Drosophila of inducible, glial cell type-specific TDP-43 overexpression, a model that causes TDP-43 protein pathology including loss of nuclear TDP-43 and accumulation of cytoplasmic inclusions. We report that TDP-43 pathology in Drosophila is sufficient to cause progressive loss of each of the 5 glial sub-types. But the effects on organismal survival were most pronounced when TDP-43 pathology was induced in the perineural glia (PNG) or astrocytes. In the case of PNG, this effect is not attributable to loss of the glial population, because ablation of these glia by expression of pro-apoptotic reaper expression has relatively little impact on survival. To uncover underlying mechanisms, we used cell-type-specific nuclear RNA sequencing to characterize the transcriptional changes induced by pathological TDP-43 expression. We identified numerous glial cell-type specific transcriptional changes. Notably, SF2/SRSF1 levels were found to be decreased in both PNG and in astrocytes. We found that further knockdown of SF2/SRSF1 in either PNG or astrocytes lessens the detrimental effects of TDP-43 pathology on lifespan, but extends survival of the glial cells. Thus TDP-43 pathology in astrocytes or PNG causes systemic effects that shorten lifespan and SF2/SRSF1 knockdown rescues the loss of these glia, and also reduces their systemic toxicity to the organism.Accumulation of cytoplasmic inclusions of TAR-DNA binding protein 43 (TDP-43) is seen in both neurons and glia in a range of neurodegenerative disorders, including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD) and Alzheimer’s disease (AD). Disease progression involves non-cell autonomous interactions among multiple cell types, including neurons, microglia and astrocytes. We investigated the effects in  Neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD) and Alzheimer’s disease (AD) involve dysfunction in both neuronal and non-neuronal cells in the brain. Each of these disorders involves abnormal accumulation of a protein called TDP-43, which is thought to cause toxic effects that ultimately lead to cell death. We used cell type specific sequencing to characterize the effects of inducing pathological TDP-43 within each of the non-neuronal cell types in Drosophila. This identified changes in expression of a gene called SF2/SRSF1, which has previously been implicated in mediating toxic effects. We found that knocking down the expression of this gene within individual glial cell types was sufficient to prevent the loss of nearby neurons. Pathological cytoplasmic inclusions of TDP-43 are seen in post-mortem brain tissues from >90% of patients with ALS, ~45% of FTD cases, and nearly 50% of AD cases –8. TDP-4Under pathological conditions, TDP-43 is lost from the nucleus and becomes sequestered into cytoplasmic inclusions, which removes this autoregulatory mechanism –17. ThisThe loss of nuclear function is in fact a shared feature of over-expression and knock out models, but the over-expression approach also has the potential to model impacts from presence of cytoplasmic inclusions . Over-exDrosophila TDP-43 model are convergent with this literature, implicating a variety of non-neuronal cells in toxicity to neurons [Drosophila glial cells is sufficient to cause severe motor deficits, drive TDP-43 pathology in nearby neurons, cause neuronal cell death, and greatly reduce lifespan [Such astrocyte toxicity has also been observed in in vivo rodent and fly models –37. Acti neurons . We havelifespan ,37,42.Drosophila have five major glial cell types in the adult central brain, whose functional roles and anatomical features bear many similarities to non-neuronal cells (including glia) in the mammalian brain [an brain ,44. The an brain . Althougan brain . In addian brain –47. We pan brain ,42.SF2/SRSF1, a known suppressor of C9orf72 ALS/FTD models [SF2/SRSF1.Here, we used cell-type-specific nuclear RNA-seq both to better characterize the baseline transcriptional profiles of the 5 major glial cell types and to investigate mechanisms underlying glial cell type specific impacts of TDP-43 pathology. We identified numerous glial cell-type-specific transcriptional changes in response to induced TDP-43 expression. Notably, these included D models  that we D models . We obseDrosophila, pan-glial over-expression of TDP-43 causes loss of nuclear localization and accumulation of cytoplasmic hyperphosphorylated TDP-43 [Drosophila model also triggers pathological cytoplasmic accumulation of TDP-43 in nearby neurons where TDP-43 is not over-expressed [ts) approach, we induced post development over-expression of TDP-43 in each of the 5 individual glial cell types. This temperature switch expression system allowed us to avoid effects on development from over-expressing TDP-43 during glial specification, and also permitted us to determine the time-course of lethality after induction on the first day of adult life  differential intracellular sensitivity of a given cell type to TDP-43 toxicity, (2) differences in