We investigated the impact of human immunodeficiency virus (HIV) infection and anti-retroviral therapy (ART) on the gut microbiota of children.This cross-sectional study investigated the gut microbiota of children with and without HIV.We collected fecal samples from 59 children with HIV (29 treated with ART [ART(+)] and 30 without ART [HIV(+)]) and 20 children without HIV [HIV(–)] in Vietnam. We performed quantitative RT-PCR to detect 14 representative intestinal bacteria targeting 16S/23S rRNA molecules. We also collected the blood samples for immunological analyses.Streptococcus, Enterococcus, and Enterobacteriaceae, positively correlated with age in the HIV(−) group, but not in the HIV(+) group. In the HIV(+) group, Clostridium coccoides group positively associated with the CD4+ cell count and its subsets. In the ART(+) group, Staphylococcus and C. perfringens positively correlated with CD4+ cells and their subsets and negatively with activated CD8+ cells. C. coccoides group and Bacteroides fragilis group were associated with regulatory T-cell counts. In multiple linear regression analyses, ART duration was independently associated with the number of C. perfringens, and Th17 cell count with the number of Staphylococcus in the ART(+) group.In spearman’s correlation analyses, no significant correlation between the number of dominant bacteria and age was found among children in the HIV(−) group. However, the number of sub-dominant bacteria, including HIV infection and ART may influence sub-dominant gut bacteria, directly or indirectly, in association with immune status in children with HIV. Each child’s parents or guardians were informed, and written consent was obtained for all participants. This study is registered at UMIN-CTR: UMIN000015044.P = 0.048 and P = 0.009, respectively). Their detailed demographic characteristics and immune status are provided in Approximately 500 children with HIV were followed at the VNCH in Hanoi, Vietnam, in 2012, and 40 of them did not start ART according to the Guidelines of the Ministry of Health in Vietnam (No. 3003/QĐ-BYT dated 19/08/2009) Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This study examined select bacteria via RT-PCR in the feces of children from Vietnam who were HIV- or HIV+ (ART+/-). The authors then looked for associations between the absolute number of bacteria and a variety of other parameters  antibiotic.Major1. This study is similar to previous work including Flygel et al 2020 Journ Infec Dis and Kaur et al 2018 Sci Rep, which the authors did cite in the introduction. This work is also similar to the studies by Dirajlal-Fargo et al 2020 AIDS (Brazil) and Abange et al 2021 Sci Repo (Cameroon) which was not cited by the authors.A). Since the current study is so similar to other studies, the authors must provide additional justification for the current study in the introduction.B). The authors should reference the additional two studies listed above.C). There is no mention of these comparable studies in the discussion. The current study’s findings must be discussed in the context of existing literature . For example, in the Flygel et al study, children were on ART for at least 6 months at the time of sampling. How does this differ since it is known that the length of time on ART impacts gut microbiome ? How did T cell activation differ between the appropriate studies? Does geographical region impact differences between studies?2. The gating strategy in S1 Figure needs some modification.A). For the CD8 vs CD4 flow plot, most of the events appear to be out of view on the axis. This flow plot needs to be adjusted so that all events are brought into view.B). For the CD38 vs HLA-DR flow plot, please provide and isotype control flow plot or FMO controls. The authors may be missing some of the activated CD8 cells based on where the quadrant gate currently sits.C). It is confusing why the authors have an arrow pointing downwards from the lymphocyte gate towards CD4 when they could have gated from the CD4+ cells found in the CD8 vs CD4 flow plot.4. The authors need to provide justification for why CD3 was not included in the panel since monocytes and NK cells in the blood can express CD4 and CD8.Minor1. Provide some explanation in the discussion as to why the antibiotic cotrimoxazole only effected C. perfringens.2. Please provide some commentary on whether the use of RT-PCR instead of sequencing could overlook changes since not all bacteria were examined.Reviewer #2: The manuscript by Nguyen Q, et al, entitled “Alterations in children’s sub-dominant gut microbiota by HIV infection and anti-retroviral therapy” seeks to investigate the gut microbiota of children with HIV in Vietnam and the impact of ART. The paper has important implications as there is no clear consensus on the microbiome in children with HIV, both on and off ART. While the authors find that HIV and ART may influence sub-dominant gut bacteria, there is surprisingly not a significant difference between the microbiome of children uninfected and infected with HIV, opposing current research in the field that has shown reduced bacterial diversity with HIV. Overall, the paper is thought-provoking and provides broad impact; however, there are many key points that the authors need to address:Major points:1. A major claim within the paper is that HIV and ART influence the sub-dominant gut microbiota; however, support for this claim rely upon various associations based on the number of bacteria. This case would be strengthened by looking at alpha and beta diversity and the relative abundance of bacterial species.2. The methods of the RT-qPCR are unclear. The primers appear to be specific for the 14 listed bacteria. If this is true, this eliminates the analysis of a large variety of other intestinal bacteria. Could the authors explain their selection criteria for these 14 selected bacteria? Are these the most abundant bacteria in children? Could the authors also explain their rationale for not sequencing with a universal 16S rRNA primer to identify the most abundant bacteria?3. FoxP3 and IL-17 are expressed by Tregs and Th17, respectively. Have the authors considered performing a functional analysis of the T cell populations to better classify these T cells?4. The authors state that they “regarded the threshold for dominance in abundance at 1.0% of the total bacterial count, and the threshold in counts was set at 109 cells/g”. What percentage of the bacterial counts passed this threshold? How was the threshold for sub-dominant bacteria determined at <109 cells/g?5. Could the authors explain whether the bacterial counts were normalized across groups?6. It appears that there were many comparisons examined to produce the associations highlighted in this manuscript. Was the data corrected for multiple comparisons using a false discovery rate?7. Table S2 provides the detection frequency of bacteria in fecal samples from each study group. However, this only accounts for the percentage of samples that harbored the detectable bacteria. Providing the relative abundance of bacterial frequencies within the entire group would strengthen the data and provide a better comparison of the distinct patterns across the groups.8. The association between number of bacteria and age represent a correlation, not a causation. Can the authors expand upon the impact of the data especially since the groups differed in the average age? Could the positive association be due to the development of the microbiome and the lower age of the HIV(-) group?9. Can the authors speculate on the impact of the ART duration? Was the duration long enough to see an impact on and restoration of the microbiome?10. Studies have shown that an altered gut microbiota is associated with elevated circulating inflammatory markers. Since blood was collected in this study, did the authors consider performing an ELISA on the plasma to check for elevated markers of inflammation and microbial translocation?11. HIV(-) and HIV(+) groups had similar gut microbiota structures both of which differed from the ART(+) group. Could the authors speculate as to whether this was due to ART itself, irrespective of HIV infection?Minor points:1. Could the authors include the age at which the children in the ART(+) group started ART?2. Could the authors provide the specific antiretroviral used in the study, including the specific nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitor, and protein inhibitor?**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  3 Sep 2021Responses to the Reviewers' comments:The authors would like to thank all the reviewers for their valuable suggestions and precise comments to clarify the major contribution of the work. Moreover, we sincerely appreciate the reviewers’ great efforts in pointing out the existing inconsistencies and errors for the improvement. To our best, the manuscript has been carefully revised and clarified according to the reviewers' comments.Reviewer #1: This study examined select bacteria via RT-PCR in the feces of children from Vietnam who were HIV- or HIV+ (ART+/-). The authors then looked for associations between the absolute number of bacteria and a variety of other parameters  antibiotic.MajorComment 1. This study is similar to previous work including Flygel et al 2020 Journ Infec Dis and Kaur et al 2018 Sci Rep, which the authors did cite in the introduction. This work is also similar to the studies by Dirajlal-Fargo et al 2020 AIDS (Brazil) and Abange et al 2021 Sci Repo (Cameroon) which was not cited by the authors.A) Since the current study is so similar to other studies, the authors must provide additional justification for the current study in the introduction. Response: Following the reviewer's comment, we have revised the sentences in the introduction as follows: “Although the composition of the gut microbiota may be influenced by age, diet, genetics, and geography, four phyla  are dominant and stable in healthy individuals ” (lines 56–58); and “A few studies from Africa and India have shown reduced bacterial diversity in the gut microbiota of children with HIV and children treated with ART compared to the microbiota of children without HIV [23¬¬−25]. However, no consensus exists on whether ART in children with HIV may restore the gut microbiota to the state observed in children without HIV [23¬¬−25].” (lines 74–78) The composition of gut microbiota is known to be influenced by geography [ref 7]. Poor understanding of gut microbiota in children with HIV living in different geography is one of the rationales for conducting the current study. Kaur et al. investigated the gut microbiota