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The Reliability of the test is calculated using The Spearman–Brown prediction formula, also known as the Spearman–Brown prophecy formula The method was published independently by Spearmanand Brown (1910). The user can also specify the number of ratings that will be averaged to obtain a score. If more than one rating is specified, the program uses the Spearman-Brown prophesy formula to extrapolate what the reliability would be if that number of ratings was averaged to obtain a score.
S1 Table: Analysis methods with references and corresponding output variables per center. Category: 1 = TFA-like methods, 2 = ARI-like methods, 3 = correlation-like methods, Method group: 1 = TFA, 2 = Laguerre expansions, 3 = Wavelets, 4 = IR-filter, 5 = ARX, 6 = ARI, 7 = ARMA-ARI/ARX, 9 = IR-filter, 10 = correlation coefficient; VLF: very low frequency; LF: low frequency; BP: blood pressure; FFT: fast Fourier transform; ARI: autoregulation index; ARX: autoregressive model with exogenous input; Centre names are listed in.(DOCX). S3 Fig: ICC values for phase LF and phase VLF. Beeswarm letter-boxplot with ICC values for Phase LF (upper figure) and Phase VLF (lower figure) for different cut-off levels of PSD-MABP.
Each analysis method is represented by a letter.!:indicates cut-off level at which significant differences between the methods were found. Post hoc sum-scores for each cut-off level with significant differences between the methods are indicated in the legend, from left to right. For each method, a significant positive ICC difference with another method is scored as +1, no difference as 0, and a negative difference as -1. The sum of all the scores in the post-hoc sum-score. Negative ICC values do not appear in this plot.(DOCX). S4 Fig: ICC values for ARI and correlation like indices.
Beeswarm letter-boxplot with ICC values for ARI and correlation like indices for different cut-off levels of PSD-MABP. Each analysis method is represented by a letter.!:indicates cut-off level at which significant differences between the methods were found.
Post hoc sum-scores for each cut-off level with significant differences between the methods are indicated in the legend, from left to right. For each method, a significant positive ICC difference with another method is scored as +1, no difference as 0, and a negative difference as -1. The sum of all the scores in the post-hoc sum-score. Negative ICC values do not appear in this plot. H and I indicate correlation like indices, which were not included in calculation of the boxplot or in the statistical analysis.(DOCX). We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2 nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects.
DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased.
IntroductionDynamic cerebral autoregulation (DCA) is a key mechanism in cerebral homeostasis and protects the brain from alterations in blood pressure by arteriolar vasodilatation or constriction,. Through this process, cerebral blood flow is preserved at a relatively constant level. Abnormal DCA status has been found in many pathological conditions such as stroke and traumatic brain injury,. Despite detectable differences between healthy controls and patient groups, high variability is present which reduces the reliability of individual estimates.
Distributions of patient and healthy subject groups overlap considerably which results in poor diagnostic properties. This hampers implementation of DCA measurements as a diagnostic or monitoring tool in clinical practice.The causes of variability in DCA measurements can be categorized as physiological or methodological.
Physiological causes of DCA variability include the influence of confounders such as PaCO 2, autonomic nerve activity, and the amount of blood pressure variability,. Non stationarity of cerebral autoregulation suggests that DCA activity is not always constant over time, and can also be thought of as physiological variability,. Non stationarity may be linked to variations in blood pressure variability or absolute blood pressure; if no change is present in blood pressure, no change in arteriolar diameter is needed to keep cerebral blood flow constant. Methodological causes of variability include measurement conditions and analytical techniques used to estimate DCA metrics. The influence of measurement conditions has been demonstrated previously: time of day, use of caffeine, amount of rest before measurement, body position, and external influences are all important to consider.
Recommendations for optimal settings of these components have been published in the 1 st CARNet bootstrap initiative. Many different analysis strategies for DCA assessment have been developed, some of which try to model and account for physiological causes of variability, such as PaCO 2. For example, Transfer Function Analysis (TFA) based on multiple inputs (MABP, CO 2) allows for correction of CO 2 influences, while Volterra Kernels, or Multi-model Pressure Flow Modeling, try to capture the non-linear aspects of cerebral autoregulation. Time varying autoregressive moving average models (ARMA models) and multiple-input finite impulse response models have been used to model the non-stationary aspects of cerebral autoregulation,.