severity of effects on brain homeostasis from loss of each glial cell type, or (3) from cell-type specificity in non-cell autonomous toxic effects from TDP-43 proteinopathy ,42. We fWith each cell type, we observed a progressive loss in the numbers of labeled nuclei over time. With PNG, ALG, EG and CG, we see a similar time-course of cell loss . With earpr). In order to quantify effects on PNG cell loss, we co-expressed a UAS-nuclear-GFP. In each case, we used the same Gal-80ts approach to induce post development expression of GFP and either TDP-43 or rpr. As above, animals were reared at 21°C and then shifted to 29°C after eclosion (Day 0) in order to induce expression. We then quantified the effects on lifespan and PNG cell loss  ,51. HereUsing this method, we generated nuclear RNA sequencing libraries from each of the 5 cell types and compared expression 2 days after induction of either the GFP tag alone (baseline), or the GFP tag plus TDP-43 (N = 3 libraries per group). Because each of the cell types are comprised of different numbers of cells per brain , we adjuWhen comparing the baseline expression profiles across each of the five glial cell types, several things are of note. First, we found that the CG and EG show higher correlation in gene expression with each other than with the other cell types . SimilarThe transcriptional responses to TDP-43 pathology within each glial cell type included significant differential expression of many genes from cellular pathways that previously have been implicated in TDP-43 related neurodegeneration. These included genes involved in neuroinflammation ,53, DNA The differential expression analysis identified numerous cell type specific responses to TDP-43, as well as responses that were found to be in common across several cell types . Among tts method, to induce TDP-43 together with either a control shRNA against mCherry or the shRNA that targets the SF2/SRSF1 gene55. We used the same Gal80ts induction conditions described in previous experiments to turn on expression after development . We found that SF2/SRSF1 knockdown significantly suppressed the lifespan defects caused by TDP-43 induction in either PNG or ALG  , and lieDrosophila do not have a clear microglial cell type, they do have an astrocyte like cell type. And expression of pathological TDP-43 in Drosophila astrocytes drives non-cell autonomous toxicity to neurons [A sizeable literature supports the idea that release of factors from disease-activated glial cells is sufficient to promote toxicity to other cells including neurons ,31,62. T neurons . The finDrosophila SPG, PNG and astrocytes is sufficient to trigger loss of nearby neurons. Although mechanisms that mediate non-cell autonomous toxic effects in TDP-43 related and other neurodegenerative disorders are not fully understood, there is evidence implicating inflammatory effects [Drosophila model that TDP-43 pathology drives expression of the mdg4-ERV, and that mdg4 contributes to the non-cell autonomous effects on neuronal survival [Our prior work has demonstrated that TDP-43 proteinopathy in  effects , movemen effects –66 and e effects ,67. We hsurvival ,42. IndeDrosophila, and potentially to the non-cell autonomous effects of TDP-43. Although several prior publications have reported baseline expression profiling of several glial-cell-types during larval development, and adult stages in Drosophila, this is the first direct comparison of RNA expression profiles across all five major glial subtypes in the adult, using a single platform. This dataset supports the idea that CG and EG are functionally most similar, and that PNG and SPG also are more similar to each other than to the other glial cell types , our laboratory wild-type strain, for at least five generations. Female flies were utilized throughout the study. Strains used are shown in Flies were reared at 21°C and maintained on standard propionic acid food for all experiments in this study. A list of transgenic lines used in this study can be found in the table below. All were backcrossed to Canton-S derivative wts repression, allowing Gal4 mediated expression. Each lifespan experiments included 20 female flies per vial of a single genotype, with at least 60 flies total per genotype in each experiment. Flies were counted and flipped onto fresh food every other day. The Log-Rank (Mantel-Cox) test and the Gehan-Breslow-Wilcoxon test were used to compare the survival curves.All flies were reared at 21°C and shifted to 29°C upon eclosion, in order to relieve the Gal80Genotypes used in Figs Brains from adult drosophila were dissected in phosphate-buffered-saline (PBS) on days 2, 5, and 10. After dissection flies were transferred to 4% paraformaldehyde (PFA) solution  and fixed for one hour in a vacuum to remove air from trachea. Brains were then washed 3 times in 1XPBST for 10 minutes each prior to either (1) being mounted directly for imaging; or (2) transferred to a blocking solution  overnight at 4°C on a nutator in preparation for