of HIV-infected and -uninfected children in India, Flygel et al. in Zimbabwe, and Abange et al. in Cameroon. In Vietnam, no study has focused on understanding the gut microbiota in children with HIV so far. B) The authors should reference the additional two studies listed above.Response: Following the reviewer's comment, we have included the paper by Abange et al. 2021 Sci Repo as a reference in our revised manuscript as mentioned above. However, we have not included the paper by Dirajlal-Fargo et al.  in the revised manuscript because of the reason as mentioned below:Dirajlal-Fargo et al.  investigated the correlation between fungal translocation and immune status in the adolescents with and without HIV infection, whose median age was 13 years (IQR 11-15), in Uganda. In contrast, we investigated the composition of gut microbiota and the correlation between gut microbiota and immune status in the children with and without HIV infection, whose age was between 2 and 8 years old, in Vietnam. Thus, their report does not match the context of our current study. Therefore, we have not included the paper in the reference list of this revised manuscript. Dirajlal-Fargo et al. (AIDS 2019) also reported that HIV-exposed-uninfected infants had higher levels of inflammation and monocyte activation compared to HIV-unexposed infants at birth, and that the elevated markers of inflammation were associated with a lower weight, in Brazil. As mentioned above, we consider that their report does not match the context of our current study, and have not included their paper in the reference list of the revised manuscript. C) There is no mention of these comparable studies in the discussion. The current study’s findings must be discussed in the context of existing literature . For example, in the Flygel et al study, children were on ART for at least 6 months at the time of sampling. How does this differ since it is known that the length of time on ART impacts gut microbiome ? How did T cell activation differ between the appropriate studies? Does geographical region impact differences between studies?Response:We believe that we have tried to discuss about most of our important findings in the context of existing literatures in each paragraph of the discussion section. However, as we have mentioned in the second paragraph of the discussion section that "our results may not be comparable directly to the results of the other studies using NGS, since the RT-qPCR method is not appropriate to calculate the diversity indices and the relative abundance of the selected bacteria (lines 336−338), we would think that direct comparison of our results with those of other studies conducted in different countries may not be appropriate. In addition, we have already mentioned the limitation of our study in the second last paragraph in the discussion section that "First, the children in the HIV(−) group were 2 years younger than the other groups. The diets were not controlled among the groups, though the children in the HIV(−) and ART(+) groups who resided at orphanages were provided the same diets. The children in the HIV(+) group who resided in their own homes appeared to have poorer nutritional status than the children in other groups, which could be due to the uncontrolled diet and/or HIV infection [26]. Considering the influence of age and diet factors on the gut microbiota , we did not focus on comparing the gut microbiota between the groups, but highlighted the factors associated with the gut microbiota in each group." (lines 398-406) As for the impacts of the length of time on ART on gut microbiome, we found that ART duration had an independent positive association only with C. perfringens, a sub-dominant gut bacterium, in the HIV-infected children on ART with a median duration of 3.5 years (range: 0.8–5.8 years). This is the most important finding in our study using RT-qPCR, the authors think. The changes of gut microbiota by ART have been reported in HIV-infected children on ART with a minimum duration of 6 months . On the other hand, Flygel et al. reported that the gut microbiota of children on ART longer than 10 years was similar to that of the HIV-uninfected children. Considering these previous studies, we would think that the duration of ART in our study was long enough to investigate the impact of ART on the gut microbiota, but not long enough to observe the restoration of the gut microbiota by ART.As for the correlation between gut bacteria and T-cell activation, we could not find any significant correlation between gut bacteria and CD8+-cell activation in the HIV(+) group nor ART(+) group in our study.Comment 2. The gating strategy in S1 Figure needs some modification.A) For the CD8 vs CD4 flow plot, most of the events appear to be out of view on the axis. This flow plot needs to be adjusted so that all events are brought into view.Response:We have adjusted the gating of CD4+ and CD8+ cells as density plots in Logicle scale to show the events much more clearly.B) For the CD38 vs HLA-DR flow plot, please provide and isotype control flow plot or FMO controls. The authors may be missing some of the activated CD8 cells based on where the quadrant gate currently sits.Response:We did not use isotype control or FMO controls, but used the unstained cells as gating controls. We have added two mini-figures, including no-staining, Per-CP vs. PE, PE-Cy7 vs. FITC, in the S1 Figure. Also, one sentence has been added in the S1 Figure legend as follows: "Unstained cells were used as gating controls.” (line 608)C) It is confusing why the authors have an arrow pointing downwards from the lymphocyte gate towards CD4 when they could have gated from the CD4+ cells found in the CD8 vs CD4 flow plot.Response:Following the reviewer's comment, we have revised the S1 Figure.Comment 3. The authors need to provide justification for why CD3 was not included in the panel since monocytes and NK cells in the blood can express CD4 and CD8.Response: The data were retrieved from our previous study . The stained immune cells were analyzed by JSAN flow cytometer  that could detect only 4 colors, which limited us including CD3 in the panel. Before conducting the study, we stained the cells in three colors: CD3, CD4 and CD8, and analyzed them with the flow cytometer to confirm if our cell-gating strategy would be appropriate. We found that CD4 and CD8 molecules were expressed more strongly on the CD3-positive CD4+ and CD8+ cells than CD3-negative CD4+ and CD8+ cells, respectively (below figures). We, therefore, gated the cells with highly-expressed CD4 and CD8 molecules as CD4+ and CD8+ cells, respectively, as shown in the below figure C and S1 Figure. The cells with highly-expressed CD4 molecule (46.17% in the figure C) were almost equivalent to the CD3+CD4+ cells (45.58% in the figure A), and the cells with highly-expressed CD8 molecule (17.51% in the figure C) to the CD3+CD8+ cells (17.86% in the figure B). In addition, as monocytes are larger than lymphocytes, most of the monocytes could be gated out by our lymphocyte gating strategy, especially when we used fresh whole blood for staining . Thus, we thought that our gating strategy could be used for this study.MinorComment 1. Provide some explanation in the discussion as to why the antibiotic cotrimoxazole only effected C. perfringens.Response: We have already discussed some about cotrimoxaole in the 6th paragraph in the discussion section as follows: "The use of cotrimoxazole reportedly influences some gut bacteria and reduces gut inflammation in children with HIV [48−50]. In the current study, the use of cotrimoxazole was associated only with C. perfringens in the ART(+) group. However, in multiple regression analysis, we found that ART duration, but not the use of cotrimoxazole, was independently associated with C. perfringens, which is a potentially harmful bacterium [51]."(lines 379−384). Thus, we determined that the association of the cotrimoxazole with C. perfringens in the ART(+) group was a spurious correlation.Comment 2. Please provide some commentary on whether the use of RT-PCR instead of sequencing could overlook changes since not all bacteria were examined.Response: We have already discussed our choice of an RT-qPCR approach rather than NGS in the second paragraph in the discussion section in the original manuscript. Following the reviewer's comment, we have added some limitation of the RT-qPCR approach in the second paragraph in the discussion section (lines 336–338).Reviewer #2: The manuscript by Nguyen Q, et al, entitled “Alterations in children’s sub-dominant gut microbiota by HIV infection and anti-retroviral therapy” seeks to investigate the gut microbiota of children with HIV in Vietnam and the impact of ART. The paper has important implications as there is no clear consensus on the microbiome in children with HIV, both on and off ART. While the authors find that HIV and ART may influence sub-dominant gut bacteria, there is surprisingly not a significant difference between the microbiome of children uninfected and infected with HIV, opposing current research in the field that has shown reduced bacterial diversity with HIV. Overall, the paper is thought-provoking and provides broad impact; however, there are many key points that the authors need to address:Major points: Comment 1. A major claim within the paper is that HIV and ART influence the sub-dominant gut microbiota; however, support for this claim rely upon various associations based on the number of bacteria. This case would be strengthened by looking at alpha and beta diversity and the relative abundance of bacterial species.Response: In this study, we did not employ the sequencing approach, but used RT-qPCR approach for analyzing specific bacteria. Because of large range of the bacteria (10^2-10^10 cells/g feces), we thought it would not be appropriate to calculate the diversity indices and the relative abundance from the microbiota data of the selected bacteria. This discussion has been added in the second paragraph of the discussion section as follows: "though our results may not be comparable directly to the results of the other studies using NGS, since the RT-qPCR method is not appropriate to calculate the diversity indices and the relative abundance of the selected bacteria. (lines 336–338) Comment 2. The methods of the RT-qPCR are unclear. The primers appear to be specific for the 14 listed bacteria. If this is true, this eliminates the analysis of a large variety of other intestinal bacteria. Could the authors explain their selection criteria for these 14 selected bacteria? Are these the most abundant bacteria