However, at present it is not clear if any of these methods is superior to others in terms of variability and reproducibility of the DCA estimates.The second CARNet bootstrap initiative was started in 2015 to gain further insight into the problem of variability of DCA estimates. Specifically, reproducibility was assessed to quantify variability between repeated measurements. The main focus of this study was to evaluate any differences in reproducibility between the different analysis strategies that have been developed. Initially, surrogate data were used to isolate the separate effects of modelling method from physiological variability. In the second analysis, physiological data were used to quantify reproducibility of several DCA analysis methods on physiological data. The third analysis is described in this study, focusing on the effect of blood pressure variability, quantified as power spectral density of mean arterial blood pressure (PSD-MABP), on DCA reproducibility.
Some studies have shown that DCA parameters are more stable if PSD-MABP is high, and can become variable if PSD-MABP is low,. A number of techniques to increase PSD-MABP variability have been described, and in some of these studies reproducibility of the DCA estimates have been shown to improve,.The specific hypothesis that were tested are:.DCA Reproducibility will increase if subjects with low PSD-MABP are left out of the analysis.DCA Reproducibility after PSD-MABP based case removal is similar for different analysis methods.DCA Reproducibility after PSD-MABP based case removal is similar for different DCA parameters. Subjects and centersand Tables summarize the participating centers, their roles and the analysis methods. Measurements from 75 subjects were collected from 6 participating centers. A formal sample size calculation was not possible before the study was performed, due to the lack of information about inter-subject variability for the majority of autoregulation indices.
In a previous study, Brodie et al. Provided estimates for the sample size in studies using the ARI index, but similar information was not available for the other indices in our study. From the information provided, with n = 75 subjects in our case, we can estimate that a difference of ARI = 0.77 could be detected with 80% power at α = 0.05, which should be very satisfactory to detect intra-subject differences.Subjects were 18 years or older, male or female, and were in good health. Exclusion criteria included a history of uncontrolled hypertension, smoking, diabetes, irregular heart rhythm, cardiovascular disease, TIA/stroke or significant pulmonary disease. The study has been carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all subjects. A study flowchart is presented in.
MeasurementsMeasurement data had to consist of transcranial Doppler ultrasound (TCD) derived cerebral blood flow velocity in the middle cerebral artery (CBFV) data and MABP as well as end tidal CO 2 (EtCO2) data. TCD recordings could be unilateral or bilateral. Beat to beat data had to be resampled at 10 Hz. For each subject, 2 periods of at least 5 minutes of continuous, good quality, artifact free data were required. The interval between the measurements could not exceed 3 months. Subjects were measured supine at rest.
No MABP oscillation inducing maneuvers were performed. Pre-analysis data validationData validation was performed using the following criteria:Mean CBFV 30 cm/s, EtCO 2 3 kPA and 40. Analysis methodssummarizes the analysis methods that were used by each center. The full details of all analysis methods will not be presented here, instead references for each method are provided.
Each method was categorized into one of three possible broad categories of methods: 1. Transfer function like methods: those that produce at least some form of phase and/or gain estimates. Autoregulation index like methods: Those that produce an autoregulation index which ranges from 0–9. Correlation index like methods: Those that produce a correlation like index. These categories were created from the perspective of similar output parameters, not because the analysis is similar on mathematical grounds. PSD-MABP based case removalPower spectra were created by calculating power spectral density (PSD) of the mean arterial blood pressure (MABP) signals. This was not part of the analysis of the individual centers, instead it was performed centrally.
A Hanning window was applied, and segments of 100 seconds with 50% overlap were used, according to the Welch method of spectral estimation. Mean PSD-MABP estimates were calculated in VLF (0.02–0.07 Hz) and LF (0.07–0.2 Hz). Histograms of the PSD-MABP estimates were created and 10 cut-off levels were defined, which reflected the 0, 10, 20, 30, 40, 50, 60, 70, 80 and 90 th percentiles of the PSD-MABP distribution. Each cut-off level was used to remove cases with PSD-MABP levels below these values.
The same cut-off levels were used for all analysis methods, which results in the same number of cases for each analysis method for each cut-off level. For DCA variables, cases were removed based on the corresponding frequency band PSD-MABP (DCA-VLF vs PSD-MABP-VLF and DCA-LF vs PSD-MABP-LF) but for DCA variables in the LF band cases were also removed using PSD-MABP in the VLF.
Statistical analysisTo evaluate the possible impact of PSD-MABP levels on DCA variables, the relation between the absolute difference of the duplicate DCA variable measurements and the lowest PSD value of the PSD-MABP signal of both measurements was analyzed using Spearman rank correlation for each method. We used the absolute difference for this analysis, since it is irrelevant which of the two measurement has the higher or lower value of the DCA variable.