staining. After blocking brains were washed once in 1X PBST for 10 minutes and transferred to primary antibodies against Repo  and Elav  in 1XPBST and incubated overnight at 4°C on a nutator. Following primary antibody incubation brains were washed 4 times for 15 minutes each in a 3% salt solution (in 1X PBST) and transferred to secondary antibodies  in 1XPBST and incubated overnight at 4°C on a nutator. After secondary incubation brains were washed 4 times for 15 minutes each in a 3% salt solution (in 1X PBST) and mounted in FocusClear (CelExplorer).Brains were imaged within 24 hours of mounting using a Zeiss LSM 800 confocal microscope at 20X. All images were processed by Zeiss Zen software package. A single standard anterior section, defined by anatomy, was chosen for analysis for all cell quantification. Images were analyzed using FIJI (ImageJ). Mixed-effect analysis and two-way ANOVA were used to compare cell counts.2 and flash frozen in liquid nitrogen before being stored at -80°C until an appropriate number of flies were collected for each group . Fly heads were separated, homogenized, and incubated with an antibody against GFP (ThermoFisher: G10362) before being bound to protein A beads, cleaved, and rebound to protein G beads. Twice purified TAPIN-tagged nuclei were then burst and nuclear RNA was collected and stored at -80°C.Methods are described in detail in prior studies ,51. BrieTAPIN-generated nuclear RNA was treated with DNase and purified by the Arcturus PicoPure system. Purified RNA was amplified to cDNA via Nugen Ovation v2 system (Nugen: 7102–32) and was then fragmented with a Covaris s-series to 200bp. Fragmented cDNA was repaired and underwent second strand synthesis before being ligated to barcoded linkers . Libraries were quantified via KAPA qPCR before being sequenced on Illumina NextSeq to 76bp read length.98 using the DESeq normalization strategy and negative binomial modeling. B-H corrected FDR P-value threshold of p< 0.05 was used to determine significance. For correlation plots, Pearson correlation was calculated using normalized count values from DESeq2. For heatmap visualization, counts were normalized using a variance stabilizing transformation in DESeq2Reads were aligned to the dm6 genome using STAR v2.7.6a , allowinWith the exception of the TAPINseq expression analysis, all statistical analyses were performed in GraphPad Prism v9.2. Specific statistical analyses are listed under each methods description and include mixed-effect analysis, two-way ANOVA, Log-rank (Mantel-Cox) test, and Gehan-Breslow-Wilcoxon test.S1 FigFlies expressing UAS-TDP-43 and the INTACT reporter (5XUAS-unc84-2XGFP) under control of a temperatures sensitive Gal80 plus a glial cell-type-specific Gal4 (methods) were (A) reared at 21°C and shifted to 29°C upon eclosion (day 0). Heads were collected for sequencing on day 2 and stored at -80°C until use. (B) schematic of the INTACT nuclear tag used to purify nuclei and schematic of the tandem affinity purification process.(PDF)Click here for additional data file.S2 FigGene expression profiles generated from (A) Pearson correlation analysis demonstrating EG and CG more closely resemble each other at baseline than they do their own cell type with TDP-43 expression (B) principle component analysis plot demonstrating PNG and SPG separate from other glial cell types. (C) upregulated and downregulated genes (p < .05) in a given glial cell type and their overlap with other glial cell types.(PDF)Click here for additional data file.S3 FigThe PNG-Gal4 and Gal80ts were used to induce post development expression of hTDP-43 and UAS- Watermelon (WM), a dual reporter module that encodes both a myristylated GFP that tethers to the membrane (myr-GFP-V5) and nuclear localized mCherry (H2B-mCherry-HA). As in other figures, flies were reared at 21°C and shifted to 29°C upon eclosion (day 0). To quantify effects on neuron number, brains were dissected at day 10 (D10) post temperature shift. PNG glia were visualized using the mCherry nuclear reporter, and neuronal nulei were visualized using the Elav antibody. The numbers of Elav-labeled neuronal nuclei were quantified in A 4.68 μm stack  of ventral, posterior brain region as depicted with a yellow box in the image at left. This reveals a statistically significant  reduction in numbers of neurons in TDP-43 expressing  vs controls . Means and SEM are shown.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(PDF)Click here for additional data file. 29 May 2023Dear Dr Dubnau,Thank you very much for submitting your Research Article entitled 'TDP-43 pathology in Drosophila induces glial-cell type specific toxicity that can be ameliorated by knock-down of SF2/SRSF1.' to PLOS Genetics.The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important topic but identified some concerns that we ask you address in a revised manuscript.We therefore ask you to modify the manuscript according to the review recommendations. Your revisions should address the specific points made by each reviewer.In addition we ask that you:1) Provide a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.archive. If your image is from someone other than yourself, please ensure that the artist has read and agreed to the terms and conditions of the Creative Commons Attribution License. Note: we cannot publish copyrighted images.2) Upload a Striking Image with a corresponding caption to accompany your manuscript if one is available (either a new image or an existing one from within your manuscript). If this image is judged to be suitable, it may be featured on our website. 