in children? Could the authors also explain their rationale for not sequencing with a universal 16S rRNA primer to identify the most abundant bacteria?Response: As the reviewer pointed out, we used the specific primer sets for the 14 listed bacteria. We selected Clostridium coccoides group, Clostridium leptum subgroup, Bacteroides fragilis group, Bifidobacterium, Atopobium cluster, and Prevotella, since more than 70% of total intestinal bacteria were covered by these groups [ref 29]. The other 8 bacterial groups including lactobacilli and potential pathogens were selected from the perspectives of their associations with health and diseases. We employed the RT-qPCR approach since it is proven to be an efficient and valuable tool for an exhaustive analysis of gut microbiota over a wide dynamic range. The RT-qPCR approach can detect and enumerate the gut bacteria at the population level between 102 and 1011 cells/g of stool, while the lower detection limit of the sequencing approach is 107 to 108 cells/g of feces. The counts of the subdominant bacteria were around the detection limit of the sequencing approach or lower [ref 33], we thus took advantage of RT-qPCR to estimate the counts of these less abundant but clinically significant targets. Comment 3. FoxP3 and IL-17 are expressed by Tregs and Th17, respectively. Have the authors considered performing a functional analysis of the T cell populations to better classify these T cells?Response:The immunological data were retrieved from our previous study . It is obvious that the cytokine production is the best marker for classifying CD4 cell subsets such as Th17. Unfortunately, we could not carry out cell stimulation experiment at that time mainly due to the limited amount of the blood samples collected from the children with HIV infection (2-3ml of whole blood per child). Thus, we used cell surface markers to define the cells as an alternative method . Comment 4. The authors state that they “regarded the threshold for dominance in abundance at 1.0% of the total bacterial count, and the threshold in counts was set at 109 cells/g”. What percentage of the bacterial counts passed this threshold? How was the threshold for sub-dominant bacteria determined at <109 cells/g?Response: The previous reports revealed that the average of total bacterial counts was around 1011 cells/g of feces . We regarded the threshold for dominance in abundance at 1.0% of total bacterial counts, and thus the threshold in counts was set at 109 cells/g . We have already mentioned quantification of bacteria in human stool with RT-qPCR paragraph in the methodology section: “Previous reports revealed that the average total bacterial count is approximately 1011 cells/g of feces . We regarded the threshold for dominance in abundance at 1.0% of the total bacterial count, and the threshold in counts was set at 109 cells/g .” (lines 133–136). In detail, the dominant bacteria in the gut microbiota (≥109 cells/g of feces) included C. coccoides group, C. leptum subgroup, Bifidobacterium, Atopobium cluster, B. fragilis group, and Prevotella. The sub-dominant gut microbiota (<109 cells/g) included C. difficile, C. perfringens, Streptococcus, Enterobacteriaceae, Lactobacillus spp., Enterococcus, Staphylococcus, and Pseudomonas. In this study, the sum of the dominant gut microbiota accounted for 90.14% ± 14.66% (mean ± SD) of the total gut microbiota, and the percentage of the sub-dominant gut microbiota was 9.86%.Comment 5. Could the authors explain whether the bacterial counts were normalized across groups?Response: We have already mentioned in the methods parts: “In the same experiment, a standard curve was generated with the RT-qPCR data  and the cell counts (by DAPI staining) of the dilution series of total RNA from the standard strain for each bacterial target. The CT values from fecal samples were normalized to the standard curve to obtain the bacterial count per gram wet weight of feces.” (lines 126–130)The processing of stool samples was standardized by those wet weights. Fecal samples were weighed, and their portion of 4 mg was subjected to RNA extraction as written in the method part (lines 114–130). In RT-qPCR assay, a standard curve was generated from dilution series of total RNA extracted from the standard strain for each bacterial target based on the cell counts, and the RT-qPCR data of fecal samples were normalized to the standard curve to obtain the bacterial count per gram wet weight of feces of all the children in the same batch.Comment 6. It appears that there were many comparisons examined to produce the associations highlighted in this manuscript. Was the data corrected for multiple comparisons using a false discovery rate?Response: As we have mentioned in the study limitation paragraph in the discussion section, "Considering the influence of age and diet factors on the gut microbiota , we did not focus on comparing the gut microbiota between the groups, but highlighted the factors associated with the gut microbiota in each group." (lines 403–406). Additionally, to avoid type 1 error , we conducted a simple correlation analysis and confirmed with multiple linear analysis.Comment 7. Table S2 provides the detection frequency of bacteria in fecal samples from each study group. However, this only accounts for the percentage of samples that harbored the detectable bacteria. Providing the relative abundance of bacterial frequencies within the entire group would strengthen the data and provide a better comparison of the distinct patterns across the groups.Response: In the current study, we quantified the dominant bacteria in the gut microbiota (≥109 cells/g of feces), including C. coccoides group, C. leptum subgroup, Bifidobacterium, Atopobium cluster, B. fragilis group, and Prevotella. The sub-dominant gut microbiota (<109 cells/g) included C. difficile, C. perfringens, Streptococcus, Enterobacteriaceae, Lactobacillus spp., Enterococcus, Staphylococcus, and Pseudomonas. There was a big gap of the percentage of relative abundance between dominant groups and subdominant groups. Taking advantage of RT-PCR method, we could detect absolute number of bacteria, and focused on the analysis of the factors associated with the gut microbiota in each group. C. difficile and Pseudomonas were not included in further analyses due to the low detection frequencies . The other bacteria groups with the detection frequency of greater than 50% were added dummy data for further analyses. Missing values were imputed using the half of dectected limitation values. Comment 8. The association between number of bacteria and age represent a correlation, not a causation. Can the authors expand upon the impact of the data especially since the groups differed in the average age? Could the positive association be due to the development of the microbiome and the lower age of the HIV(-) group?Response: As the reviewer pointed out, the average age of the children in the HIV(–) group was 2 years younger than the other groups. This would limit our data comparison among the groups. Considering these, we have tried to make it clear that we did not focus on comparing the gut microbiota between the groups but highlighted the analysis of the factors associated with the gut microbiota in each group. Please refer to the second last paragraph of the discussion section (lines 398–406): “First, the children in the HIV(−) group were 2 years younger than the other groups. The diets were not controlled among the groups, though the children in the HIV(−) and ART(+) groups who resided at orphanages were provided the same diets. The children in the HIV(+) group who resided in their own homes appeared to have poorer nutritional status than the children in other groups, which could be due to the uncontrolled diet and/or HIV infection [26]. Considering the influence of age and diet factors on the gut microbiota , we did not focus on comparing the gut microbiota between the groups, but highlighted the factors associated with the gut microbiota in each group.” Comment 9. Can the authors speculate on the impact of the ART duration? Was the duration long enough to see an impact on and restoration of the microbiome?Response: Our multiple linear regression analysis showed a significant association only between C. perfringens and ART duration. The HIV-infected children in our study were on ART with a median duration of 3.5 years [range: 0.8–5.8]. The changes of gut microbiota by ART have been reported in HIV-infected children on ART with a minimum duration of 6 months . On the other hand, Flygel et al. reported that the gut microbiota of children on ART longer than 10 years was similar to that of the HIV-uninfected children. Considering these previous studies, we would think that the duration of ART in our study was long enough to investigate the impact of ART on the gut microbiota, but not long enough to observe the restoration of the gut microbiota by ART. Comment 10. Studies have shown that an altered gut microbiota is associated with elevated circulating inflammatory markers. Since blood was collected in this study, did the authors consider performing an ELISA on the plasma to check for elevated markers of inflammation and microbial translocation?Response: In our previous study targeting same cohort , we could not detect any bacterial 16S/23S rRNA from all children, though a few of the targeted bacterial 16S/23S rRNA gene (rDNA) were detected in the children of HIV(+) and HIV (–) groups but not in the ART(+) group. The level of sCD14 was not significantly associated with the detection frequency of the bacterial rDNA in the serum. Therefore, we did not include sCD14 as an indicator of microbial translocation in the current analysis. We also measured IL-2, 4, 6, 10, 17, IFN-γ, and TNF-α in the serum using two different Multiplex cytokine detection kits, however, all of these cytokines but IFN-γ were under the detection limits. Therefore, we did not include these data in current study. Other makers for inflammation and microbial translocation such as sTNFR-I and II, sCD163, IP-10, D-Dimer, hsCRP and so on, could not be evaluated due to the limited amount of the blood samples collected from the children (2-3ml of whole blood per child).Comment 11. HIV(-) and HIV(+) groups had similar gut microbiota structures both of which differed from the ART(+) group. Could the authors speculate as to whether this was due to ART itself, irrespective of HIV infection?Response: As we mentioned in the study limitation paragraph, the children in the HIV(−) group were 2 years younger than the other groups and the diets were not controlled among the groups, though the children in the HIV(−) and ART(+) groups who resided at orphanages were provided the same diets. Considering the influence of age and diet factors on the gut microbiota, we did not focus on comparing the gut microbiota between the groups but highlighted the factors associated with the gut microbiota in each group.  