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The lowest PSD value was used, since this will assure that when using PSD-MABP as a filter variable based on threshold levels, both PSD-MABP values will be above the threshold. To investigate main effects, the Spearman correlation coefficients were used as a summary measure and were tested against the null hypothesis value of zero using a one sample T test.To assess reproducibility, one way Intraclass Correlation Coefficient (ICC) analysis was performed on all available data, and for each subgroup created through case removal based on PSD-MABP. The one way setting in SPSS, version 22, was used to calculate the ICC. To meet the prerequisite of bivariate normal distributions, data were transformed using Box-Cox transformations. Within one analysis method, the same transformation was applied to both the first and second measurement, but different transformations could be used for different methods and DCA variables.
To quantify the effect of case removal based on PSD-MABP on ICC values for different DCA variables within the main method groups, a repeated measurements ANOVA was used, entering the ICC values at the 10 PSD levels as repeated measurements and DCA variable type as the group variable. Gain VLF, Gain LF, Phase VLF, Phase LF and ARI were the possible values for the group variable. Post Hoc tests were performed to assess differences between the DCA variable groups.To test for differences in ICC values between different analysis methods, Monte Carlo simulations were conducted using correlated random data with an inhouse written script in LabVIEW 2014. The details of the Monte Carlo simulations can be found in.A final analysis consisted of extrapolating the ICC result to a longer measurement period. The Spearman-Brown prophecy formula was used to predict the length of time needed for a DCA variable to reach a good ICC value of at least 0.6. The estimated longer durations involve the assumption that autoregulation would remain stable when the measurement duration is extended.
The Spearman-Brown calculated ICC (ICC SB) is calculated as:ICC SB = n I C C 1 + ( n − 1 ) I C C where ICC is the ICC value based on 2 measurements of 5 minutes and n is the factor by which the measurement duration will be extended. Subject characteristicsThirty-three subjects (44%) were female and 42 were male. Mean age was 47.8 ± 18.6 years (range 20–80). Five subjects had a history of asymptomatic hypertension, which was well controlled by medication at the time of measurement. Median lowest PSD-MABP was comparable between those that used vasoactive medication and those without any medication Four older subjects had a history of mild cognitive impairment, but not dementia. Another 4 subjects used NSAIDs regularly, without any history of vascular disease. Correlation between DCA variables and PSD-MABPshows the correlation between the absolute difference of DCA variables between the first and second measurement and the lowest PSD-MABP value of both measurements.
High absolute differences tend to occur at low PSD-MABP values, especially for gain variables, and to a lesser degree also for phase variables. For ARI and correlation like variables, this dependency is less clear or absent. Results of the Spearman rank correlation analysis can be found in. For individual methods, differences in gain in the VLF had a strong relation to the PSD-MABP estimates; for other variables, this relationship was less strong.
When all Spearman correlation coefficients were used as summary measures, mean gain (VLF: -0.25 ± 0.1, p. Reproducibility: ICC analysis for incremental case removalFigs –, and – Figs summarize the main findings of the ICC analysis for different levels of PSD-MABP based case removal. For gain, phase and ARI, mild to moderate increases in ICC can be seen, while for correlation like indices, no clear change in ICC is present. There was a significant linear increase in ICC with increasing numbers of cases removed based on PSD-MABP for gain (VLF: F = 34.3 p. ICC values for ARI and correlation like indices.Beeswarm boxplot with ICC values for ARI and correlation like indices for different cut-off levels of PSD-MABP. Each grey dot represents an analysis method.!: indicates cut-off level at which significant differences between the methods were found.
indicates correlation like indices, which were not included in calculation of the boxplot. The increase in ICC with increasing cut-off levels was significant for ARI like indices, when the highest cut-off level (110) was excluded. ICC values for phase LF and phase VLF.Beeswarm boxplot with ICC values for phase LF (upper figure) and phase VLF (lower figure) for different cut-off levels of PSD-MABP. Each grey dot represents an analysis method.!: indicates cut-off level at which significant differences in ICC values between the methods were found. The increase in ICC with increasing cut-off levels was significant for both phase LF and phase VLF.Within each main method group, Monte Carlo simulations indicated significant differences between individual methods for phase VLF, phase LF and ARI, but not for gain VLF and LF. – Figs show beeswarm letter-box graphs with post-hoc sum-scores for each individual method.