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You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.Please let us know if you have any questions while making these revisions.Yours sincerely,Fengwei YuAcademic EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Krupp et al. described the effect of SF2/SRSF1 knockdown in glia-induced TDP-43 pathology in Drosophila. Overall the study is interesting, of interest, and well conducted. I have some major concerns prior to publication:1. How physiologically relevant is glial-specific TDP-43 overexpression? TDP-43 accumulation occurs in both glia and neurons, but is TDP-43 expressed in both cell types? If it’s dominantly expressed in neurons, overexpressing TDP-43 in glia does not recapitulate the situation under normal physiological condition.2. For all experiments in the study, does the control have similar GAL4:UAS ratio as the experimental group? As titration effect of gene expression might occur when using one GAL4 for two UAS, it will be best to compare all effects with similar number of UAS transgenes for the same GAL4.3. Did the authors try overexpressing SF2/SRSF1 in the presence of TDP-43 in glia? It is a bit counter-intuitive to learn that knockdown of SF2/SRSF1 ameliorate TDP-43 pathology in glia, as RNA-sequencing results suggest that this gene is downregulated when TDP-43 is overexpressed. One would imagine that by overexpressing SF2/SRSF1 TDP-43 pathology will be rescued.4. The discussion on the non-cell-autonomous toxicity from glia to neurons is a bit hard to understand. It will help if the author can revise and add in more discussion to make things more clear.5. There is a typo in Figure 1A, the genotype is unc8402xGFP?Reviewer #2: Understanding how TDP-43 related pathology and aggregation can underlie neurodegenerative diseases like ALS, FTD, and AD is a critical current goal for the field. Important recent work highlighted that not only neurons play a role in the pathological process, but that non-cell autonomous interactions with glia are important. Krupp and colleagues examine such interactions in more depth in this manuscript, to determine the effects of TDP-43 overexpression in specific subpopulations of glia on TDP-43 pathology. They show that organismal survival is most severely impacted following TDP-43 overexpression in perineurial glia or astrocytes; for perineurial glia, this is not due to cell ablation, but likely non-cell autonomous defects. Further, they observe that TDP-43 overexpression in each glial cell type leads to varying degrees of age-dependent cell loss. The authors conduct TAPIN-seq to determine how TDP-43 overexpression affects the transcriptome of each subpopulation of glial cells and show that SF2/SRSF1 is reduced in perineurial glia and astrocytes, consistent with previous broad cell-type findings in ALS models. Further knockdown of SF2/SRSF1 in either perineurial glia or astrocytes reduces TDP-43 pathology including cell loss and lifespan.This paper represents a strong and important contribution to the field for two main reasons: 1) it demonstrates a fascinating biological finding that glial subtypes are not equally susceptible / causative in TDP-43 pathology and 2) it establishes a valuable resource in the nuclear RNA sequencing databases of different glial subtypes under normal conditions and TDP-43 overexpression conditions. Both aspects are notable advances in the field. Moreover, the paper is well-written, with the experimental logic clearly laid out and the results straightforward and easy to follow. My criticisms are minor and largely restricted to issues of data presentation and the need for additional discussion on the roles of glial subtypes and the mechanism of SF2/SRSF1 function. With these minor additions, the paper will be suitable for publication.Experimental Point - Though consistent with data from the field, the knockdown of SF2/SRSF1 that rescues TDP-43 pathology when TDP-43 pathology already results in a reduction of SF2/SRSF1 is counterintuitive. If the two were causative / genetically linked, I expect SF2/SRSF1 overexpression would rescue the pathology. Can you instead overexpress SF2/SRSF1 and see altered TDP-43 pathology? Further, the authors posit that SF2/SRSF1 “rescue” results in “a reduction in the non-cell autonomous toxicity of the glia to surrounding neurons.” Can this be demonstrated and quantified? It would help strengthen the