Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENE SIGNATURES WITH PREDICTIVE AND PROGNOSTIC SURVIVAL VALUES IN HUMAN OSTEOSARCOMA
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1. The English of the manuscript is clear.
2. The authors should compare and describe the prognostic gene set for osteosarcoma (X Guan et al., 2020. Cancer Cell Int; Goh TS et al., 2019. J Cell Physiol).
3. The data in the manuscript is proper and raw data is accessible because they used open databases.
Experimental design: 1. The manuscript is included in aims and scope of peerj.
2. The study design is reasonable and sounds clear. For the readers, please add study flow as a figure, it will help to understand the study.
3 and 4. They performed several statistical methods and they described the methods in detail.
Validity of the findings: 1. They developed the prognostic gene set using LASSO. LASSO is one of the most popular and statistically advanced methods. However, they did not compare the previous prognostic gene sets.
2. The statistical methods of the manuscript sound clear.
3. The conclusions and limitations in the manuscript are well stated.
Additional comments: The authors developed and validated prognostic gene set for osteosarcoma using open databases. The manuscript is interesting, but, there are some scientific issues to address.

1. The authors should compare and describe the prognostic gene set for osteosarcoma (X Guan et al., 2020. Cancer Cell Int; Goh TS et al., 2019. J Cell Physiol).

2. They developed the prognostic gene set using LASSO. LASSO is one of the most popular and statistically advanced methods. There are several grouped variable selection methods including LASSO in the coxnet R package (Goh TS et al). In the 'user service' in easysurv (Pak K et al., 2020. JMIR, https://easysurv.net), the authors can perform Elastic net and Net methods after uploading the data. Please perform other grouped variable selection methods and describe them in the manuscript.

3. Please describe the cutoff values in each dataset.

4. Please perform the univariate and multivariate cox regression of risk score as a continuous variable (not categorical variable) in each dataset.

5. (Table 3) Please add the general patients' information and characteristics according to the risk score in the training and validation set.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENE SIGNATURES WITH PREDICTIVE AND PROGNOSTIC SURVIVAL VALUES IN HUMAN OSTEOSARCOMA
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The author did an interesting study, which is of scientific and clinical significance to some extent. The role of gene expression by rna-seq was studied by using some simple but effective methods. I think the author's idea is very clever and commendable, The authors used public data, which is a reasonable use of resources since many high-throughput sequencing data have not been fully developed. By using the bioinformatics, the authors point out a direction of prognostic prediction for OS, which is very instructive for other relevant researchers. The authors not only reveal the key genes in the OS prognostic genes, but also explore the influence of different biomarkers. This is helpful to reveal the molecular mechanism of OS and to develop new drugs. I think it is acceptable for publishing, but there some problems needs to be revised before publication.

In table 1, the author stressed the potential biomarkers for osteosarcoma, this was well summarized. But the meaning for this should be compared with the authors’ signature. Is the authors’ signature more accuracy than these biomarkers or not. The authors should clearly point out the results.

There is no experimental validation in this study, and the author should discuss the limitation of this part at least.

There were a lot of signatures for OS and they should be discussed in this study, for examples Liu et al (PMID: 31146489) developed a pseudogene signature for OS prognosis and the author should explain or discuss the difference or connection of the these studies and clearly cited corresponding manuscript.

How to explain the extremum of HR in fig2a, they are very close to 1 and this may mean they are not so important?

20 genes consisted a signature, that was too complex, I think the author can use step-wise cox regression to simplify the signature, because if 5 genes can get a AUC=0.95, why we need to test 20 genes to get AUC=0.95?.

Please pay attention fig1d and fig4b was not time-dependent ROC curve, it just ROC curve. The authors should clearly recognize these two different analysis. If the authors are interested to time-dependent ROC curve, it would be much better to add this analysis in this study.

The author did a lot of GO analysis, please explain the meaning or usage of them.

The module related to risk-score has a low correlation coefficient and this means these genes may related to risk-score little. I think gsea analysis between high-risk and low-risk group can better illustrated the risk-score related biological process.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENE SIGNATURES WITH PREDICTIVE AND PROGNOSTIC SURVIVAL VALUES IN HUMAN OSTEOSARCOMA
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The authors performed the revision properly.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: HIGH EXPRESSION OF MAPK-14 PROMOTING THE DEATH OF CHONDROCYTES IS AN IMPORTANT SIGNAL OF OSTEOARTHRITIS PROCESS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article is professional with structure figures table and raw data shred. It is relevant to the hypothesis.
Experimental design: 23 osteoarthritis knee joint samples and 22 samples of normal knee joint were obtained  from the microarray omnibus databases. 265 DEGS were identified. MAPK-14, PTPRC, PTPN12 were upregulated while B9D1 was downregulated. Results were confirmed in human OA knee joints for the 4 DEGs. However the conclusion that MAPK-14 is promoting chondrocyte apoptosis was not supported.
Validity of the findings: The findings showed that the microarray data found in the database and the DEGS were verified. However no mechanism or additional new data was presented. It was just a basic confirmation. It is unclear how this will aid int he understanding of OA. Therefore as it stands it is just descriptive suggesting 3 genes are upregulated and 1 is downregulatd during OA.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: HIGH EXPRESSION OF MAPK-14 PROMOTING THE DEATH OF CHONDROCYTES IS AN IMPORTANT SIGNAL OF OSTEOARTHRITIS PROCESS
Review round: 1
Reviewer: 2

Output:
Basic reporting: In the present study, the authors aimed to explore the hub genes in the OA and found that the hub genes MAPK-14, PTPRC, PTPN12 were upregulated while B9D1 was downregulated. Moreover, the inflammatory necrosis of OA chondrocytes was increased compared with the normal chondrocytes.
Experimental design: By bioinformatics, the authors found that the hub genes MAPK-14, PTPRC, PTPN12 were upregulated while B9D1 was downregulated. And qPCR further confirmed the expression of screening differential genes in normal and OA chondrocytes. Compared with normal chondrocytes, OA chondrocytes had high expression of COL I protein and low expression of COL II protein by fluorescence. The inflammatory necrosis of OA chondrocytes was increased compared with the normal chondrocytes by propidiumiodide staining.
Validity of the findings: No comment
Additional comments: The results may provide a novel insight into an signal of osteoarthritis process. However, there are several concerns that should be addressed. It may require major revision before ready for publication.
Major concerns:
(a1)
Overall, there is no evidence provided about the relationship between MAPK14 and the death of chondrocytes. Hence, it would be important to overexpress or interfer MAPK14 in chondrocytes and observe the changes of apoptosis-related markers.
(a2)
It would be more persuasive if the changes of COL I and COL II protein levels in OA and normal chondrocytes are proved by western blot.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: HIGH EXPRESSION OF MAPK-14 PROMOTING THE DEATH OF CHONDROCYTES IS AN IMPORTANT SIGNAL OF OSTEOARTHRITIS PROCESS
Review round: 1
Reviewer: 3

Output:
Basic reporting: A big concern is that there are a lot of grammatical errors and typos in this manuscript. For examples:
- Line 27: ... howerer it is still unclear.
- Line 42: ... the expression levels of B9D1 were significantly lower than that in ...
- Line 56-57: One of the most important risk in the OA development ...
- Line 75: GSE43923 include 3 OA knee joint samples ...
- Line 82: ... users can integrate and analyze multiple datasets for identify DEGs ...
- ...
The authors should re-check and revise carefully. It is better if it is checked by a native speaker or English editing service.

Second concern is the introduction. The authors have not shown clearly the objective/motivation of the work as well as some related literature. Currently, the manuscript includes 2 paragraphs in the introduction without much information to convince that the work is promising and necessary.They should consider rewriting the content of the article in concise language, especially the introduction and method sections, highlighting the key points.

In the abstract, "background" section has not contained much information or key points.
Experimental design: Abbreviation should be defined at the first time use (i.e., OA)

The authors only had few samples in their dataset (23 OA and 22 normal). It is a dataset with very small sample size.

They merged the data from three GEO datasets without considering the inspection and removal of batch effects.

GO database or analysis has been used in previous bioinformatics works such as PMID: 31921391 and PMID: 31277574. Therefore, it is important to refer more works in this description.
Validity of the findings: The concentration of the paper is MAPK-14, but the authors also concluded the findings on COL I, COL II protein, or PTPRC and PTPN12 genes.

The authors should improve the figure's legend to provide more information to readers.