As we have mentioned in the first paragraph of the discussion section, "We found that several sub-dominant gut bacteria were positively associated with age in children without HIV, but this was not observed in the children with HIV. In addition, Staphylococcus negatively correlated with age, i.e. the duration of HIV infection, in the children vertically infected with HIV, and ART duration had an independent positive association with C. perfringens, a sub-dominant gut bacteria, in the HIV-infected children on ART. These findings indicate an impact of HIV infection and ART on the sub-dominant gut microbiota in children." (lines 319-326) Thus, we would think that HIV infection and ART influence the sub-dominant gut microbiota, directly or indirectly, in association with the immune status of children with HIV. (lines 45-46)Minor points:Comment 1. Could the authors include the age at which the children in the ART(+) group started ART?Response: Children in the ART(+) group started ART with the median age of 2.67 (0.42–6.92) years. This information has been added in the Table 1.Comment 2. Could the authors provide the specific antiretroviral used in the study, including the specific nucleoside reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitor, and protein inhibitor?Response: Following the reviewer's comment, the following sentence has been added in the results section (lines 205–210): "8 received zidovudine/lamivudine/nevirapine; 7 received stavudine/lamivudine/nevirapine; 6 received zidovudine/lamivudine/efavirenz; 4 received stavudine/lamivudine/efavirenz; 2 received zidovudine/lamivudine/lopinavir boosted with ritonavir; 1 received abacavir/lamivudine/lopinavir boosted with ritonavir; and 1 received abacavir/didanosine/lopinavir boosted with ritonavir."  Editorial comment 3. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository  and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.Response: We have re-conducted the multiple linear regression analysis for Staphylococcus and found that the Th17 count was independently associated with the number of Staphylococcus in the ART(+) group. We sincerely apology our mistake.However, fortunately the change did not affect our conclusion: “HIV infection and ART may influence sub-dominant gut bacteria, directly or indirectly, in association with immune status in children with HIV”, since Staphylococcus belongs to the sub-dominant gut microbiota.The sentences have been added or revised in the abstract, results, and discussion section as follows: In the abstract: “In multiple linear regression analyses, ART duration was independently associated with C. perfringens, and and Th17 cell count with the number of Staphylococcus in the ART(+) group.” (lines 42–44)In the result section: ”The multiple linear regression analyses including age, ART duration, immune status, and use of cotrimoxazole showed that the ART duration was independently associated with the number of C. perfringens  and the Th17 count with the number of Staphylococcus  in the ART(+) group (Tables 2 and 3). The linear regression analysis for C. coccoides group in the HIV(+) group showed no significant association (S5 Table).” (lines 299–304)In the discussion part: “We found that several sub-dominant gut bacteria were positively associated with age in children without HIV, but this was not observed in the children with HIV. In addition, Staphylococcus negatively correlated with age, i.e. the duration of HIV infection, in the children vertically infected with HIV, and ART duration had an independent positive association with C. perfringens and Th17 count with Staphylococcus in the HIV-infected children on ART. These findings indicate an impact of HIV infection and ART on the sub-dominant gut microbiota, including C. perfringens and Staphylococcus, in children.” (lines 311–318)Fifth paragraph has been newly added as follows:“Multiple regression analysis showed a positive association between gut Staphylococcus and Th17 counts in the ART(+) group, which was shown for the first time. Th17 cells produce interleukin-17, which is important for promoting neutrophil recruitment to clear bacteria and has a specific role in the host defense against Staphylococcus aureus skin infection [47]. Thus, it would be interesting to investigate the interaction between Th17 and gut Staphylococcus in order to understand the pathophysiology of HIV infection in children who are on ART.” (lines 361–367)The title of the table 2 has been modified as follows: Table 2. Linear regression analysis of Clostridium perfringens with age, ART duration, immune status, and use of cotrimoxazole in the ART(+) groupTable 3 and S5 Table have been newly added in the revised manuscript.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 22 Sep 2021Alterations in children's sub-dominant gut microbiota by HIV infection and anti-retroviral therapyPONE-D-21-16737R1Dear Dr. Ichimura,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: (No Response)**********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: (No Response)**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: (No Response)Reviewer #2: (No Response)**********what does this mean?). If published, this will include your full peer review and any attached files.7. 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Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapper with a 10-fold cross-validation method was performed to find the best subset of features which maximized the classification accuracy for each considered ML algorithm. Finally, patient classification was assessed through a ML analysis using AdaBoost of Decision Tree, k-Nearest Neighbors and Naive Bayes algorithms. A univariate statistical analysis proved 5 out of 9 parameters presented statistically significant differences among patients of distinct NYHA classes; similarly, a multivariate logistic regression confirmed the importance of the parameter  Heart-rate variability (HRV) measurements over the years have proven to be a valuable aid in the study of cardiorespiratory control systems under various pathological conditions and, particularly, in the definition of the prognosis in patients with myocardial infarction and congestive heart failure (CHF). Indeed, Malik et al.  indicateThe New York Heart Association (NYHA) functional class is widely used in clinical practice for evaluating the severity of functional limitations from a patient’s heart failure condition. Classification is based on the symptoms a patient experiences during activity .Machine learning (ML) has been used deeply in recent years to find hidden patterns in data or to build models for classification and prediction . In healML has been widely employed and discussed in the literature for its application in cardiology : it has Previous research has documented the implementation of ML algorithms in this field. For instance, Isler and co-workers  studied Despite the promising results presented in the last paragraph, previous ML studies have aimed at distinguishing CHF severity considering NYHA classification and proved the feasibility of HRV indexes in classifying patients according to this consolidated clinical scale ,23,24; nThe implementation of ML-based tools in physiology, particularly in the cardiovascular area, has become more and more important; these tools have transformed the framework of biomedical research and the introduction of new parameters  for performing the classification of the severity of CHF according to the NYHA could potentially support physiologists suggesting specific decisions.One hundred and ninety-nineth stable patients affected by CHF in sinus rhythm ranging from mild (NYHA I) to moderate (NYHA III) were hospitalized to the Heart Failure Unit of the Institute of Care and Scientific Research Maugeri  for assessment and therapy of the CHF. The dataset was the same used in the research of Maestri et al. having excluded only the patient with NYHA class equal to IV since only one subject could not be representative of the entire population . AccordiAs an alternative to the classic approaches to study HRV in the time and frequency domains, it is possible to study the beat-beat variability using Poincaré plot, the prognostic value of which has already been demonstrated in the literature for patients affected by CHF . The morThe developed software has allowed to automatically derive the following parameters extracted from the 2D plots see . BrieflyFour features were extracted following the aforementioned operations: Highest Variability Extension (HVE [ms]), Length (L [ms]), Area (A [ms]) and the percentage of the L which corresponds to HVE (P [%]), as shown in p [adim]), mean distance of peaks from the axis of symmetry (Dp [ms]), and the three inertia radii of the semi-ellipsoid of inertia  are the triplet of the barycenter of the distribution of the points and .A preliminary statistical analysis was carried out to investigate the dataset. First, a univariate statistical analysis was performed for each parameter extracted from the Poincaré plot analysis. A Kolmogorov Smirnov test for normality was performed to investigate the distribution of the data with a level of uncertainty of 0.05. Then, ANOVA or a Kruskal Wallis tests were performed to distinguish the classes of NYHA. Finally, a post-hoc test was performed whether the previous tests resulted significant  was computed to build a first simple model using two classes: low (NYHA = 1 and NYHA = 2) and high (NYHA = 3) cardiovascular risk. Three assumptions for the regression analysis were checked :The abseSeveral tools can be used to perform ML analyses: Tougui et al. performed a study on these tools in the context of heart disease classification  and idenThe following three ML algorithms were considered to carry out the analysis.k-Nearest Neighbors (kNN) is an instance-based statistical method. This algorithm is based on the hypothesis that records that are alike are likely to have properties that are alike. We can use this principle to classify data by placing it in the category with the most similar, or “nearest” neighbors. This method is based on the principle that the instances of a dataset will remain in close proximity with the other instances that have similar properties . In thusADA-B, short for Adaptive Boosting, is