existing data. It would also be helpful for the authors to include further discussion of the role / mechanism of SF2/SRSF1.Discussion Point - There is little discussion of the role that astrocytes are playing in TDP-43 overexpression pathology. The roles of subperineurial glia and perineurial glia are well discussed, but additional discussion / speculation on the role of astrocytes would be very helpful here.Data Presentation Points - My biggest issue with this paper is in data presentation. In its current state, it is very challenging to discern labels and interpret the results of figures and graphs throughout the paper. Though the issue is present in all figures, it is most distinctly so with Figure 2. For all figures, can the authors adjust font size and clarity? In Figure 2, even at very high magnification, it’s still challenging to tell what the labels are showing. Additionally, larger graphs  would be very helpful in conveying the important points as with the rest of the figures. In all figures, larger image labels would be helpful.In Figure 1, it is also difficult to tell the difference in the curve genotypes due to the size of the shapes. Can you color the TDP-43 containing curves so it’s easier to see? Also, can you denote significance on the graph? There is no significance in the figure despite noting it in the text and a p value in the figure legend.Finally, in Figure 4, the letter labeling is inconsistent with the rest of the paper. Also, the pink bar denoting the Tg_TDP43 genotype is difficult to see. Can it be changed to aid in visibility?Reviewer #3: TDP43 pathology plays a central role in the pathogenesis of ALS and FTD, and may be also involved in AD as well. Thus, it has been under intense investigation in the field. Over the years, Dr. Josh Dubnau and his colleagues have investigated the effects of TDP43 pathology in Drosophila by overexpressing it in different cell types and have made a series of important discoveries (such as DNA damage and retrotransposon activation). In the current study, the authors made an interesting observation that TDP43 expression in the perineural glia (PNG) and astrocytes had most pronounced effect on organismal survival compared with other types of glia. Because genetic ablation of PNG did not have the same effect, these findings suggest that some unknown factors released by TDP43-expressing PNG cause systematic toxicity and neurodegeneration. This is important, because they are now in an excellent position to perform genetic studies to identify the nature of these factors released by PNG. Of course, their work here would be much more significant and novel if they could report what these factors are. Then they went on to perform cell-type specific nuclear RNA-seq and found that SF2/SRSF1 is downregulated in PNG expressing TDP-43. Further KD of SF2/SRSF1 in PNG rescued loss of these glia and reduced systematic toxicity of TDP43. Although the beneficial effects of SF2/SRSF1 KD in TDP-43 or C9 models are already known, their RNA-seq datasets will be useful for the field.The authors can consider the following suggestions for minor revisions to further improve their manuscript.1. The strength of different glial subtype-specific Gal4 drivers may be different. Is it possible different levels of TDP43 are expressed in different glial subtypes to cause different effects on organismal survival? Western blot analysis will not help address this question because the numbers and location of glia in different subtypes are different. Or immunostaining of different flies is good enough to tell the difference. At a minimal, the authors can discuss more in detail the number and location of glial subtypes, and potentially differential strengths of different Gal4 drivers.2. As mentioned above, SF2/SRSF1 is already known to be a modifier in TDP43 and C9 models . If not too much work, the authors can include another novel modifier identified from their RNA-seq analysis to enhance the novelty of the study. Alternatively, they can perform a few more mechanistic studies to understand further how SF2/SRSF1 modifies TDP-43 toxicity. For instance, is SF2/SRSF1 required to export TDP43 mRNA from the nucleus to the cytoplasm as it does for C9 repeats as reported? Does SF2/SRSF1 overexpression enhance the phenotype? And so on.3. Maybe the authors published this in their earlier papers. In several places, the authors talk about toxicity of glial TDP43 on neurons, but they never showed any data. Is that possible to include neuron survival data at least in one figure?4. In Figure 1 legend, it is stated that p<0.0001 for all panels. Is that true? This reviewer is a little bit surprised, because in Panel C, the small difference is largely due to the second data point and death occurred only few days after the experiment started. So the authors should carefully perform statistical analyses again and state in the legend what statistical methods were used. The number of flies for each panel should be stated and it is better to list p values in each panel of the figure as well.5. In Figure 2 and Figure 3 legends, *p<0.5 must be an error. Is it *p<0.05? Also, please state the values are S.D. or S.E.M., what are n numbers, and by what statistical analysis in all figure legends.6. In Figure 3B, the number of PNG cells at day 2 is >200, but the image does not show that many cells as in Figure 2. Please explain.7. In Figure 4 legend, they say “upregulated (blue) and downregulated (red) genes”. But in the figure, upregulated genes are in red and downregulated genes are in blue.8. Figure 5 can be reorganized to better utilize the empty spaces. Panels D and E can be new Panels A and B, and Panels A-C can be new Panels C-E.9. Please increase font size in all figures.