Why did the authors select the confidence score of 0.4 in PPI network?
Additional comments: No comment.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RNA SEQUENCING ANALYSIS OF FGF2-RESPONSIVE TRANSCRIPTOME IN SKIN FIBROBLASTS
Review round: 1
Reviewer: 1

Output:
Basic reporting: Performed RNA-seq to explore the differentially expressed genes of skin fibroblasts in response to FGF2. Will be a good resource.
Experimental design: Good
Validity of the findings: Validation is missing. See comments below.
Additional comments: The manuscript entitled “RNA sequencing analysis of FGF2-responsive transcriptome in skin fibroblasts” is a well written manuscript. The present study performed RNA-seq to explore the differentially expressed genes of skin fibroblasts in response to FGF2. Using bioinformatics analysis, they identified 1475 FGF2-responsive differentially expressed genes, including LOX, TGFB1, 180 PDGFA, COL3A1, COL4A1 and ITGA10. However, they have not validated anyone. Although RNA-seq is a very powerful technique, some technical artifacts may be present in the data. Therefore, Validation with an independent technique is always advisable.
Here are the following major changes to be performed for improving the quality of this manuscript for publication.
1. Validation by qPCR of some of the prominent FGF2 driven differentially expressed genes in skin fibroblasts (like, LOX, TGFB1, 180 PDGFA, COL3A1, COL4A1 and ITGA10).
2. Comparison of the present FGF2 responsive genes set to the know FGF responsive gene data set. Also, a comparison of this data with genes known to be differentially regulated in wound healing could be highlighted.
3. Cellular functions of LncRNAs must be included in the discussion to highlight its possible role in wound healing.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RNA SEQUENCING ANALYSIS OF FGF2-RESPONSIVE TRANSCRIPTOME IN SKIN FIBROBLASTS
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: In this manuscript, Wu et al. seek to characterize the transcriptomic changes induced in fibroblasts by FGF2, with a focus on co-expression networks of mRNA and lncRNAs, and predicted network interactions.

The paper is written fairly well but would benefit with revisions to grammar and clarity.

The authors appropriately introduce fibrotic pathology generally and state, importantly, that there are commonalities amongst different fibrotic etiologies (e.g. the myofibroblast paradigm). The authors should expand more on this concept and better provide more information on fibrosis, how myofibroblasts contribute to fibrosis, the properties of myofibroblasts (including expression of specific genes), etc., which will be important for a reader's interpretation of this manuscript in the greater context of the literature. In addition to the cellular effects of FGF2 discussed by the authors in the introduction, the authors should also talk a little bit about the clinical effects demonstrated by FGF2 (Fiblast and trafermin are its other names) for burn wounds, incisions, skin grafts, etc., which have manifested prominently in the Chinese and Japanese literature in particular. In addition to the Dolivo et al. review in 2017 cited by the authors, see also See Nunes et al 2016 review in this journal titled "Fibroblast growth factors as tissue repair and regeneration therapeutics" for an excellent collection of much of the recent available data that they can incorporate into this manuscript introduction.

The authors should give a little bit of background on FGF2 and FGFs more generally. It's important to know that FGF2 is part of a superfamily of growth factors, that's it's part of a subfamily (FGF1), that is has 5 described isoforms but only one of which is typically secreted, that it acts via FGFR/MAPK pathways canonically, that it activates only certain receptor variants of particular FGFRs (see ornitz et al in JBC 1996).

Where is the FGF2 coming from? Is it purchased? Is it recombinant human FGF2? Was it produced recombinantly in the lab? What is the expression system? What is the purity? etc.

Recent literature (especially with the advent of single cell RNA-seq) has demonstrated extensive transcriptional and operative heterogeneity in fibroblast populations from different patients, from different areas of the skin, from different depths of skin, etc. There are far too many critical papers here to cite them all, but a good place to start would be Shaw and Rognoni's 2020 review article in Current Rheumatology Reports entitled "Dissecting fibroblast heterogeneity in health and fibrotic disease." As such, in order for the data generated in this manuscript to be useful, the authors needs to provide much more information about the patients from which these fibroblast samples were derived. We know that transcriptional differences lead to differences in, for example, receptor expression. Therefore, differences in gene expression may cause very real differences in the effects and magnitude of effects downstream of FGF2 signaling. How old were the patients? Where was the skin taken from? How many donors? etc. were fibroblasts pooled from different donors? This information all needs to be included.

The authors don't actually describe the stimulation with FGF2 in the methods section. Was the stimulation with FGF2 performed in the presence of FBS in culture? (This is fairly atypical and can make a big difference in gene expression changes). Figure 1a shows skin fibroblasts cultured for 48 hours at several indicated doses, but it's not obvious which dose was utilized for the samples harvested for RNA-seq. This information should be in the methods and in the results section and is very important. The authors should also justify the concentration that they used.

Also for the methods section, was DNA enzymatically degraded after Trizol extraction? Were any quality control steps performed? (e.g. RNA quality/degradation cutoff by RIN is common for transcriptomic analyses?)

This is not the first transcriptomic analysis published on FGF2 effects on human fibroblasts. How does this differ from previous analyses? (See Kashpur et al 2013 for example in BMC Genomics). The authors should make a point to focus on expression networks and lncRNAs here.

Picky comment but the authors state "Meanwhile, there were 213 LncRNAs with a fold change >2, among which 80 lncRNAs were up-regulated and 133 were down-regulated." It might be more clear to describe this as 213 lncRNAs with an absolute value(Fold change)>2.

The authors constructed lncRNA--mRNA expression networks. Many readers may not be familiar with this concept and how this prediction can be derived from RNA-seq data. The authors should do a much clearer job describing how this network is constructed, what the data mean, and what kind of predictions can be made from such data. As a researcher, what kind of testable hypotheses can I make from such a network? etc.

In the discussion authors state "Despite advancements in the understanding of the mechanism of skin wound repair, an effective method for accelerating the process remains to be justified." This statement is 1. too general (not always true, for example, as some growth factors can enhance wound healing under certain circumstances; this has been demonstrated in some scenarios in the clinic with FGF2 and PDGFBB), and 2. Not focused on the same fundamental principle as was set out in the introduction, which was focused around the possible uses of FGF2 as an antifibrotic, in keeping with extensive clinical (and preclinical) data. The authors should stick to the story about FGF2 as a negative regulator of fibrosis, as this is where the data are the strongest.

The authors don't give any information about the tissue culture substrate in the methods section, so this information must be included. Authors should acknowledge and discuss the fact that fibroblasts grown on tissue culture plastic or glass are subject to very high stiffnesses, well beyond what they would experience in a physiological situation (particularly in a homeostatic, healthy tissue). Therefore, this must be kept in mind when discussing FGF2-induced transcriptomic changes. Pathologically stiff fibroblasts tend to maintain myofibroblast phenotypes, so effects of FGF2 on in vitro tissue culture-grown fibroblasts may be more representative of the effects of pharmacologic exogenous FGF2 on myofibroblasts (as are the case with antifibrotic therapeutics) compared to the effects of endogenous FGF2 on quiescent fibroblasts in the papillary dermis, for example. It's an important distinction and the authors should acknowledge and address it.

The authors should compare their findings of specific genes being upregulated or downregulated with those of other reports looking at comparable FGF2-induced transcriptomic changes in fibroblasts, in order to lend greater credability to their results (as well as to the results of the papers that they cite!). Quick perusal of the supplementary excel file of DEGs and some simple find commands, and I find that the myofibroblast markers previously reported to be negatively regulated by FGF2 (for example ACTA2, CNN1, TAGLN, etc.) are reported to be downregulated here. That is a good sign! The authors should be more explicit about comparing their data to other published data to the extend that it makes sense/is practical to do so.

The authors should discuss lncs in general more, as well as the specific findings here regarding lncs.

Figure 1a should have scale bars.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RNA SEQUENCING ANALYSIS OF FGF2-RESPONSIVE TRANSCRIPTOME IN SKIN FIBROBLASTS
Review round: 1
Reviewer: 3

Output:
Basic reporting: An important concern is the use of English language is poor and ambiguous. The authors should re-check and revise carefully. Some examples of grammatical errors or typos are as follows (even in the abstract):
- FGF2 plays important roles in the skin wounds.
- A total of 1475 DEGs was identified.
- The current study comprehensive analyzed the FGF2-responsive transcriptional profile ...
- ...

The authors have not mentioned some related works related to bioinformatics analysis in FGF2 in skin fibroblasts and discuss about the differences between previous works and their work.
Experimental design: The authors used R language to perform bioinformatics analysis. It is important if the authors could show the source codes as supplementary information to help reproduce the results.

GO database or analysis has been used in previous works in bioinformatics such as PMID: 31277574 and PMID: 31921391. Therefore, the authors should refer more works in this description.
Validity of the findings: The authors should have some validation cohorts to support the findings.

Why did the authors select the cut-off value of KEGG pathway analysis is 7 pathways?

The authors should add more explanation in the figure's legends, now it did not contain enough information. For example, Fig. 1 is only the top-7 KEGG pathways, not all.
Additional comments: No comment.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RNA SEQUENCING ANALYSIS OF FGF2-RESPONSIVE TRANSCRIPTOME IN SKIN FIBROBLASTS
Review round: 2
Reviewer: 1

Output:
Basic reporting: Good
Experimental design: Good
Validity of the findings: They have validated RNAseq data using qPCR
Additional comments: Authors have incorporated all the suggested changes. Manuscript looks good and is ready to proceed further.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RNA SEQUENCING ANALYSIS OF FGF2-RESPONSIVE TRANSCRIPTOME IN SKIN FIBROBLASTS
Review round: 2
Reviewer: 2

Output:
Basic reporting: None
Experimental design: None
Validity of the findings: None
Additional comments: The manuscript is much improved and I thank the authors for their incorporation of the critiques made by myself and the other reviewers. The manuscript in its current form is very good and the only changes I would recommend are regarding readability.