a ML meta-algorithm formulated by Yoav Freund and Robert Schapire . It is aNaive Bayes (NB) algorithm is based on a simple application using Bayes’ theorem. Bayesian probability theory is rooted in the idea that the estimated likelihood of an event should be based on the evidence at hand. NB makes a “naive” assumption about the data, i.e., all the features in the dataset are equally important and independent. The Naive Bayes learner is trained by constructing a likelihood table. NB assumes class-conditional independence, which means that events are independent so long as they are conditioned on the same class value. The NB classifier greatly simplify learning by assuming that features are independent given classes. Although independence is generally a poor assumption, in practice NB often competes well with more sophisticated classifiers . Despitep-value greater than 0.05 indicating normality. Therefore, these underwent ANOVA test and, in the case of a significant result, also a Bonferroni post-hoc test; otherwise, the remaining variables underwent a Kruskal Wallis test and then, eventually, the non-parametric post-hoc test.The Kolmogorov Smirnov test for normality showed that only P, L and According to the results of the univariate statistical analysis , 5 variap-value = 0.069). By contrast, Np and Dp were both significant  and could be used to distinguish respectively groups 1 and 3 and groups 2 and 3.Among the 3D parameters, only In summary, the post-hoc tests highlighted several differences between groups 1 and 3 or groups 2 and 3; no difference was found between groups 1 and 2.In light of the results achieved in the univariate statistical analysis, the following analyses were performed distinguishing subjects in low (groups 1 and 2) and high (group 3) risk classes. The first group was made up of 138 patients while the other one was composed of 61 patients (where the 138 patients are sum of the 22 and 116 patients belonging to NYHA classes I and II cited in the “Study population” section).The multicollinearity was checked and 8 outliers were removed .The ratio between the sample size of the smallest class and the number of independent variables was greater than 10 .To perform the MLR, the 3 assumptions  were evaluated :The multp were not significant in the univariate statistical analysis, but they were considered in the multivariate analysis despite having only one significant result; differently, the strong significance of p-value < 0.001).HVE and To classify patients into low and high-risk classes, it was carried out a ML analysis. First, a hold-out division intro train and test sets was performed, respectively 80% and 20% of the dataset. On the training set, a wrapper with a 10-fold cross-validation was applied to find the best subset of features maximizing the accuracy. The evaluation metrics were computed on the test set and the results are shown in ADA-B, kNN and NB achieved an accuracy greater than 80% and an Area Under the Receiver Operating Curve (AUCROC) greater than 0.70.ADA-B and NB achieved satisfactory and similar results as regards both accuracy and AUCROC, but probably NB could be considered the best algorithm in this context considering the higher sensitivity (66.7% against 58.3%). KNN, instead, achieve the highest precision (83.3%) followed by ADA-B (77.8%).This study shows how ML algorithms can potentially help physiologists to correctly classify CHF patients considering the well-known NYHA severity scale using non-linear unconventional features. Moreover, the results of statistical analysis and ML confirm the potential benefit of these unconventional features to help clinicians in quantitative assessments.First, the univariate statistical analysis showed the feasibility in distinguishing patients with low or high cardiovascular risk according to NYHA classification using features extracted through the Poincaré plot analysis. Then, the MLR helped to build a model with the above-mentioned features: HVE, P and he field , where sp and In this research decision tree, random forests and multilayer perceptron were implemented without obtaining satisfactory results (data not shown for the sake of brevity). By contrast, the results of the ML analysis obtained from ADA-B, NB and KNN were shown because these were not highly affected by the unbalanced nature of the dataset. For this reason, while we had previously used an artificial augmentation of the dataset to perform the ML analysis , in thisA more detailed analysis based on the implementation of a greater number of ML algorithms coupled with the features selection by means of the wrapper methodology has showed more interesting results than our recent pilot paper . FeatureThe combination of objective, methods and the selected Poincaré-related parameters expand the knowledge in this field setting this study apart from others. In fact, to our best knowledge, this is the first work which studies the possibility to distinguish CHF patients’ illness severity (considering NYHA classes) relying only on a subset of geometrical 2D and 3D parameters extracted from Poincaré plots. Similar works focused either on CHF patients and healthy subjects’ classification or performed ML classification considering also features related to the temporal and spatial domain.Gonçalves and Oliveira  extracteIt could be started a discussion to compare the presented results citing previous studies whose aim was to distinguish CHF patients considering different severity classes for many cardiovascular diseases. These works consider several types of features extracted from time, spatial and non-linear—e.g., Poincaré plot conventional features—domains. The research of Tripoliti et al. summarizes multiple of these findings . NonetheIn conclusion, the results obtained show NB, ADA-B and KNN (listed considering increasing sensitivities) can effectively classify CHF patients’ severity based on NYHA functional classification. Furthermore, this paper presents a novel application of specific and unconventional geometrical features extracted from Poincaré plot, previously investigated for CHF manual detection. Of course, other machine-learning workflows and statistical analyses could be performed and other conventional parameters or indexes correlating with CHF could be included to improve the evaluation metrics.The main limitations of our work are linked to the features of the considered dataset, due to both clinical and technical considerations. On the one hand , the unbalanced distribution of patients assigned to the different NYHA classes in our dataset has—undoubtedly—negatively influenced ML scores, in particular sensitivity and specificity. Previous researchers have shown how unbalanced datasets can affect the results of machine-learning analyses and have suggested using both more than one single metric to represent the results and an AUCROC of the precision-recall diagram to quantitatively evaluate the reliability of the models . In thisFinally, the implemented workflow of research may be affected by the manual subjective revision of all the single beats before building the Poincaré map; a future development could consist of building an automatic process also for this step of the research.


Despite the push to find ways to use and store excess CO2, the development of a large-area monitoring system is lacking. For these applications, there is little literature reporting the development and testing of sensor systems capable of operating in remote areas without maintenance and having significantly low cost to allow their deployment across a large land area. This paper presents the design and validation of a low-cost solar-power distributed sensing architecture using a wireless mesh network integrated, at selective nodes, into a cellular network. This combination allows an “internet of things” approach in remote locations and the integration of a large number of sensor units to monitor CO2 and methane (CH4). This system will allow efficient large area monitoring of both rare catastrophic leaks along with the common micro-seepage of greenhouse gas near carbon sequestration and oil recovery sites. The deployment and testing of the sensor system was performed in an open field at Oklahoma State University. The two-tear network functionality and robustness were determined from a multi-year field study. The reliability of the system was benchmarked by correlating the measured temperature, pressure, and humidity measurement by the network of devices to existing weather data. The CO2 and CH4 gas concentration tracked their expected daily and seasonal cycles. This multi-year field study established that this system can operate in remote areas with minimal human interactions.Carbon sequestration and enhanced oil recovery are two important geochemical applications currently deployed using carbon dioxide (CO Demonstration of a solar-powered sensor array for remote carbon sequestration and enhanced oil recovery monitoring. An unattended sensor array can collect real-time gas concentrations, allow leak detection, and measure daily concentration cycles. Injected gas can be stored in naturally occurring subterranean voids, or those left by mining and hydrocarbon production sites.3,4 Studies suggest that carbon sequestration by injection has a net benefit in terms of greenhouse gas (GHG) mediation efforts.5 The injection of pressurized CO2 gas has also been found to liberate hydrocarbons, enabling enhanced oil recovery (EOR) from new or existing wells. Once injected, CO2 liberates valuable hydrocarbons including methane (CH4).6 A report from the National Energy Technology Laboratory of the United States Department of Energy claims the sequestration of 20 billion metric tons of CO2 has been employed as part of EOR programs since their discovery.7,8 As of 2014, 53% of all commercial-scale EOR projects in the United States utilize gas injection techniques,9 and the number of projects using this method is expected to increase as there is more push for CO2 sequestration in conjunction with rising energy prices.10The effect of carbon dioxide , and other similar technologies working in combination can also be used to detect leaks.28,29 A study by NASA of a CH4 leak has shown the modern LIDAR equipment was even able to measure an above-ground gas plume from an exceptionally large leak.30 Nevertheless, the authors found no published studies of CO2 or CH4 leakage from carbon sequestration sites over long time scales due, in part, to the lack of low-cost instrumentation that can efficiently be deployed to provide local coverage around physically remote area. This paper presents