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: None**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article , and click on the Fetch/Validate link next to the ORCID field.  This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager.In the meantime, please log into Editorial Manager at If you have a press-related query, or would like to know about making your underlying data available , please see the end of this email. If your institution or institutions have a press office, please notify them about your upcoming article at this point, to enable them to help maximise its impact. Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Fengwei YuAcademic EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The reviewer has no further questions and the manuscript is ready for publication in its current formReviewer #2: I thank the authors for their thoughtful replies to my comments, for the addition of new data in Figure S3, and especially the changes to data presentation which greatly improved the figures. This was very helpful. The authors have appropriately addressed all of my comments and I can support publication. Thank you!Though I'd like to cast my vote  for overexpressing SF2/SRSF1 in the next paper!Reviewer #3: The authors have satisfactorily addressed most of my comments. Some suggested experiments were not performed, such as SF2/SRSF1 overexpression, which was also proposed by another reviewer. I understand their reasoning why it is more important to focus on down-regulation.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: NoneReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  20 Sep 2023PGENETICS-D-23-00514R1 TDP-43 pathology in Drosophila induces glial-cell type specific toxicity that can be ameliorated by knock-down of SF2/SRSF1. Dear Dr Dubnau, We are pleased to inform you that your manuscript entitled "TDP-43 pathology in Drosophila induces glial-cell type specific toxicity that can be ameliorated by knock-down of SF2/SRSF1." has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Soon after your final files are uploaded, unless you have opted out or your manuscript is a front-matter piece, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.Thank you again for supporting PLOS Genetics and open-access publishing. We are looking forward to publishing your work! With kind regards,Zsofi ZomborPLOS GeneticsOn behalf of:The PLOS Genetics TeamCarlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdomplosgenetics@plos.org | +44 (0) 1223-442823plosgenetics.org | Twitter: @PLOSGenetics


This reactionis of fundamental interest for precise lattice patterning, however,is not well understood. Herein, using van der Waals density functionaltheory (vdW-DFT) incorporating spin-polarized calculations, we findthat O3 strongly physisorbs on graphene with a bindingenergy of −0.46 eV. It configures in a tilted position withthe two terminal O atoms centered above the neighboring graphene honeycombs.A dissociative chemisorption follows by surpassing an energy barrierof 0.75 eV and grafting an epoxy group on graphene reducing the energyof the system by 0.14 eV from the physisorbed state. Subsequent O3 chemisorption is preferred on the same honeycomb, yieldingtwo epoxy groups separated by a single C–C bridge. We showthat capturing the onset of spin in oxygen during chemisorption iscrucial. We verify this finding with experiments where an exponentialincrease in the density of epoxy groups as a function of reactiontemperature yields an energy barrier of 0.66 eV, in agreement withthe DFT prediction. These insights will help efforts to obtain precisepatterning of the graphene lattice.The exposure of graphene to O Epoxy groups alter the local density of statesof graphene, which can be used for stepwise tuning of band gap ofgraphene.12 When multiple epoxy groupsare grafted, they tend to form energy-minimizing clusters15 known to be responsible for the oxidative cleavage of graphiticlattice18 and the formation of vacancy defects.21 The formation of such defects, if controlled precisely, can beextremely attractive for application in molecular separation,24 enhanced water evaporation,25 sensing,27 sequencing,28 and power generation.29 In fact,exposure of graphene to O3 has become a promising way toprepare