There still remain some awkward language issues at times. For example, "myofibroblasts are larger in area and have its structural characterization" should rather be "their structural..."., "and the fibronectin isoform" should read "and the ED-A fibronectin isoform" etc.

Minor comments:

The city that proteintech is based out of is called "Rosemont" not "Rosemount".

The authors say that FGF2 activates FGFR1 IIIb, and has low affinity for FGFR2 IIIb and FGFR3 IIIb, but that's not really correct. Check table 3 in the Ornitz JBC 1996 reference. According to this analysis, FGF2 activates FGFR1b, FGFR1c, FGFR2c, FGFR3c, and FGFR4.

The authors say "Importantly, FGF2 was observed to induce a shift in gene
 expression to a more anti-fibrotic signature attenuated the expression of pro-fibrotic genes,
including collagen I, collagen III, α-SMA, and MMP-1(Dolivo et al. 2017b)"
The word "attenuate" means to decrease; Dolivo et al. 2017b (and other papers) demonstrate that FGF2 actually increases expression of MMP1 (which might be expected if FGF2 is antifibrotic, as MMP-1 degrades type I collagen) .


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RNA SEQUENCING ANALYSIS OF FGF2-RESPONSIVE TRANSCRIPTOME IN SKIN FIBROBLASTS
Review round: 2
Reviewer: 3

Output:
Basic reporting: English language still needs to be improved.

In the previous comments, I suggested the authors to publish the R source code, but it has not been included in this version.
Experimental design: No comment
Validity of the findings: No comment
Additional comments: No comment


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 1
Reviewer: 1

Output:
Basic reporting: I review lots of manuscripts with models, but when I meet dimension errors, I cease further reviewing. Unfortunately this applies to the current ms
Experimental design: Acceptable, but not great. A lot of literature exists on the dynamics of soil macro-fauna and the authors only mention that they found little information.
Validity of the findings: NA
Additional comments: General: What I really like in Keylink is the attempt to integrate phsyical, chemical and biological aspects of the soil.
I do think that the result can potentially be valuable for management purposes and assessments.

When I see the term "model" as theoretical biologist, I think of a list motivated assumptions from which a mathematical model follows,
which properties can be studied and realism tested. To be honest, it was a bit of a disappointment to see that the paper is
in fact a description of computer code, and the result even depends on the sequence in which the various steps are calculated.
The many if-then statements make it impossible to study the mathematical properties. So it will be of little help to gain more
(theoretical) insight in soil dynamics, but can, nonetheless, still be usefull in practical applications.
I do understand that the type of mathematical model that had in mind can easily be too simple to match reality close enough,
but the implication is that validation will be very hard, which the authors in fact admit.

Living in a country where N-deposition  is a key issue for soil pH, I was missing how this is incorporated in the model.

What was not really clear to me is the time-scale the authors had in mind in the background of seasonal forcing.
Apart from cycles in temperature and precipitation, leaf litter is of course dominating food availability for soil macro-fauna.
Earthworms easily life for 7 years (depending on temperature); the simulated period 1999-2008 is in fact matching this life span.
My complaint boils down to the notion that the memory of the various player of the game, from bacteria to macro-fauna differs by
order of magnitude, which I do not see reflected in the structure of the model.
The only memory that the model accounts for is the existing pools.

Line 216 introduces growth G, but G is not specified. Previously g_max was introduced, with dim(g_max) = #/time, which follows from
d/dt N = g_max.
From (10) follows that the units of R is g C/d.m^3 and not g C/m^3, like the line above (1) suggest.
In (27), however, g_max has the units of R, which amounts as a dimension error.
In (28) and (29), g_max has the units of CN_eng, and even time disappeared from the units of g_max.
There are more issues with units and dimensions.
In (39), DOM is in g/m^3, f_DOM is a fraction, but respiration R is a rate.

When I notice dimension issue, I cease further reviewing. Sorry for this.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 1
Reviewer: 2

Output:
Basic reporting: As was stated by the authors in their letter to the editor, this is a follow up paper on model tat is described in an unpublished paper. I have no problem with this, more how it's referenced:
(G Deckmyn et al., 2020, unpublished data)
I think it should be:
(G Deckmyn et al., in review)
There are 2 problems with the reference, you have put a year on it, and you have choosen the word data. To me, that indicates that there is data that support a statement, but it hasn't been published yet. But that's not the case here.

In any case, this manuscript is not publishable without the first paper, so I guess that will sort it self.

A part from this, I think (given the circumstances I can not be sure) that the background is sufficent. In all sections but the model description this was the case. And this is maybe something that needs to be changed, even though you have the accompanying paper, there are no references to the logic/science behind using certain formulations. The current setup makes it impossible to follow witout having the other paper.

A comment on the notation CN ratio, I think it should either be C-N ratio or C-N ratio (C:N) the first time, and then C:N throughout the text. And also on the same, C is defined in the Abstract. The Abstract is a stand alone part of the manuscript, any definitions made there have to be redefined in the main text. N is never defined.
Experimental design: no comment
Validity of the findings: no comment
Additional comments: A part from the comments I have made above, I think it is a good manuscript, it was easy to read and how the assumptions that have been made in both model and experimental design affected the results were clear and easy to follow.

Even with accompanying paper, I think some background is needed in the model description part of the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 1
Reviewer: 3

Output:
Basic reporting: 'no comment'
Experimental design: 'no comment'
Validity of the findings: 'no comment'
Additional comments: I read the manuscript by Flores et al. about an ecosystem model (KEYLINK) to integrate soil biological and physical processes to predict soil processes like carbon dynamics. In general, I like the modeling approach, and the details provided by the authors. I, however, have little background on soil hydrology and I am not an ecosystem modeler myself. My background is more soil biology and particularly soil food webs. Authors will realize that when reading some of my concerns below and probability some of my naivety on the topic.

Line 51: remove “shaping both”.
Line 57: perhaps use Carbon (C) for the first time, and then use C throughout the manuscript.
Line 63: soil organic matter (SOM).
Line 64: this sentence reads odd. Do you mean “the relevance of other processes in the physical protection…”
Lines 67: do these models have full name? If so, please provide them before abbreviating!
Line 68: ecosystem engineers.
Line 72: what are detailed soil models? Not clear!
Line 75: I again wonder if there is a full form of KEYLINK? If not, why is it called KEYLINK?
Line 77: such as? Provide some examples what those parameters are in extant models?
Line 87: please use ecosystem engineers throughout the manuscript.
Lines 81-88: were there also different thermal and moisture sensitivity modeled for soil organisms? For example, large body sized soil organisms could be more sensitive to higher temperature.
Lines 89-90: I understand that authors have another paper with more scientific background about their approach. It, however, is unpublished. So as reviewer, I have no access to it to understand the scientific background behind the model. It will be therefore a favor to the readers if authors provide at least with few sentences about the scientific background of the model.
Line 118: which ecosystem engineers authors have in mind here? Earthworms? Adult earthworms? The size differences between adult and juvenile ecosystem engineers may have differences in accessibility to soil pores.
Lines 129-130: are these densities general? Won’t they differ with soil type?
 Line 198: did not get why authors have inverted comma for found?
Line 200: does completely dry pores have no access to a consumer?
Lines 633: not sure why soil bacteria would profit most from a rapidly changing environment. Please clarify.
Lines 651-656: there could be some other small sized ecosystem engineers, such as enchytraeids. Did authors consider “other ecosystem engineers” of the soil than earthworms?
Lines 659-660: which engineers increased? Do engineers then compensate for decay reduction? Also, some ecosystem engineers stimulate bacterial growth. How does that balance out?
Line 666: the loss of predator effects was exclusively owing to large predators or all body sized predators. Please make this clear as this is so far the most interesting model outcome.
Line 687: are authors developing any software to make KEYLINK accessible to ecosystem modelers?


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: Overall, the authors updates really improved the manuscript.

There is only one thing, which I missed in my previous assessment.

"... soil structure as ecosystem engineers are predated...", this is the first mentioning of the term ecosystem engineer.
From ecology, ecosystem engineers are organisms that creates, significantly modifies or maintains an ecosystem.
Sure earthworms are doing that, but so are beavers, sphagnum mosses, kelp and some bacteria.
It would be much better to replace ecosystem engineer with earth worms throughout the document, since that is what you are referring to. But of course acknowledging that earth worms can be considered to be ecosystem engineers.
One reason I am assuming that is because you are allowing pH to directly affect the group, not sure that ants would be as affected by pH as earth worms are.
If not, then it has to be very clear that you in this study how you are defining ecosystem engineers.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 2
Reviewer: 2

Output:
Basic reporting: Discussion needs more flesh.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: I thank Flores et al for revising their manuscript and addressing my concerns in their revised manuscript. The revised manuscript has improved, and it complements well with their other review manuscript (Deckmyn et al.). I have mainly suggestions to improve their discussion a bit. For instance, they have three citations in total in their entire discussion. Authors should discuss their results with a little more context before the manuscript is accepted. For example, there were two recent review papers out highlighting the importance of soil predators in driving soil microbiome (Thakur and Geisen 2019, Trends in Microbiology and Erktan et al. 2020, Soil Biology & Biochemistry), which could easily be integrated in their discussion (lines 668-682). Another minor issue is the way authors claim KELYLINK as “one of the first and most ambitious attempts ….”. I will be careful and tone it down-particularly “the most ambitious attempts”. The authors should leave that to the readers to judge!