the design, implementation, and deployment of a sensing array for this application.To address both environmental and health and safety concerns, comprehensive monitoring of both CO22 and CH4 around physically remote areas of interest, such as CO2 injection sites. The sensor network also follows a “lightweight” design paradigm to allow the array to be deployed and rearranged with minimal effort. These constraints are distinguishable by this implementation from those used for other environmental monitoring stations, such as those used to monitor weather (Mesonet) or Chemical Speciation Network (CSN) each with significant infrastructure investment at fixed locations.31–33 With this philosophy in mind, a low-cost solar-powered distributed sensing architecture using a wireless mesh network integrated, at selected nodes, into a cellular network, was designed, and implemented at the test site. The resulting array was validated by continuous operation since 2015.The sensing array's design aim is to provide dense local coverage of CO34 and aerial cloud analysis.35 The implemented sensor array is organized into three tiers. An overview of the tiered hierarchy is shown in e.g. Zigbee). Each of these local networks are limited to approximately 50 devices per network to ensure network reliability, with a unique identification number. Every mesh network, principally comprised of Tier 2 nodes, contains a single, larger Tier 1 node. Tier 1 nodes are similarly self-powered devices with additional sensors and cellular connectivity, requiring a larger solar panel and battery capacity to meet the power consumption requirements. Due to these additions, they are less portable than the small Tier 2 devices. This larger node coordinates with the sensors in its local network to transmit measurements over the cellular data network. Importantly, this scheme allows addition of sensor elements with high power consumption to operate in conjunction with the low-power sensors on the Tier 2 nodes. In the presented implementation, the Tier 1 nodes have extended sensing capabilities for CH4. The highest level of network architecture, Tier 0, is an internet server, a workstation housed at the base of operations, and this device is connected to a municipal power supply and the internet. The Tier 0 server acts as a receiver of the field data and has the computational ability to process and analyze the data. This tiered node architecture allows for a large number of inexpensive solar-powered sensors to monitor CO2 and CH4 with maximal area coverage.This effort builds on wireless sensor array concepts used in agricultural applications to monitor local conditions2.136The Tier 1 and 2 nodes can be generalized by a set of functional blocks, such as depicted in 2.2For low-power and low-cost environmental sensors, the specific needs of the collected data must be weighed against the complexity and cost of the integrated system. The Tier 1 and Tier 2 nodes share a set of the inexpensive sensors mounted on a common sensor board  and a MPXA6115A pressure sensor . For the CO2 and CH4 sensors, Honeycutt et al. reports limit of detection and precision of various low-cost commercially available sensors.37 Based on these results, the common sensor board includes a K-30 sensor  for CO2 and an MQ-4 sensor  for CH4. The K-30 sensor was picked for our system due to performance, low-power, and low-cost. In the case of CH4, there is no commercially available sensor that provides parts-per-million sensitivity at atmospheric levels, with low power consumption, low price, and easy availability. Previous work by Honeycutt et al.37 compared several solid-state CH4 sensors, including those from MQ-4 from Hanwei Electronics and TGS-2600, TGS-2610, and TGS-2611 manufactured by Figaro Engineering Inc. These sensors are not low-power, due to the presence of a heating coil, and require power management. The detection limit of the MQ-4, TGS-2600 and TGS-2610 were similar. Previous measurements for the TGS-2611 sensor show that it was slightly superior by about a factor of 5 to 10 with respect to its detection limit when compared to the other sensors.37 The MQ-4 was chosen due to its availability in larger quantities (100 s) within mandated the time period to construct the array and perform this study. The selected sensor are sufficient to detect potential leaks (defined as 1000 ppm and above) from carbon sequestration and EOR sites.The common sensor board includes elements to obtain an environmental baseline for performance validation in addition to the low-cost CO2.337 a Gascard CH4 sensor  was chosen for inclusion in these nodes. The Gascard CH4 sensor requires a mechanical pump for sampling, which can be turned off to save power when required. A long-life 1410D/2.2/E/BLDC diaphragm pump  was used to pull air from the outside of the enclosure through a hole on the underside. A 0.45 μm particle filter protected the pump and the sensors from particulate matter. The air is passed through the Gascard CH4 sensor, then to an acrylic enclosure containing the common sensor board, before being finally exhausted outside the enclosure. The air inlet and outlet ports are placed on opposite ends of the bottom side of the enclosure to minimize recycling of analyzed gas.The higher power storage capacity means the Tier 1 node can include additional sensors for better performance. Given a previous study,2.4The details of the calibration and baseline analysis is reported in ref. 2.5The control board contains an Atmel ATMega2560  low power 8 bit microcontroller with 64 kB flash operating on a RISC architecture (referred to as ATMega 2560 in this manuscript). The ATMega 2560 is under-clocked to save power using an MA-506  with an 8 MHz ± oscillator. The software was written in the Arduino environment with additional low-level code to allow the processor to turn its oscillator off and to enter a deep sleep mode when not in use. To realize this savings all functions are interrupt driven. A DS3231 real-time clock  and coin cell battery is used to create logging timestamps and to generate an interrupt to wake the processor every second. The DS3231 also provides an accurate clock, critical when data are being transferred across the network from multiple sensors. The Tier 0 server is responsible for ensuring that all clocks are set correctly. Data are logged using two memory storage methods. The first is a 24LC1026 1024 kB serial EEPROM . The second is a micro Secure Digital (SD) stable storage card, placed in a SCHD3A0100 micro SD card holder . The SD card can be disconnected from the power supply when not writing or reading data to reduce energy consumption.2 and an MQ-4 sensor for CH4 can be independently turned off to save power.For node-to-node communication, the board contains a socket for a XBee-Pro 900 HP radio . A serial interrupt to wakes the processor when a data packet is available. The board also provides a serial interface and power management for the cellular modem and the Gascard sensor used in the Tier 1 node. A 34-pin ribbon cable interface is also provided for the common sensor board described above. The cable provides a digital serial communication channels for the K-30 sensor and SHT75 sensor. The cable also connects the pressure and MQ-4 sensors to an analog filter, then to a dedicated 12 bit analog to digital converter on the control board. Both the K-30 sensor for CO2.62 and CH4 sensors in a remote area with minimal setup. The array must be deployable and rearrangeable with minimal effort and skill. Thus, a modem network capable of self-assembly was chosen. In the mesh network, data packets can transfer across multiple Tier 2 nodes as they travel to the Tier 1 node, increasing the range and allowing easily deployment without worrying about network constraints. The raw data format is an UTF-8 encoded hexadecimal string generated from the binary data from each network element. The stream also includes routing information (device addresses and network identification), timestamps, power status and current draw of each individual sensor element, solar panel and battery status, data payload, network status, and CRC16 checksum. Adhering to best-practices, the checksum records origin, transport nodes and integrity of each packet. Both the Tier 1 and 2 communication implementation uses the packet acknowledgment feature of the mesh network to ensure reliable transmission. The network status code includes these acknowledgments along with a flag indicating that it been written to an SD card. The automatic network routing allows any Tier 2 node to route packets originating from other Tier 2 nodes which are too far to allow these packets to be directly received by a given Tier 1 node.The Tier 1 and 2 devices are designed to allow dense local coverage of CO−1. Thus, even in less than ideal conditions, it will never reasonably take longer than a minute to complete the transmission, even if this is the first successful data transmission event in a given month. Delays longer than a month are unlikely. Such delays would imply highly overcast skies for the same period or damage to the equipment which would be indicated by a lack of packets for a particular node received by the Tier 0 node. In areas where large delays are possible, additional functionality to prevent transition and require directly accessing the local SD card, which is possible from Tier 0 node, should be implemented. In the current setup, minutes :01 through :50 are reserved for sending data packets while minutes :51 to :60 are reserved for Tier 1 to issue commands to Tier 2 devices. Thus, the theoretical limit for the communication scheme described is a maximum of 50 Tier 2 units assigned to a single Tier 1 unit. The authors have confirmed operation networks containing 30 Tier 2 nodes empirically. During the final ten minutes of the hour, commands can be issued by the Tier 0 server to the Tier 1 node, which may be further disseminated a selected Tier 2 node. Critical commands for network functioning include time synchronization and transmission time allotment. Additional commands include sensor parameter adjustments, power management parameters, and direct access to data stored on any local SD card. The reserved time ensures that any command can be completed before the data packets start being sent over the network again. This time partitioning proved to be very reliable over several years of continuous operation.As a means of congestion avoidance, each Tier 2 sends data packets at predetermined minute-long windows within a given hour. The network has a large latency (on the order of seconds) following the