porous graphene membrane.32 However, the mechanismof the formation of the epoxy group from O3 is not wellunderstood. An accurate description of underlying energetics wouldallow one to model and predict the evolution of epoxy groups and relatedclusters and will help efforts to precisely pattern graphene.Controlled incorporation of defects ingraphene by surface functionalizationis attractive for modulating its electronic,3 with grapheneand carbon nanotubes are scarce, and report O3 physisorptionenergy in the range of −0.2 to −0.3 eV.35 The study of Lee and co-workers on the interaction of O3 with graphene is the most comprehensive report so far.36 They used the local spin density approximation(LSDA) to understand the reaction of O3 with graphene.They found that the O3 orientation on top of the graphenestrongly influences its physisorption. However, the reported physisorptionand chemisorption energies were small, −0.25 and −0.33eV, respectively. Such moderate binding energies do not explain thestrong affinity of O3 toward graphene or carbon nanotubes,which is observed by experiments at room temperature.38Theoretical studies on the interaction of O39 we show that O3 physisorption on graphene is quite strongcorresponding to a high binding energy of −0.46 eV. The interactionis uniquely maximized when O3 approaches graphene in atilted , face-downorientation and when the terminal O atoms of O3 are positioneddirectly above neighboring graphene honeycomb. Ozone then goes throughdissociative chemisorption to functionalize an epoxy group on graphenesurpassing an energy barrier of 0.75 eV. The resulting chemisorptionbinding energy is high, 0.60 eV. We show that the second chemisorptionevent preferentially takes place on the same honeycomb, and the resultingpair of epoxy groups is separated by a single C–C bridge. Finally,we carry out experiments probing reaction of O3 with graphene.Monitoring the coverage of the epoxy group on graphene as a functionof temperature, we calculate an energy barrier of 0.66 eV, in agreementwith the DFT calculations.Herein, using vdW-DFT calculations,3 adsorption on graphene, vdW-DFT calculations were performed usingthe Quantum ESPRESSO package.41 A supercell made of7 × 7 periodic unit cells of graphene was used to ensure thedecoupling of in-plane molecule–molecule interactions. TheBrillouin zone was sampled with uniform 4 × 4 × 1 unshiftedk-point grids. A vacuum region of 20 Å was used in the z-direction to avoid interactions among the periodic replicas.An energy cutoff of 90 Ry was used for the plane wave expansion ofthe wave functions. A kinetic energy cutoff of 720 Ry on the chargewas used together with ultrasoft pseudopotentials.43 In order to include the contributions of dispersion interactions,vdW-DF239 approximation was used. For comparison,some adsorption energies were also calculated with the more recent,semiempirical vdW-DF3 approximation.44 Convergencethresholds of 2 × 10–6 Ry and 10–4 Ry/Bohr for the total energy and forces, respectively, are used.To investigate O3 intoepoxy and oxygen, spin-polarized calculations were performed. Thereaction mechanisms and transition-state energy barriers were studiedwith climbing image nudged elastic band (NEB) calculations. The NEBsimulations were carried out using a 6 × 6 supercell and a uniform4 × 4 × 1 unshifted k-point grid to sample the Brillouinzone. The spin of the system is changed from the spin-neutral (O3) to a spin-polarized (epoxy + O2) at the transitionstate.For studying the dissociative chemisorption of O2 andH2 at 1000 °C for 30 min. The next step involved introducingCH4 and H2 gases with a 3:1 volume ratio intothe reactor to initiate graphene growth. The reactor was maintainedat 460 mTorr for 30 min during the growth process. Once the synthesiswas complete, the reactor was rapidly cooled down to room temperature.To synthesize single-layer graphene,low-pressure chemical vapor deposition was used on annealed Cu foilwith a thickness of 50 μm and a purity of 99.9% (Strem). TheCu foil was first annealed in an atmosphere of CO3 generator yielding O3 concentrationof 150 g/Nm3 in O2. The samples were heatedto temperatures ranging from 290 to 329 K for functionalization. Thereactor pressure was kept at 1 bar during each oxidation process.Following the functionalization, O3 was purged by flowingAr. Finally, the samples were transferred to the ultrahigh vacuum(UHV) chamber of X-ray photoemission spectroscopy (XPS) for the characterizationof the functional groups.Freshly synthesized graphenesamples were functionalized by placing them in a quartz tube connectedto an OHigh-resolution XPS measurements were conductedusing a Kratos Analytical Axis Supra instrument equipped with themonochromated Kα X-ray line of an aluminum anode. The pass energyand step size used in this study were 20 and 0.1 eV, respectively.To prevent charge buildup, the sample was ground during measurements.The binding energy data were used without any corrections, and dataprocessing was performed using