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 2
Reviewer: 3

Output:
Basic reporting: Language use throughout the article could use improvement. There are numerous grammatical issues throughout the text and the writing style at some points is less formal than typical for academic articles. I would recommend thorough proofreading of the text for grammar and style. In addition, at some points the text reads less as a scientific description and evaluation of the model and more as an advertisement for others to use the model.

Literature references are adequate, taking into account that the scientific context for the model, including a more extensive literature review, was mostly presented in a separate companion paper. There are a few specific model structural choices that could use more explanation or citation support.
Experimental design: The general structure of the model makes good sense in terms of process representation and the goals of the model. The integration of biological factors and their impacts on soil porosity and structure are interesting and well based in soil science. There are some issues with equations and organization of the model description, but overall I think the model design is sound.

The application of the model and calibration of parameters, however, have a lot of problems and I think the model simulations overall fail to reach accepted technical standards. The model has a large number of pools (13) and a lot of parameters that are poorly constrained by site measurements. Parameter estimation was conducted using a Bayesian Markov Change Monte Carlo approach using only 9 data points, of which several were literature-based estimates rather than site estimates. The study treats these 9 data points (representing a biomass number for each of 9 pools) as 99 points by assuming steady state across years, which I think is unjustifiable pseudo-replication. Overall, I think a Bayesian calibration of a model with 13 C pools using only a total of 9 data points cannot be justified. This is borne out in the results, such as Tables 5 and 6 which show huge ranges in parameter value distributions. In addition, mean pool values (Table 5) are not at all consistent with the data values (Table 2), suggesting that the calibration was unsuccessful. For example, fungal biomass was calibrated to a value of 15 g C m-3 (Table 2) but in model simulations has a mean value of 200 (Table 5).

It does not seem that there is enough data available for the study’s field site to actually constrain the parameters of the model, so it’s hard for me to say what the best path forward might be. I think that for this study to be successful the calibration procedure needs to be completely re-conceived and rewritten in a way that is consistent with the availability of data to constrain the model’s parameters. It is extremely important to evaluate the calibration procedure itself, which clearly failed in the results presented. Careful examination of posterior parameter distributions and the pattern of the MCMC results is important for establishing whether the parameterization procedure actually converged in a useful way.

Perhaps a more theoretical analysis of the model behavior, using reasonable parameter values and focusing on understanding interactions among different pools in more detail, would be one path forward. Overall I think this model is a good example both of the process insights that can be gained by increasing model complexity and an example of the difficulty of getting meaningful results from an increasingly complex model with parameters that are difficult to constrain.

One of the most promising aspects of the model is the connection between faunal engineers and soil structural changes. It seems like a missed opportunity to run the model only at a site with very low faunal engineer biomass, where this effect cannot be investigated fully.
Validity of the findings: The results section of the manuscript is very problematic. The poor design of the model calibration and the complexity of the model itself produced results that are difficult to interpret and inconsistent with reality. The interpretations of the results in the manuscript take an optimistic view that is not consistent with the actual results. Model simulations produced a very wide range of outcomes as evident in Table 5. Biomass time series (Figure 3) look very unstable and are characterized by short-term spikes in biomass of some pools, including huge variability in bacterial biomass, that is not realistic for an ecosystem-scale soil model. Because only the mean of multiple simulations is shown, it is difficult to tell how these time series varied among simulations (within the same scenario) but I expect the variability is very high, calling into question many of the interpretations of the results. It would help to see the variability in simulations behind Fig 3. I would not be surprised if individual simulations within each scenario were extremely variable due to parameter uncertainty (consistent with Table 5 and 6).

The analysis of the results is very short and quite shallow and does not address the key aspects of the different scenarios that were simulated. There is not really a meaningful analysis of the model simulations beyond a cursory, qualitative description. The results and discussion seem to start from the assumption that the model will be useful because of the processes it includes, and do not reflect an actual objective analysis of the simulation results. There is a lot of complexity in the model results which is mostly overlooked. I think to meet scientific standards the results and discussion would need to be completely rewritten to reflect an objective assessment of the model results.

Overall, the results and discussion are more an argument trying to justify the value of the model’s structure than a real analysis of the model outcomes. The analysis of the calibration procedure does not take a serious critical view of the possibility that the Bayesian approach might not give strong constraints on the model parameters. Part of the value of a Bayesian analysis is that it provides estimates of parameter uncertainties, and insights about whether parameters could be well constrained by available data, and it seems that the analysis did not take advantage of this but rather started with the assumption that the model could be well constrained despite the limited measurements that were used.
Additional comments: Line 31: I suggest starting the abstract with a sentence about the scientific context or knowledge gap that motivate the model before jumping into the model description

Lines 34-37: This reads as more of an advertisement for the model than a statement about the science. In this study, KEYLINK was not coupled to another ecosystem model so it does not seem relevant to the study to say how it could be coupled.

Line 50: The model was not actually compared with a first-order model, so there isn’t evidence that it was actually a more successful approach.

Line 59: Century and RothC are not the oldest soil models that exist. It would be more accurate to just say that they are widely used

Line 102: I would start the methodology section with an overview of the whole model and how the pieces relate to each other as shown in Fig 1.

Line 114: If earthworms eat all soil, do they have access to all pore sizes, or only the bacterial pores? It is not clear from the description

Line 127-129: The statement about macroporosity should have a citation to literature supporting it. And this statement does not seem to fit with this section since none of the other pore size classes are described in terms of laboratory measurements.

Line 139: Equation 1 should have a + between the terms in the denominator, not a -

Line 153-155: Is evaporation assumed to be equal to potential evapotranspiration? This does not seem realistic since it ignores source limitation of evaporation as well as boundary layer and conductance effects that limit actual evapotranspiration

Line 199: Equation 9 should include terms for modifiers to gmax (temperature, pH, etc). The sum notation should indicate the index that is being summed over. The role of excreted faeces also needs to be in this equation, otherwise it states that all of the substrate is being converted to biomass growth which is untrue.

Line 205-207: An equation should be shown or referenced for fa

Line 208: Physical recalcitrance is far from a novel concept, and has been included in conceptual and numerical models prior to KEYLINK (e.g., MIMICS, and the passive pool in CENTURY)

Line 218: The model simulates increases in biomass of pools, not in population number

Line 225: It’s confusing to refer to temperature sensitivity in growth when gmax as presented so far does not include temperature dependence (Eq. 9). Adding these factors to Eq. 9 would make this clearer.

Line 235: This equation states that predation rate depends only on the predator’s total growth rate from all substrates, and not on the biomass of the prey. This does not make sense. If a predator feeds on multiple types of prey, then this suggests that all are predated at the same rate regardless of their different biomass amounts. This equation would make sense if it refers to the fraction of a predator’s growth from a single prey type, but that is not what the equation actually shows.

Line 248: availability was f_a in Eq. 9, but is noted as (a) here

Line 254-312: This description would be easier to follow if it was moved to be right after Eq. 9 (which describes gmax), or if there were a statement after Eq. 9 stating that modifiers to gmax were described below. Either way, Eq. 9 should show that gmax is modified by additional factors.

Line 259: Density-dependent microbial turnover should be a modifier on microbial death rate, not on growth rate

Line 271: This equation has a discontinuity in it at T=Tmax, where mT goes directly from 1 to 0. This doesn’t make much biological sense

Line 280-281: The text should reference literature supporting the pH effects on bacteria and fungi

Line 283-284: These equations do not seem correct. gmax would approach infinity as pH approaches the threshold in each case. For example, at pH=8.01, mpH for fungi would be 10. An exponential function would work better. Also, Eq. 18 defines a declining fungal growth at high pH and constant fungal growth at lower pH (< 8), while the text above refers to an increasing fungal growth at low pH.

Line 287: It is not clear why there is a linear response of engineer saprotrophs to pH but a 1/pH response for bacteria and fungi. Is there some literature support for this choice?

Line 291-293: This sentence does not make grammatical sense and needs to be rewritten.

Line 294: mrec should be defined when it is introduced

Line 303: Why was a linear equation chosen here and a power law above?

Line 314: This section is not really about closing the C budget, it is about the fraction of decomposed material that is converted to faeces (which also needs to be included in Eq. 9)

Line 325 and 328: Recalcitrance is not a conserved mass quantity and does not have a budget

Line 335: Based on the text, N limitation should be modifying r rather than gmax

Line 336: This equation states that growth rate is positively correlated with C:N of SOM, the opposite of the statement here that growth slows with lower N

Line 348-349: What specific parameter is “twice as recalcitrant” referring to?

