-DL system would be a powerful advanced QSAR system on toxicological and biochemical/cheminformatic fields.