first packet sent in each transmission. However, the next packets are sent quickly. The CPU communicates with the modem using a 9600 BAUD serial link. Transmission between the digital modems occurs at a maximum rate of 200 kbit sIn lieu of automatic repeat requests for data which are not acknowledged, devices send unacknowledged data again on during the next transmission window. The final data packet also contains the collection time, the transmission time, and the time received by the Tier 0 node. This accounting proves critical when debugging the software and when reconstructing the data log from packets delayed due to power management events or communication errors during live deployment. Transmission of each data packet consumes significant power, and reduced power reserves can reduce transmission range, resulting in dropped packets. In these low-power situations, the ring buffer allows the system to delay transmission to the Tier 2 node until power reserved are replenished. The power-managed SD card provides a local backup and allows a record to be stored even in during extended times of cloudy weather. Depending on the battery charge, data sent to the Tier 1 node is transferred from ring buffer to the SD card once an hour  and network parameters are also stored in a SQL database. These “vitals” data may be accessed separately from the collected raw sensor data. This feature is useful for remote monitoring of the sensor network through an internet portal. Periodically, the Tier 0 server processes the raw data to obtain the measured values from the sensors and other monitoring points. For further data analysis, this information may be downloaded from the server as a plaintext comma delineated data file.The Tier 0 server stores the raw data from the binary streams in a SQL database. The validity of each data packet is conformed through a simple CRC16 checksum generated during data collection. This second step allows all checksum to be validated, and a variety of post-processing to be performed without modifying the raw data. In addition, sensor configuration data   to evaluate the device network. These sensors have been actively returning data since October 20The results reported in −1, and the enclosures suffered heavy cosmetic damage. Even during these extreme conditions, the array stayed operational. The long term data also shows significant fluctuation around the average valve, with more fluctuation toward lower CO2 concentrations. These fluctuations could be do to animal movements (i.e. deer), sensors powering down and then up due to solar power limitations, and potential effects of temperature, pressure and relative humidity on the reading of the sensor. Large fluctuations from the average are also possible during conditions of high relative humidity or under rapidly changing weather conditions. See Section 4.2 for more discussion. Importantly, all fluctuations from the average are under 1000 ppm, a level expected from a significant leak from a carbon sequestration or EOR site. Given this application, the described sensor design performed exceptionally well.Over this time period, all sensors in the array operated reliably after brief periods of inactivity during low-power conditions and after changing weather conditions. During cloudy conditions, heavy rain, and winter and summer temperature extremes, all units continued to function properly. In one severe weather event, some of the units were even struck by flying debris from wind gusts exceeding 110 km h437Carbon sequestration and EOR are two important geochemical applications for remote sensor systems capable of operating in remote areas without maintenance and having significantly low cost. The selected sensors are sufficiently sensitive to detect potential leaks (defined as 1000 ppm and above) from carbon sequestration and EOR sites. In this discussion, the collected atmospheric data are compared to both expectations and data collected at local weather stations. The data reported by the sensor network is considered “good” if those reported by each sensor tracked well with the other sensors in the network, relying on previous studies of accuracy and precision for sensor quality assumptions.4.139 The closest weather station in the USCRN, Stillwater Regional Airport (KSWO) , reports hourly data, and an additional nearby station ) reports data every five minutes. To compare the sensor network collectively with the weather data, temperature, pressure, and relative humidity were averaged across all units and for all points within an hour. By taking the difference of these hourly points from the hourly average reported by KSWO, we generate residuals to compare the two. 40 Repeating the distribution analysis with the 5 min data from the Stillwater 2 W station showed no appreciable difference from the hourly sampling from KSWO. The peak of each distribution is shifted slightly from the mean value reported, and the difference is likely due to the difference in site elevation. While the surface of the sensor site stands at 287.7 m above sea-level, the KSWO instrumentation sits 299.9 m above sea level. The inverse shift of pressure and temperature peaks supports this argument.41 Although relatively simple to correct pressure for altitude, correcting temperature for altitude is not possible.Data collected from the temperature, pressure, and relative humidity sensors were evaluated with respect to known weather data collected from the database of the U.S. Climate Reference Network's (USCRN) local climate data for Stillwater, OK.vice versa), we see that the distribution from KSWO, depicted in The apparent bimodal distribution of humidity residuals at the site suggests altitude differences do not account for everything. Controlled environment tests, the SHT75 sensors faithfully recorded humidity within the bounds (±1.8%) stipulated by the manufacturer. By correcting the humidity residuals for time of day, we can plainly see in 4.22 concentrations, thus validation of the concentration values reported by the sensors is not as simple to perform. As there are no other atmospheric gas sensors deployed in the atmospheric layer and geographic locale of our sensor network, we must approach conclusions in an indirect manner by verifying sensors with known equivalents nearby and considering sensor limitations. To help with the analysis, averaging the time points for the whole data set , the K-30 manufacturer specifications state a maxim error of less than ±30 ppm. This error range is similar to the concentration fluctuation around the average shown in For COet al. at the nearby Department of Energy Atmospheric Radiation Measurement Climate Research Facility (ARM) suggests that low altitude CO2 should only increase by roughly 10 ppm compared to the global average reported by the Mauna Loa observatory situated above 3000 m, which is generally stable above 2000 m.43,45 The ARM Southern Great Plains site, selected in part for the rural locale, consistently reports diel cycles that peak near 420 ppm, close to the global average.46 Local measurement in dense urban centers47 have shown excess CO2 levels of up to about 80 ppm with much smaller average excesses. Other studies have shown larger excess of up to 100 ppm, with large fluctuation between day and night.48,49 The test field location is close to several industry sources, include set of ceramic kilns and a petroleum fractionation tower.A previous study by Abshire 2.50–54 While only a sampling of the available literature is mentioned in the previous citations, a critical analysis of sources shows current technologies are not utilized to detect concentration often, for a long time, and over a large area. These studies usually involve a single sampling instrument operated continuously for a short period, samples taken with large gaps between points, or distributed detection of multiple sites not performed concurrently. The “lightweight” distribution of sensors in the array described here, combined with the relatively short measurement time-scale, lends itself to opportunity for detailed discussion of the diel cycle of reported gas concentrations.Similar to the daily cycling of temperature, pressure, and other climatological phenomena, relative atmospheric concentration of gases undergoes a diel variation. Determination of these cycles is valuable to climate scientists, biologists, and agricultural engineers for CO4.32 analysis, 4 sensing implementations. Although low cost–cost CO2 sensors have good performance,37 the same confidence cannot be granted CH4 sensors in general. Chemiresistive sensors, which comprise nearly all of the CH4 sensors in this class, are notorious for temperature dependence and sensitivity to competing gases, including humidity. In our prior study, we found the MQ-4 sensor was the best performer with regard to precision and accuracy at the low gas concentrations near the atmospheric baseline, and had a limit of detection of 27 ppm.37 In addition, the nonlinear calibration of the MQ-4 sensor has signification uncertainty at low concentrations. It is also important to note that for stated application of leak detection, these sensors should be sufficient. The rationale behind the selection was described in Section 2.2.Similar to our CO2, daily cycling is also reported for CH4.55–60 The authors believe that this observation of daily cycling in the CH4 data is possible due to the large number of averaged CH4 sensors in the network. In terms of the absolute value of CH4 concentration reported by the sensors deployed at this site, given the similar values, the elevated CH4 background could be real and should be followed up in another study. As OSU is a land-grant school with a strong agricultural program, nearby animal experiments may produce unexpectedly large amounts of GHGs, including CO2 and CH4. Livestock is a notorious emitter of GHGs.61–64 Occupational health and safety and animal husbandry articles often report well-ventilated indoor ranching operations with CO2 concentration in thousands of ppm.65–70 Despite the accepted estimates of GHG emissions from ranching, there is a dearth of literature reporting ground-level outdoor ambient concentrations based on land use.71,72 Satellite remote sensing efforts to date are only able to measure high altitude columns, resulting in distinctions of <5 ppm from heavy urban to light rural land use.73–75 Engineering research facilities on the campus, including a concrete testing lab and a chemical engineering pilot plant very near to the deployment site may also contribute to the high gas levels.76 Due to Stillwater's relatively rural location