Line 360-361: These units do not make sense.

Line 363-364: Eq. 31 burrow volume as being directly proportional to engineer biomass, in an instantaneous way. It does not make sense to pair this with a turnover rate. If rates are defined, then burrow volume needs to have both a formation and a turnover rate.

Line 369: Should these units be m3/m3?

Line 395: These units are also incorrect

Line 398: Can layer thickness change in the model? Based on other equations, it does not seem like it, so this statement is confusing

Line 423-446: This section does not actually specify how much DOM is leached

Line 472-474: Since KEYLINK has not actually been modified or calibrated for different ecosystems or coupled to any other ecosystem models, this statement is unsupported. I think this whole paragraph (except for the Github link) could be removed since it is mostly an advertisement of the model and not a scientific statement.

Line 480-481: This text should specify why multiple runs are needed (to explore parameter space). generally, I think this would be specific to the uncertainty in model parameters rather than general to the model itself, so I’m not sure it makes sense as a general recommendation.

Lines 506-531: I think these lines could be removed. It is not necessary to describe the basics of how Bayesian parameter estimation works. Previous literature could be cited instead.

Line 524-525: This is the only mention of a drylands version of the model, and seems irrelevant to the rest of the manuscript.

Line 536: Assuming that 9 data points are equivalent to 99 data points by assuming pool values are constant is unjustifiable. I think this is a fatal flaw of the parameterization approach.

Line 550-552: This is not specific to this study and could be removed.

Line 575: R is respiration rate predicted by the model. It is not a parameter. Should this be r?

Line 578: This calibration procedure used 9 data points to constrain 9 model parameters. There is much too little data for this to be a workable approach.

Line 582: There are not 81 data points. There are 9 data points.

Line 584: “no measurements were available” — measurements of what exaclty?

Lines 614-617: Clay, litter recalcitrance, and litter C:N are not included in Table 1

Line 621: What is LHS?

Line 606-624: Was the model calibrated to steady state for each scenario? Or did it use the baseline parameters for all scenarios? If the second is true, then the model was likely out of steady state for other scenarios, making the results unreliable

Line 630-634: Based on Table 5, it looks like the model parameters were not well constrained at all. The +/- values don't make much sense as they include negative biomass for most pools. I imagine the distributions are skewed, so it would make more sense to show a figure with actual distributions rather than report a standard deviation that is not a good measure of actual variance. Showing posterior parameter distributions would provide better information about whether the MCMC approach was actually effective at constraining parameters.

Line 635: What is meant by “relatively uncoupled?” They were poorly correlated with each other?

Line 637-638: The behavior of bacteria in the model is clearly unrealistic, with a huge biomass spike at the beginning of the simulation followed by death of basically the entire bacterial community.

Line 642: “bacteria would profit most from a rapidly changing environment” This doesn’t make much sense either in the model or in real life. Typical soils have large bacterial populations whether they are rapidly changing or not

Line 645: Looking at Figure 3, the opposite of this statement is true. C pools seem very unstable and are characterized by spikes and large fluctuations which suggests to me that the model is poorly balanced. One scenario lost 75% of SOM in the first year!

Line 651: Different soil layers should have the same long-term average temperature, although shallower soil layers would be expected to have wider fluctuations

Line 658-659: The study included six different model scenarios, but only one is discussed and only in one sentence here.

Figure 2: The diagram should show which pools are external to the model (tree shoots, litter, CO2) and which are actually model pools. From the text, it seems like the SOM pools shown (different POM sizes, DOC) are not actually in the model, so it is misleading to show these as model components in the diagram. The diagram should be consistent with the model that was actually used in the study.

The colors of the lines in Figures 3 and 4 are very difficult to tell apart.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: KEYLINK: TOWARDS A MORE INTEGRATIVE SOIL REPRESENTATION FOR INCLUSION IN ECOSYSTEM SCALE MODELS—II: MODEL DESCRIPTION, IMPLEMENTATION AND TESTING
Review round: 3
Reviewer: 1

Output:
Basic reporting: The style of writing in the manuscript has been improved and the issues with clarity and English use have been mostly addressed. Literature references and background provide good context for the modeling study. The article structure and organization are good, and figures and tables are clear and work well.

In terms of raw data sharing: The model code is available on Github and the meteorological drivers are included in supplementary material. However, the code used for Bayesian calibration is not posted, and the model output or code to recreate the actual full set of simulations is not made available. For complete data sharing, I would suggest adding the code used for calibration to the Github repository and posting the model output and/or a script for running the actual simulations that were analyzed in the results.
Experimental design: The design of the model is sound. The calibration procedure is not very robust due to lack of data, as I noted in my previous review. I do think that the revised framing of the calibration procedure and interpretation of the results has mitigated most of these issues. The simulations are now explained more clearly as an initial test of the model, and the results are not interpreted as being quantitatively predictive. I think the Bayesian calibration procedure does make sense when viewed as a strategy for balancing the different model parameters to produce a simulation that is relatively close to steady state for the measured pools, for the purpose of investigating the model’s behavior, and the paper now acknowledges that this calibration procedure should not be viewed as a strong constraint on actual model parameters or quantitative predictions. Overall I think this framing addresses the main issues with the calibration procedure that I brought up in my previous review.
Validity of the findings: I think the manuscript has been improved in terms of the framing of the conclusions and results. In terms of underlying data, as I noted above I would suggest providing scripts and/or model output data for the calibration procedure and the actual simulations that were analyzed in the manuscript.

Overall I think the revised manuscript gives a good overview of the model and demonstration of how it works, including both strengths and weaknesses of the model. There are certainly areas where the model and its application could be improved in the future, but I think the revised manuscript does a better job of acknowledging the weaker areas of this initial model application and sets a baseline for future improvements.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: OVEREXPRESSED PLAU AND ITS POTENTIAL PROGNOSTIC VALUE IN HEAD AND NECK SQUAMOUS CELL CARCINOMA
Review round: 1
Reviewer: 1

Output:
Basic reporting: Line 176. "higher expression of PLAU methylation" is not appropriate. It is not generally used "high (low) expression" for the status of DNA methylation status, but it is used in mRNA level.

Line 249. The authors mention PLAU expression is associated with radio-sensitivity. There is no evidence demonstrated in the study. They should correct the explanation.

Line 251. The limit of this analysis is that it has not been verified by in vitro/in vivo analysis using cell lines and animals, and is a retrospective analysis of the multiple datasets. In addition, there is no mention of the infection status of HPV (alternatively, p16 IHC), which is an important factor in head and neck cancer. Not only the lack of in vitro/in vivo analysis as the limitation of this study, the authors should also mention all the limitation points above in the discussion part.
Experimental design: Figure 1.
HPV (p16) status is an important prognostic factor in H&N cancer. It would be more informative if they could separate the tumor group by HPV (p16 IHC) status.

Figure 2.
E. It would be more informative if they could incorporate the HPV (p16 IHC) status into the multivariate analysis.
F. What was the unit of "expression of PLAU"? The terms "T-without" and "T-with" are vague. More detail clarification is needed.

Figure 3.
B. What does the color code (red, orange, yellow) mean?

Figure 4.
A&B. HPV (p16) status is an important prognostic factor in H&N cancer. It would be more informative if they could separate the tumor group by HPV (p16 IHC) status.
C. What was rationale for the cut-off of PLAU methylation status (high vs. low)?

In addition,it would be more informative if they could assess the correlation between mRNA expression and methylation value of PLAU gene.

Figure 5
It would be more informative if they could assess the correlation between PLAU expression and immune checkpoint molecules (PD-L1, PD-L2, CTLA4, LAG3, TIGIT etc.) in terms of immune related tumor microenvironment.

Table 1.
It is encouraged to incorporate HPV (p16 IHC) status in this clinical parameters as an important clinical factor.
Validity of the findings: Line 256. Conclusions
They mention that "PLAU can be used as a potential diagnosis and prognosis biomarker". However, based on the Cox multivariate analysis for OS [Figure 2E], the increased risk of high PLAU expression is only 18% despite it is significant, while the T-stage and N-stage are more prominent and convenient prognostic factors (52% and 74% increased risk, respectively with substantial significance). How can they emphasize the importance of PLAU as a prognostic biomarker from clinical perspective?
Based on the shown result, PLAU expression should not be used as a prognostic marker for the patient with HNSCC. The authors should reconsider the contents of conclusion.
Additional comments: The authors focused on the expression of the PLAU gene, one of the ECM pathways, and used datasets of multiple head and neck cancer cohorts. The association between PLAU expression in cancerous part, its effect on prognosis, estimation of microenvironment, and methylation status was shown based on data analysis.