For phase VLF, methods 14.1 and 14.2 (, both ARX (autoregression) models with 1 and 2 inputs (MABP, MABP and CO2) respectively) were the methods that showed the clearest increase in ICC, with ICC values compatible with moderate to good reproducibility. For phase LF, multiple methods had ICC values in the moderate range, while some had very low ICC values. For ARI, method 11.5 (impulse response filter coefficient) had the highest reproducibility, with ICC values showing good reproducibility at low PSD-MABP cut-off levels.
Results of the Spearman-Brown analysis.Spearman-Brown predicted ICC values based on single measures ICC values calculated on 2 measurement periods of 5 minutes. The assumption is that autoregulation would remain stable when the measurement duration is extended.
The observed median ICC values for DCA variables based on all cases (n = 75) are projected onto the predicted ICC = 0.6 line. The first intersecting curve on the left represents the recording time that is needed to achieve an ICC of at least 0.6.
To achieve an ICC value of ≥ 0.6 the recording length must be increased to 10 minutes for median phase LF, 15 minutes for gain VLF, 20 minutes for ARI and 35 minutes for phase VLF. For gain LF, 5 minutes is sufficient.
DiscussionIn this study, the effect of PSD-MABP on DCA reproducibility was investigated. In the first step, a dependency of reproducibility on PSD-MABP was established by correlating the absolute within subject difference of DCA variable values with the lowest PSD-MABP value of the two measurements. Significant correlations were exclusively found for TFA like DCA variables, with the highest correlations for gain. For ARI and correlation like methods, no significant correlations were found. This means that the effect of any case removal based on PSD-MABP levels on reproducibility can be expected to be higher in TFA like methods. However, absence of significant correlations does not mean that there can be no benefit in reproducibility following case removals: if only a few outliers exist at low PSD-MABP power, there may not be a significant correlation, but reproducibility statistics such as ICC values can still be significantly affected, as they are quite sensitive to even a few outliers.
In line with this reasoning, increases in ICC after incremental case removal were most pronounced in DCA variables whose difference values were significantly correlated to PSD-MABP (gain and phase), but were also found in ARI like methods. For most DCA variables, case removal based on PSD-MABP within the corresponding frequency band yielded the highest increases in ICC, except for gain LF where case removal based on PSD-MAPB-VLF (instead of PSD-MABP-LF) resulted in the highest increases in ICC. S1 Table Analysis methods with references and corresponding output variables per center.Category: 1 = TFA-like methods, 2 = ARI-like methods, 3 = correlation-like methods, Method group: 1 = TFA, 2 = Laguerre expansions, 3 = Wavelets, 4 = IR-filter, 5 = ARX, 6 = ARI, 7 = ARMA-ARI/ARX, 9 = IR-filter, 10 = correlation coefficient; VLF: very low frequency; LF: low frequency; BP: blood pressure; FFT: fast Fourier transform; ARI: autoregulation index; ARX: autoregressive model with exogenous input; Centre names are listed in.(DOCX).
S3 Fig ICC values for phase LF and phase VLF.Beeswarm letter-boxplot with ICC values for Phase LF (upper figure) and Phase VLF (lower figure) for different cut-off levels of PSD-MABP. Each analysis method is represented by a letter.!:indicates cut-off level at which significant differences between the methods were found.
Post hoc sum-scores for each cut-off level with significant differences between the methods are indicated in the legend, from left to right. For each method, a significant positive ICC difference with another method is scored as +1, no difference as 0, and a negative difference as -1. The sum of all the scores in the post-hoc sum-score. Negative ICC values do not appear in this plot.(DOCX). S4 Fig ICC values for ARI and correlation like indices.Beeswarm letter-boxplot with ICC values for ARI and correlation like indices for different cut-off levels of PSD-MABP. Each analysis method is represented by a letter.!:indicates cut-off level at which significant differences between the methods were found.
Post hoc sum-scores for each cut-off level with significant differences between the methods are indicated in the legend, from left to right. For each method, a significant positive ICC difference with another method is scored as +1, no difference as 0, and a negative difference as -1. The sum of all the scores in the post-hoc sum-score. Negative ICC values do not appear in this plot. H and I indicate correlation like indices, which were not included in calculation of the boxplot or in the statistical analysis.(DOCX).
7 Nov 2019PONE-D-19-25218Assessment of dynamic cerebral autoregulation in humans: is reproducibility dependent on blood pressure variability?PLOS ONEDear Dr. Elting,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Three experts raised independent concerns. In the revision all the concerns should be clarified.