in north-central Oklahoma, nearby oil infrastructure may also influence these readings.72,77,78 No matter the source, sensor limitations prevent a definite conclusions about the elevated concentrations.Similar to the daily cycling reported for CO5The constructed devices of the networked array of sensing units provide a viable method for determining the gas concentration above ground. The objective was to develop a sensor system for monitoring leaks at carbon sequestration and EOR sites in remote areas without maintenance and having significantly low cost. In this paper such a system has described and shown to work reliability for a period of several years without maintenance. In all cases, the sensors in the network reliably report data to the master node. The network programming is resilient enough to store redundant copies of the data across nodes of the network. During prolonged periods of low solar flux to charge the cells, units successfully entered a low-frequency sampling regime or “sleep” mode, depending on the level of power depletion. Sensors were additionally shown to be capable of withstanding dangerous weather and seasonal extremes of temperature.The sensor network distinguishes itself from larger networks such as Mesonet and CNS, filling a niche between those large implementations and small, single-sensor installations. By design choices reflecting an economy of cost and power consumption, a “lightweight” design architecture is achieved. The low power consumption allows for inexpensive power generation from small solar panels, which, in turn, allows for remote deployment of the sensor array from expensive power infrastructure. The tiered mesh networking architecture makes use of the broad GSM cellular coverage in North America, without incurring band saturation, since most of the units in the network only possess smaller radios. This mesh architecture also lends itself well to flexibility, allowing for diverse local geographic distribution to meet project requirements. Collection and storage of data at the server level (Tier 0) provides data access by scientists far-removed from the actual device installation, giving this “lightweight” network capabilities to match or even integrate with the large data repositories discussed above.4 sensing near the atmospheric baseline. New sensors are needed that are resilient, accurate, and low-cost. Notably, the array is capable of detecting minor local variations of gas within an area. This property makes the devices attractive for the identification of micro-seepage within the radius of an injected plume. Consideration in future deployments must be made to account for the sensitivity of the devices responding to unintended sources. The device network described here is a significantly lower cost and implementation barrier than other micro-seepage monitoring strategies.Testing the gases in the region just above the surface of the earth is an essential but challenging part of monitoring the environmental impact of carbon storage sites. The sensor network in this paper measure this region in a consistent manner. However, it is clear that improvements need to be in the field of CH79The Tier 2 devices, the majority of the units in a deployment, were housed in the Remote Pro 2.5 W continuous remote power system die cast enclosure  and Tier2, temperature and humidity, pressure, and MQ-4 CH4 sensors attached to a breakout board, as shown in 80 intercedes as a barrier between the board and the metal case (see The sensors are placed in a manner such that they would have contact with the environment for active sampling through holes pre-drilled on the enclosures. These holes are oriented towards the ground to prevent collection of rainwater and moisture. A 3D printed part designed in OpenSCADcase see . By flusThe network of sensors described in this paper, pictured in There are no conflicts to declare.


Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications.We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information.We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility.We found a high reproducibility of fNIRS measurements on a single-subject level (We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications. Furthermore, we quantify the effect of SCR on the hemodynamic response and its link to measurement reproducibility, brain activity estimates, and BCI classification using fNIRS. This work is important as it addresses the fundamental challenge of single-subject reproducibility, which is a crucial point for establishing fNIRS as a neuroimaging technique, and helps to translate fNIRS into daily life environments.22.1Fifteen subjects , the Cantonal Ethics Commission of the Canton of Zurich (2018-01078), and were conducted in accordance with the Declaration of Helsinki. Written informed content was provided by all subjects.2.2,A custom-built fNIRS instrument called optoHIVE was used to detect cortical brain activity.2.32.3.1To minimize the influence of variations in the optode placement at different measurement days and to maximize the sensitivity to the targeted M1 brain region, we determined the locations of the left and right M1 via TMS at the beginning of each session and marked it with a dry marker. Single-pulsed TMS was performed with a 70-mm Magstim200 stimulator  and a figure-eight TMS coil. The coil was placed tangentially to the scalp with the handle in the dorsal direction and laterally at 45 deg away from the mid-sagittal line. During the magnetic stimulation, electromyography (EMG) activity  of the contralateral abductor pollicis brevis muscle was monitored.2.3.2All 15 subjects completed a test-retest protocol consisting of two sessions on two different days [time-span foam see . The tas2.4,Data processing was performed in MATLAB . Motion artifacts were removed using spline interpolation.,A GLM2.5Statistical analysis was performed in R .,k)) measures and their 95% confidence intervals were calculated as suggested by Li et al.Reproducibility between sessions was assessed using linear correlation analysis and intraclass correlation coefficient (ICC) analysis applied to the lmer in ,A linear mixed effects model with restricted maximum likelihood estimation  was performed using a support vector machine (SVM) with 33.1On a group level, spatially specific patterns of brain activity were detected for the left and right grasping tasks, shown in On a single-subject level (see A), task-evoked brain activation became visible for nine out of 15 subjects, whereas six subjects showed only minimal or no activation . These qualitative results can be put into relation with the individual 3.2In the correlation plots in Test-retest reliability based on ICC is presented in 3.3The influence of different variables on Analysis of variance of [The results for HHb were similar to 3.4The effect of signal strength and MW amplitude on 3.5Classification accuracies were in good agreement with the results from the previous analyses: strong responders had high accuracies and weak responders had low accuracies. In the bottom line of 4In this work, we examined the reproducibility of fNIRS measurements during an active grasping task and the influence of SCR on it. We specifically investigated reproducibility on a single-subject level, which has not been presented yet in fNIRS. Furthermore, we linked reproducibility with BCI classification and factors affecting the estimation of brain activity.,–,–,On a group level, we successfully detected task-evoked cerebral activation and demonstrated the canonical hemispheric dominance, with the ROIs above the contralateral M1 (ROI 1 and 5) exhibiting the strongest hemodynamic responses for the right and left hand grasping tasks. In contrast, the more frontal (ROI 4 and 8) and ipsilateral (ROI 5+6+7+8 or 1+2+3+4) regions showed no or much reduced activation depending on the moved hand (left and right). These observed activity patterns are as expected and aligned with the fNIRS literature,–We thoroughly characterized the reproducibility of fNIRS measurements after minimizing factors that could affect them. Specifically, we set up a simple hand grasping task to reduce physiological changes  and obtained a well-localized and distinct brain activity pattern. We placed the fNIRS optodes over the M1-hotspots as identified by TMS, which reduced the influence of inconsistent optode placement.We adopted the concept of “strong” and “weak” responders from Saager et al.,,–,As the main driver for the separation into strong or weak responders, we observed two critical factors. First, five of the six weak responders had a low raw signal intensity below 0.06 V, which corresponds to an SNR below 40 dB and which is often suggested as a threshold for reliable fNIRS measurements.,,–For the future use of fNIRS for (out-of-the-lab) BCI applications, we trained a classifier on the first run and tested it on the unseen second run for each session. A relatively high classification accuracy of 85% was obtained over all subjects and sessions, which is in a similar range as other fNIRS studies investigating motor execution.,,The TMS localization was performed to investigate the reproducibility of measurements and ensure that the M1-hotspot was precisely determined for each subject. Although performing TMS localization in real-world applications would be impractical, it was important in the context of this study to maximize signal response and quality. As possible alternatives to TMS, approaches such as unguided optode placement should be considered. Because the hair characteristics (density and color) had a strong influence on the ability to extract strong brain activation in the statistical analysis, it is especially important to make sure that the fNIRS instrument optimally copes with hair,,,In this study, we did not obtain spatial-dependent values of scalp thickness, bone density, or skull thickness, which could influence the 5In this work, we demonstrated that fNIRS measurements are reproducible on a single-subject level when fulfilling certain prerequisites . We separated the measured subjects into strong and weak responders based on the quality of the hemodynamic response and showed that higher test-retest reliability and classification accuracy are obtained for the strong responders without and more distinctly with SCR applied. Raw optical signal strength and MWs were found to be the major determinants of reproducibility. Therefore, to ensure robustness in fNIRS applications and that sufficient brain activity is captured, we suggest screening each subject once with regard to raw optical signal strength and 66.1In In Figs. 8