Since different analysis tools are employed, it is understandable that the format of the output KM plot is different, but if possible a unified format would be easier for PeerJ readers.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: OVEREXPRESSED PLAU AND ITS POTENTIAL PROGNOSTIC VALUE IN HEAD AND NECK SQUAMOUS CELL CARCINOMA
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1.	The article conducted a comprehensive analysis of PLAU from the perspectives of expression, signaling pathways, methylation, tumor immune microenvironment, survival data, etc., but the content lacks necessary relation. The article describes the GEO database used for the expression level of PLUA, and the methylation level of PLUA uses the UALCAN and DiseaseMeth version 2.0 databases. Is the expression level of PLUA different between two databases?
2.    There are clerical errors in references such as: Wu G et al. 2020.
Experimental design: 1.	The article hopes to prove that PULA is a potential biomarker for the diagnosis and prognosis of HNSCC, but the sensitivity, specificity, positive predictive value, negative predictive value, cut-off value and other indicators of PULA in HNSCC are not discussed, and clinical application is not enough.
Validity of the findings: 1.	This article uses multiple databases (TCGA, GEO, GTEx, STRING, DiseaseMeth version 2.0) and data analysis platforms (UALCAN, GEPIA, Kaplan-Meier survival analysis), data analysis methods (DiseaseMeth, Cytoscape, MethSurv, KEGG, GO, Gene Set Enrichment Analysis), the data source used in the article is more complex, the article does not mention the method of data quality control, how to ensure the reliability of the article results? The lack of consistency in the data used in each part of the article may introduce excessive bias and lead to unreliable results. It is recommended to consider this issue again.
Additional comments: In article, plasminogen activator urokinase (PLAU) is an important factor in extracellular matrix remodeling and metastasis. The article used a variety of data analysis tools to conduct a large number of analyses on multiple databases and obtained statistically different results. The related clinical and experimental data were described and analyzed retrospectively. However, the article has some problems as follows.
1.	This article uses multiple databases (TCGA, GEO, GTEx, STRING, DiseaseMeth version 2.0) and data analysis platforms (UALCAN, GEPIA, Kaplan-Meier survival analysis), data analysis methods (DiseaseMeth, Cytoscape, MethSurv, KEGG, GO, Gene Set Enrichment Analysis), the data source used in the article is more complex, the article does not mention the method of data quality control, how to ensure the reliability of the article results? The lack of consistency in the data used in each part of the article may introduce excessive bias and lead to unreliable results. It is recommended to consider this issue again.
2.	The article conducted a comprehensive analysis of PLAU from the perspectives of expression, signaling pathways, methylation, tumor immune microenvironment, survival data, etc., but the content lacks necessary relation. The article describes the GEO database used for the expression level of PLUA, and the methylation level of PLUA uses the UALCAN and DiseaseMeth version 2.0 databases. Is the expression level of PLUA different between two databases?
3.	The article hopes to prove that PULA is a potential biomarker for the diagnosis and prognosis of HNSCC, but the sensitivity, specificity, positive predictive value, negative predictive value, cut-off value and other indicators of PULA in HNSCC are not discussed, and clinical application is not enough.
4.	What is the basis for PLAU divided into high expression and low expression groups in table1? What is the cut-off value? Are the sample detection methods consistent in the database?
5.	Among the clinical parameters of Table1, only metastasis of lymph nodes is statistically different, suggesting that PLAU may be related to lymph node metastasis. There is no significant statistical difference between tumor T staging and distant metastasis. How to understand the role of PLAU in the diagnosis and prognosis of HNSCC?
6.	The article has a little problem with the statistical processing of data. In Table 1, the tumor staging and cervical lymph node metastasis, some cases are less than 5, which should be tested with continuous-corrected chi-square or Fisher's exact probability.
7.	There are clerical errors in references such as: Wu G et al. 2020.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: OVEREXPRESSED PLAU AND ITS POTENTIAL PROGNOSTIC VALUE IN HEAD AND NECK SQUAMOUS CELL CARCINOMA
Review round: 1
Reviewer: 3

Output:
Basic reporting: An important concern is that there are a lot of grammatical errors and typos in this manuscript. For examples:
- Using the clinical data from The Cancer Genome Atlas (TCGA) databases to explore whether PLAU as a potential prognostic factor in HNSCC.
- The relationship between the expression of PLAU and immune microenvironment were analyzed using stromal score or immune score.
- Moreover, it indicated that PLAU as a potential prognostic factor in HNSCC through ...
- No correlations between of PLAU with gender (P=0.0763) and grade (P=0.9461).
- ...
Therefore, the authors should re-check and revise carefully. I suggest that it needs to be checked by an English editing service.

Literature review is weak. The authors should refer more related works on bioinformatics studies on HNSCC.

What is "reanalysis of the data gathering" in line 76?
Experimental design: - The authors should release source codes for replicating the results.
- National Center for Biotechnology Information (NCBI) has been used to collect data in previously published works in bioinformatics such as PMID: 31319963 and PMID: 31920706. Therefore, the authors should refer more works into this description.
- For this kind of problem, ROC curves and AUC analyses should be conducted.
Validity of the findings: - "Conclusion" section is short and did not contain a lot of information.
- The authors should validate the performance on unseen data.
- The authors should compare the performance with previous works on the same dataset.
Additional comments: No comment


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: OVEREXPRESSED PLAU AND ITS POTENTIAL PROGNOSTIC VALUE IN HEAD AND NECK SQUAMOUS CELL CARCINOMA
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: No comment
Validity of the findings: No comment
Additional comments: My previous comments have been addressed satisfactorily.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TUMOR MICROENVIRONMENT RELATED NOVEL SIGNATURE PREDICT LUNG ADENOCARCINOMA SURVIVAL
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.1 Omission of key prior publications is a weakness of the current manuscript. Additional papers should have been discussed in the existing relevant reportings.
Eg.
[1] Chen G, Dong Z, Wu D, Chen Y. Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression-based retrospective study [published online ahead of print, 2020 Jan 31]. J Cell Biochem. 2020;10.1002/jcb.29667. doi:10.1002/jcb.29667.

1.2 In figure 1 to 8, organization of all charts needs to be rearranged. The charts should be align and sufficient resolution. The content of main text should be corresponding with the results in charts.
Experimental design: 2.1 “Construct and validation the prognostic model” Line 236, “…the final prognostic model combined with 106 candidate gene were constructed…” 106 TME related SurGenes is might the most important innovation point of this research. Thus, Multivariate Cox regression analysis should be further preformed between age, gender, TNM stage and Risk score in this model. The author may refer to this article:
[1] Chen G, Dong Z, Wu D, Chen Y. Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression-based retrospective study [published online ahead of print, 2020 Jan 31]. J Cell Biochem. 2020;10.1002/jcb.29667. doi:10.1002/jcb.29667
Validity of the findings: No comment.
Additional comments: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Tumor microenvironment (TME) cells are important elements in constitutions of tumor tissue. There is increasing evidence that they have important clinical pathological significance in predicting tumor clinical outcomes and therapeutic effects. It is a good idea to investigate the correlation of TME related genes signature with LUAD prognosis, and it might provide new ideas for the diagnosis and treatment of LUAD. In this manuscript, Dr. Chen and colleagues focuesed on the role of the "106 TME related SurGenes" in diagnosis and prognosis of LUAD. The study provided the conclusion that "106 TME related SurGenes" might be used to predict the prognostic survival of LUAD. Although the current study is interesting, there are some major concerns that need to be addressed.

Major concerns:
1. In the newly published article [1], Dr. Chen and colleagues also analysis LUAD dataset from TCGA using ESTIMATE, and the results are similar to this manuscript, so it is necessary to discuss the similarities and differences between the two studies.
[1] Chen G, Dong Z, Wu D, Chen Y. Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression-based retrospective study [published online ahead of print, 2020 Jan 31]. J Cell Biochem. 2020;10.1002/jcb.29667. doi:10.1002/jcb.29667
2. “Data acquisition” Line 99. How to calculate or obtain the immune and stromal scores from ESTIMATE of 517 LUAD samples in TCGA dataset. The detailed method should be provided.
3. “Data acquisition” Line 95. Whether the 442 LUAD samples in GSE68465 were analyzed by ESTIMATE? Please clarify this point.
4. “Construction of the risk assessment model” Line 131 and 139. The “SurFactors” and “Risk socres” are not a common calculation method. Please provide relevant references.
5. "ESTIMATE algorithm of LUAD" Line 156. Please explain the relationship between Estimate score, Immune score and Stromal scores.
6. "Overall survival analysis of DEGs" Line 201. Why did the author to choose the survival curves of CD3G, LAMA2 and ITGAL for analysis subjects? Please explain the meaning in the research.
7. "PPI network construction of prognostic genes" Line 206. What was the  basic rationale for “first three important modules”? Please state clearly and provide references.
8. "PPI network construction of prognostic genes" Line 208. What was the  basic rationale for "hub genes"? Please state clearly and provide references.
9. "Functional enrichment analysis of significant modules" Line 213-219. What is the meaning of this point? Is there any supporting eveidence for this conclusion?
10. "Prognostic genes validation" Line 228. “...there are 47 genes of particular concern because they have not been proved to be related to the prognosis of LUAD.” What is the basis of this statement? Please state clearly and provide references.
11. “Construct and validation the prognostic model” Line 236. “…the final prognostic model combined with 106 candidate gene were constructed…” 106 TME related SurGenes is might the most important innovation point of this research. Thus, Multivariate Cox regression analysis should be preformed between age, gender, TNM stage and Risk score in this model. The author may refer to this article:
[1] Chen G, Dong Z, Wu D, Chen Y. Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression-based retrospective study [published online ahead of print, 2020 Jan 31]. J Cell Biochem. 2020;10.1002/jcb.29667. doi:10.1002/jcb.29667
12. In figure 1 to 8, organization of all charts needs to be rearranged. The charts should be align and sufficient resolution. The content of main text should be corresponding with the results in charts.