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The conclusions must be drawn appropriately based on the data presented.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.2. Has the statistical analysis been performed appropriately and rigorously?Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.3. Have the authors made all data underlying the findings in their manuscript fully available?The requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file).
The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available.
If there are restrictions on publicly sharing data—e.g. Participant privacy or use of data from a third party—those must be specified.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.4.
Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)Reviewer #1: This manuscript provides us the novel clinical implications in the assessment of cerebral circulation.
The methods were reasonable, and discussion is quite scientific.To strength the manuscript I would like to ask several minor questions to the authors.1) How did you calculate the sample size (75 healthy subjects)?2) As described in the limitation, I also afraid the effects of hypertensive agents to your results. Your discussion about this issue in the limitation paragraph was quite reasonable. If possible, it will be helpful to us that the subjects with hypertensive agents would be excluded or showed in separate table.3) I would like you to show more concrete clinical perspectives.Reviewer #2: The reproducibility of dynamic cerebral autoregulation assessment is an important step for its clinical use. The authors found that the assessment was not reproducible unless MAP variations during the measurement window are significant. I found the analysis in the paper is sound. I have the following suggested revisions:1. The authors mention that reproducibility will probably improve if measurement duration is increased.
This is based on their use of the Spearman-Brown prophecy formula. The authors cite a reference for this, but they should also write what the formula is in the manuscript to make it easier to understand Fig. Are longer durations more likely to be reproducible because there are more likely to be significant spontaneous MAP variations?2. I would appreciate practical recommendations in the manuscript about what the power spectral density MABP levels need to be for reproducible results.Reviewer #3: The article 'Assesment of dynamic cerebral autoregulation in humans: is reproducibility dependent on blood pressure variability?' By Elting et. Is an interesting and potentially significant article on the intra-sibject variability of cerebral autoregulation measurements. The authors have performed a retrospective analysis of data from several sites, comprising of recordings of mean arterial blood pressure and cerebral blood flow velocity (derived from TCD).
The goal of the analysis is to quantify the variability in autoregulation indices specifically with respect to blood pressure variability. The analysis methods, statistical techniques used are rigorous, with instructive and explanative figures.
The conclusions drawn by the authors are appropriate. However, a few minor details reduce the clarity and readabilty of the article.
Those and other comments are listed below.1. The organization of the data sources is somewhat difficult to understand. The authors provide a list of data sources and analysis performed therein in supplementary tables S1 and S2. While the text states that data was derived form 5 participating centers, the tables seem to suggest 6 data sources. Furthermore, it is evident that the tables that each data source is used more than once. However, it is difficult for the reader to ascertain if the same class of analysis (e.g., transfer function) is performed more than once on the same data set. Furthermore, are all three categories of analysis avaibale for all data sources?
Some clarity on this, perhaps in the form of a supplementary table that lists the data source and associated analysis, will help.2. One of the goals of the analysis is to look at the variabilities of autoregulatory responses at different time points. However, the authors do not specifiy the range of time pioints from which dual ratings were derived for analysis (besides from an upper bound of 3 months). The time between measurements may be a significant factor in the variablity - this is not considered here.3. For the surrogate data used to replace data of poor quality, the authors performed simulatons absed on the Tiecks model.
How did the authors know which ARI to use, if the data was of poor quality to perform analysis in the first place? Also, please include a citaiton for the model.4. It might help to describe the outecome variables from the three autoregulation measurement classes.5. The authors describe the PSD based case removal approach, but do not provide sufficient justification for why the authors considered PSD as a measure of variability. Similarly, what is the rationale for using the 'lowest' PSD-MABP as the independent variable for correlation analyses?.6. PLOS authors have the option to publish the peer review history of their article.
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26 Dec 2019Assessment of dynamic cerebral autoregulation in humans: is reproducibility dependent on blood pressure variability?PONE-D-19-25218R1Dear Dr. Elting,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.Shortly after the formal acceptance letter is sent, an invoice for payment will follow.
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For more information, please contact.With kind regards,Tatsuo Shimosawa, M.D., Ph.D.Academic EditorPLOS ONEAdditional Editor Comments (optional):Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your 'Accept' recommendation.Reviewer #1: All comments have been addressedReviewer #2: All comments have been addressedReviewer #3: All comments have been addressed.2.
Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.3. Has the statistical analysis been performed appropriately and rigorously?Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.4. Have the authors made all data underlying the findings in their manuscript fully available?The requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available.
If there are restrictions on publicly sharing data—e.g. Participant privacy or use of data from a third party—those must be specified.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes.6.
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