Minor comments:
1. P-values should be marked in a uniform way (Italic capitals), and just keep three decimal places.
2. "ESTIMATE algorithm of LUAD" Line 160. Why did not analyze the relationship between Estimate score and prognosis?
3. "ESTIMATE algorithm of LUAD" Line 174. "transcriptional microarray analysis"?
4. "Identification of DEGs" Line 179. How many DEGs were up regulated in both analysis for comparison based on immune scores nor stromal scores? The data should be shown by Wynne chart.
5. "Overall survival analysis of DEGs" Line 197. "...overall survival <30..." What is the unit, month or year?
6. "PPI network construction of prognostic genes" Line 205. What is "prognostic DEGs"? Please state clearly.
7. Figure 1A to C. A scatter plot should show the quartiles.
8. In figure 1D and E, the range of Y-axis (Percent survival) should be the spectrum from 0 to 100, and the unit of survival time should use “months”.
9. Figure 6，The displaying format of the survival curve of gene PRPRC is different from that of other genes, please keep in consistency.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TUMOR MICROENVIRONMENT RELATED NOVEL SIGNATURE PREDICT LUNG ADENOCARCINOMA SURVIVAL
Review round: 1
Reviewer: 2

Output:
Basic reporting: n/a
Experimental design: n/a
Validity of the findings: n/a
Additional comments: In this manuscript titled "Tumor microenvironment related novel signature predict lung adenocarcinoma survival" by Chen et al. have used gene expression datasets of lung adenocarcinoma patients to identify genes linked with patient survival. They also performed  functional annotations to further refine the genesets to generate a prognostic model for survival prediction. Manuscript in the current form is weak and needs additional work to support the hypothesis to further strengthen the manuscript. Following are the issues that need to be addressed.

Major comments:

1. What is the significance of using tumor microenvironment for predicting prognosis?
How does this model differ from other prognostic models for LUAD? A detailed comparison of available prognostic models of LUAD is required with the current model to know the prognostic performance.

2. Authors have shown that immune and stromal scores are predictive of OS. Are they also predictive of progression free survival (PFS)? Fig1 D pls. mention how many samples were used to calculate stats and mean survival stats. TCGA has many cohorts of LUAD (see cbioportal) are these trends similar across various cohorts?

3. How cutoffs for immune and stromal scores were derived? Figure 2 is not informative unless authors highlights the upregulated and downregulated pathways or genes and scale the heatmap. what is the significance of each cluster? Instead of using all DEGS authors should use only highly variable one to make this heatmap meaningful. There is no legend to show the expression range. Expression values should be normalized for better visualization.

4. Its not clear when authors have shown that Estimate stromal and immune scores are predictive of OS, what is the point of using DEG derived from these scores to again perform survival analysis when Estimate scores can also predict? Does a combined DEG score predicts OS better than ESTIMATE Scores?

5. Quality of figures is  low and either they are not labelled well or they are not informative. For eg. what is the significance of PPI maps in Figure5? why these nodes are highly connected?

6. how many DEGS were overlapping b/w immune vs stromal scores?

7. introduction and discussion section of the manuscript is poorly written and lacks clear questions and findings of the paper. Figure 3 should be combined with figure 2 and all the pathway enrichments should be mapped on the heatmap
why string database?

8. fig 1 : how p values were derived
9. define AJCC
10. line 175: its RNA-seq not microarray


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TUMOR MICROENVIRONMENT RELATED NOVEL SIGNATURE PREDICT LUNG ADENOCARCINOMA SURVIVAL
Review round: 1
Reviewer: 3

Output:
Basic reporting: The authors' hypothesis the TME in LUAD indicated by ESTIMATE is associated with patients' survival. However, this manuscript is quite similar to one previous study (Profiles of immune infiltration in lung adenocarcinoma and their clinical significance: A gene‐expression–based retrospective study. Chen et al, doi.org/10.1002/jcb.29667). Although authors performed a pathway annotation analysis on the key genes of TME, I don't think it adds additional significant value in this field.
Experimental design: This experimental design is acceptable but there are a few missing key information.
1. How did the authors get the ESTIMATE score? Did they get the score through the ESTIMATE website or by performing ESTIMATE on LUAD locally?
2. How were the groups determined in the differential gene analysis? What are the groups?
3. In survival analysis, authors can not utilize the student t-test as statistical analysis for the Kaplan-Meier curve and Cox regression. (line 119)
Validity of the findings: More details should be addressed. Here are some examples of blurred statement.
1. In figure 1 (A-C), which groups did the p-value represent for? Did the author perform ANOVA between all groups? Then, a post-hoc test should be considered.
2. In figure 1D, I don't believe there is a statistical significance between these two groups.
3. In Cox regression, authors should present a table listing the Cox coefficients and significance. Authors also should address why they chose CD3G, LAMA2, and IGTAL in figure 4
Additional comments: Although the authors proposed an interesting topic in this field, the authors seem not to provide novel results or insights.
Some details of the design should be provided.
Some English grammar needed to be improved.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TUMOR MICROENVIRONMENT RELATED NOVEL SIGNATURE PREDICT LUNG ADENOCARCINOMA SURVIVAL
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer - related mortality worldwide. Tumor microenvironment (TME) cells are important elements in tumor tissue. There is increasing evidence that they have important clinical pathological significance in predicting tumor clinical outcomes and therapeutic effects. It is a good idea to investigate the correlation of TME related genes signature with LUAD prognosis, and it might provide new ideas for the diagnosis and treatment of LUAD. In this manuscript, Dr. Chen and colleagues looked at the role of the "106 TME related SurGenes" in diagnosis and prognosis of LUAD. The study provides the conclusion, which showed that "106 TME related SurGenes" were expected to be used to predict the prognostic survival of LUAD. The manuscript is improved and most concerned raised by the reviewer have been addressed, but there is still room for improvement in the figures. In order to facilitate the readers and increase the influence of the article, please continue to improve this article.

Minor comments:
1. Figure 1A to C. A scatter plot of population data needs to show the median and quartile values. The “bars” should be black instead of the same color as the “dots”.
2. Figure 1D to E. For the convenience of the reader, the survival curve takes months.
3. Figure 3. For clarity, use vector graphics instead of direct screenshots.
4. Figure 4. Please enlarge the network structure diagram. The text in the picture must be clearly visible.
5. Figure 5C and D. For the convenience of the reader, the survival curve takes months. Mark the sample number of each group.
6. Figure 6. Please enlarge the GSEA results. The text in the picture must be clearly visible.
7. Figure 7. Please enlarge the figures. The text in the picture must be clearly visible.
8. Figure 8A and E. For the convenience of the reader, the survival curve takes months. For the convenience of the reader, the survival curve takes months. Mark the sample number of each group. Please unify the format of all survival curves, including units of time, ordinate amplitude, and markup format!
9. Figure 9. For clarity, use vector graphics instead of direct screenshots.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TUMOR MICROENVIRONMENT RELATED NOVEL SIGNATURE PREDICT LUNG ADENOCARCINOMA SURVIVAL
Review round: 2
Reviewer: 2

Output:
Basic reporting: NA
Experimental design: NA
Validity of the findings: NA
Additional comments: Figure 2: Gene expression heatmap should be z- normalized across patient samples for better representation.

Figure 2 C: WHat is the p value of overlap?

Figure 3: What does color of bar indicates?

Figure 4: These PPI maps are not informative unless the nodes names are marked

Figure 5: A-B It is noyt clear how these pvalue are derived. If it is one pvalue - it should have been derived from ANOVA rather than wilcoxon rank sum test.

Figure 6: Instead of so many enrichment plots which is unreadable, only selected GSEA should be shown.

Figure 7: It is not clear why a correlation value of 0.001 i.e NO correlation is also significant?


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TUMOR MICROENVIRONMENT RELATED NOVEL SIGNATURE PREDICT LUNG ADENOCARCINOMA SURVIVAL
Review round: 3
Reviewer: 1

Output:
Basic reporting: No comment.
Experimental design: No comment.
Validity of the findings: No comment.
Additional comments: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer - related mortality worldwide. Tumor microenvironment (TME) cells are important elements in tumor tissue. There is increasing evidence that they have important clinical pathological significance in predicting tumor clinical outcomes and therapeutic effects. It is a good idea to investigate the correlation of TME related genes signature with LUAD prognosis, and it might provide new ideas for the diagnosis and treatment of LUAD. In this manuscript, Dr. Chen and colleagues looked at the role of the "106 TME related SurGenes" in diagnosis and prognosis of LUAD. The study provides the conclusion, which showed that "106 TME related SurGenes" were expected to be used to predict the prognostic survival of LUAD. The paper is improved and most concerned raised by the reviewer have been addressed. I think it is might suitable for publication at this version of revised manuscript.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: BLOOD MARKERS OF ENDOTHELIAL DYSFUNCTION AND THEIR CORRELATION TO CEREBROVASCULAR REACTIVITY IN PATIENTS WITH CHRONIC HEPATITIS C INFECTION
Review round: 1
Reviewer: 1

Output:
Basic reporting: Well written paper, easy to follow, well structured and to the point. Adequate references.
Experimental design: Chronic hepatitis C is associated with ischaemic stroke. The mechanism may relate to pro-inflammatory cytokine synthesis leading to pro-atherogenic activity. This paper looks to link increased levels of E-selectin; ICAM-1; VCAM-1; VEGF-A (as markers of inflammation) in HCV patients with reduced cerebral vasodilatory response to hypercarbia (as a marker of progressive atherosclerosis).
Validity of the findings: There are a few points to address:
1)	BHI would increase due to progressive carotid stenosis and intracranial atherosclerosis, neither mechanisms yet demonstrated to be more relevant in HCV patients than any other cause of stroke. The relevance of this study findings relies on evidence that atherosclerosis causes most strokes in HCV patients and this may be worth mentioning also in the discussion. Were these patients screened for extracranial carotid stenosis?
2)	This study is likely to have identical findings when comparing 18 chronic smokers with 18 non-smokers. Using smoking as an exclusion criterial for this study may result in more convincing findings. Nevertheless, it would be useful to include more detail about the multiple regression.
3)	It would be prudent to know how Hepatitis C was acquired, or more relevantly if any or many of the 18 HCV group used injectable methamphetamines (also known for increasing the risk of cerebrovascular events)?
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: BLOOD MARKERS OF ENDOTHELIAL DYSFUNCTION AND THEIR CORRELATION TO CEREBROVASCULAR REACTIVITY IN PATIENTS WITH CHRONIC HEPATITIS C INFECTION
Review round: 1
Reviewer: 2

Output:
Basic reporting: Overall an interesting paper with clinical relevance.

Few small comments:

In table 3, E-selectin is spelt wrongly, and all the commas should be full stops.

The sentence in the abstract and results "A significant difference in the BHI was found in the hepatitis group showing lower values of 0.5 (95% CI 0.2, 0.8) on average" is a bit unclear. It should read: "A significant reduction of 0.5 (95% CI 0.2, 0.8) in the mean BHI was found in the hepatitis group (mean BHI 0.64) compared to controls (mean BHI 1.10)"
Experimental design: in line 76 it is stated that this is an interventional and cross-sectional study. However it is not interventional, I would delete this. No changes to patient treatment or medications are made to deem this 'interventional'.

I think this study might be strengthened by reporting the measured non-invasive markers of fibrosis in patient groups, such as the FIB-4 or APRI score. If, as the author state in line 162-169, liver fibrosis may be correlated with changes in endothelial markers, it would be important to quantify differences in such fibrosis scores between groups (even in the absence of frank F4 cirrhosis). It would be important to include these fibrosis scores in regression analyses to ensure that fibrosis is not mediating the differences in endothelial markers between groups.

If the authors don't have this fibrosis score, this is something that should be explicitly stated in the limitations of the study.
Validity of the findings: The discussion section should include a limitations section.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GEOGRAPHIC POTENTIAL OF THE WORLD’S LARGEST HORNET, VESPA MANDARINIA SMITH (HYMENOPTERA: VESPIDAE), WORLDWIDE AND PARTICULARLY IN NORTH AMERICA
Review round: 1
Reviewer: 1

Output:
Basic reporting: No comment
Experimental design: The experimental design is very comprehensive: the authors experiment on several aspects of model building and correctly document them. Besides, they are very thorough in the modeling analyses, clearly stating and applying concepts that often times are missed by niche modelers. I have only some small to medium concerns that I express here:

1) Whereas the main objective of the study is clearly defined (i.e., simulating the invasion potential of the AGH hornet in NA), the authors also have other interesting methodological objectives that are not stated as clearly (i.e., assessing the effect of particular modeling decisions/approaches in model variability and uncertainty). You can infer this through the reading, but since it is not explicitly stated, the reader might have difficulties with the flow of the manuscript, having the main objective being constantly "hindered by these methodological details". I totally agree with the authors on the importance of these aspects, showing their effects, and having the study be more than a case study of invasive potential. However, I think they will have much to gain if they state these tangential objectives clearly since the beginning, rephrasing/structuring some of the subheaders so that the reader can clearly follow the main findings.

2) My only medium concern regarding experimental design is with the PCA, which the authors undertake to characterize environmental variability within fewer dimensions. However, this PCA is done within a restricted area and then extrapolated to the whole world. To my understanding, one has to very careful when extrapolating PCA's. If the interest was to reduce dimensionality to the few variables that characterize most of the environmental variation both in the training and testing area of the model, the PCA should have been done considering at least these two areas (if not the whole world, although that entails additional caveats). This is because these areas can differ in the collinearity shown by the variables (which is likely, given the differing longitudes and biomes they represent, and the authors actually show with their MOP analyses). If the PCA is done only within the training environmental data, and then those PCs are interpreted within the testing area, whatever collinearity structure that characterized those PCs might change (or not even be present) in the testing area. Hence the need for summarizing/characterizing environmental variability simultaneously for both training and testing areas.

However, given that the authors also present models using another framework besides the PCs, I don't consider this issue to be a major drawback for the study. Redoing the analyses and assessing whether the general findings hold could be fruitful, but it might be too much to ask at this stage of the manuscript.

What I consider that the authors should do is:
a) justify why they did this analysis as such (there are probably other caveats involved when doing a more expansive PCA like the one I suggest).
b) Clearly state this potential caveat and its potential effects in their findings.

3) One lesser thing that the authors should also mention regarding their design pertains to the particular scheme they used to divide occurrences in training and testing subsets (i.e. random partitioning). Theory and practice agree that, lacking truly independent data for model evaluation, the best next thing is spatially structured cross validation. This is because structuring the data this way reduces the probability of the datasets sharing the same biases, as well as reducing the spatial autocorrelation between training and testing datasets. If training and testing datasets are spatially autocorrelated, it is an easy task for the model to predict the testing dataset, becoming a lenient evaluation (and failing to detect any biases both datasets share). Random partitioning usually results in training and testing datasets sharing the same biases because of their spatial proximity, resulting in models that are either fit to biases or overfit to whatever nuances are present in the datasets. Hence, spatially independent evaluation results in models that are less biased and more general (less overfit to the particularities of the dataset), which is especially desired when models are to be transfered to other places, as was the main objective in this study (see Veloz 2009; Muscarella et al. 2014; Radosavljevic & Anderson 2014; Wenger & Olden 2014; Soley-Guardia et al. 2019).

Luckily, here it seems that the occurrences lay far from one another (which the authors improved by filtering). This likely reduced or eliminated this issue, but I consider that the authors would do well in pointing to how their approach reduced the possibility of obtaining inflated evaluation metrics (for justifying their findings but especially to avoid encouraging the continued usage of a method that has usually been criticized under general circumstances). For instance, many novel users do not even filter their datasets, and then random partitioning results in heavily correlated clusters of training and testing datasets.
Validity of the findings: I salute the authors clear documentation of their procedure and results, which allows for the reproducibility of the study (something often times neglected in modeling studies).

Currently, I think that the validity of the findings lose a bit of momentum because a stronger justification is needed for assessing this invasion potential. Currently, there is no explicit mention of the invasion of this hornet in other parts of the world or its effects (at least not in terms of invasion frequency, establishment rate, or the implications for the environment and the economy). If this information does not exist or such invasions haven´t even occurred or resulted in major impacts, the authors should then tone down their discussion a little bit. In no way are the authors being alarmistic or catastrophic, but they do point out to the importance of monitoring this invasive species in several occasions. To me, this current emphasis lacks justification in the writing, and that is why I suggest that authors "match things up" (either by including additional justification information or being careful in that these statements do not come across as overly stressed).
Additional comments: This is a well-written, interesting, and neatly carried study that uses niche models to address an important ecological question while at the same time studying important modeling aspects. I provide general comments here and detailed ones throughout the manuscript (including tables and figures) that I hope help the authors improve it. I look forward to eventually reading a published version.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GEOGRAPHIC POTENTIAL OF THE WORLD’S LARGEST HORNET, VESPA MANDARINIA SMITH (HYMENOPTERA: VESPIDAE), WORLDWIDE AND PARTICULARLY IN NORTH AMERICA
Review round: 1
Reviewer: 2

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: I have carefully read the manuscript entitled "Geographic potential of the world's largest hornet, Vespa mandarinia Smith (Hymenoptera: Vespidae), worldwide and particularly in North America" and I enjoyed reading the manuscript.