CasaXPS software. Background subtractionwas performed by using the Shirley method.3/graphene system was allowed to relax. O3 wasrelaxed in all directions to minimize the potential energy. C atomsin graphene were also relaxed to adjust their positions dependingon the interactions with O3. To extract the lowest energyO3/graphene configuration, we explored several O3 configurations and positions on top of the lattice   was the most optimal configurationthat minimized the energy as it allowed improved interaction for allthree O atoms. Therefore, we fixed the tilt angle to 79° forthe two tilted configurations. Finally, the relative distance betweenthe central O of O3 and C atoms in graphene honeycomb wasvaried along the xy plane to probe three most probablepositions  1b and twizontal) 1c and faizontal) 1d] confi3-graphenedistance (z) for the total nine configurations  are reported in z position of the O atom that is nearest to graphene. In all cases,as O3 is brought closer to graphene from 4 Å, theenergy of the system first decreases and then increases. This is attributedto the increasing attractive interactions at first followed by thedominance of the repulsive dispersion interactions. Irrespective ofthe three orientations, the minimum energy is always obtained forthe position 1 [red curve in the interaction energy plot, 3 aresurrounded by six C atoms of the honeycomb, which maximizes the interaction.In other words, the number of nearest neighbors (C atoms) for theterminal O atoms are highest in position 1, which ensures maximuminteraction. Therefore, position 1 is the most favorable.The interaction energy as a function of O3 is 3 Å away from the graphene lattice whilethe middle O is 3.3 Å away from the lattice (Supplementary Note S2). The resulting free energy correction is +0.09 eV,bringing the free energy of adsorption to values of −0.37 eV(vdW-DF2), −0.47 eV (vdW-DF3-OPT1), and −0.49 eV (vdW-DF3-OPT2).Finalconfigurations are ranked as following in the order of theirpreference at the most optimal position, i.e., position 1: tiltedΛ (−0.46 eV) > tilted V (−0.44 eV) > horizontal(−0.42 eV). Based on this, we assign the physisorbed stateto position 1 in a tilted Λ configuration with physisorptionbinding energy of −0.46 eV. In this position, the terminalO of O lattice 3a. When 35 and can explainthe observed strong affinity of O3 toward graphene. Weattribute our results to improved capture of long-range interactions,which allows maximization of interaction with all three O atoms. Thisperhaps also explains why the optimal configuration is close to planarwith a slight (11°) tilt. In general, nonlocal density functionals,including the vdW-DF2 functional, are considered promising to modelvdW interactions. They include nonlocal, long-range correlation interactionsdirectly in the form of the potential of the density functional. Todemonstrate the importance of vdW approximations, we also calculatedthe adsorption energy at the Perdew–Burke–Ernzerhof(PBE) level, which is less accurate in capturing the long-range interactions.PBE calculations indeed predicted a lower binding energy, −0.37eV .While it also predicted the Λ configuration, O3 isnot tilted  and the adsorptionheight is larger, 3.3 Å similar to what has been reported inthe literature.36 We also studied the effectof the supercell size. A 3 × 3 supercell used in the literatureis not large enough to completely remove the interactions betweenO3 molecules in neighboring images. Indeed, the bindingenergy increases and converges to −0.46 eV when the supercellsize is increased from 5 × 5 to 6 × 6 and then to 7 ×7 periodic unit cells . Takentogether, these findings underscore the importance of consideringnonlocal density functionals, optimal configuration (tilted Λin this case), and large enough supercell size for accurate modelingof physisorption interactions.The predicted physisorption energy in our study (−0.46 eV)is higher than reported by the literature3, we carried out climbingimage NEB calculations to understand the chemisorption of O3 on graphene . At this point, the net energy of the system startsto increase because of an increased repulsive interaction. The distancebetween the terminal O and C continues to decrease (images 4 and 5)until it reduces to 2.3 Å where a covalent O–C bond isformed (image 6). The net energy of the system is the highest at thispoint (0.3 eV). Therefore, we assign this as the transition statecorresponding to an energy barrier of 0.75 eV from the physisorbedstate.In the physisorbed tilted Λ configuration, terminalO atoms are closest to the graphene lattice with a distance of 3 Å.Chemisorption proceeds with the approach of a terminal O toward theC atom of the graphene lattice . When O2 is detached from the terminal O of O3, it prefers tobe magnetic (see discussion on spin-polarization later). Therefore,to accurately capture the system energy, we performed spin-polarizedcalculations after O2 detachment. The energy of the systemreduces significantly at this point. Subsequently, O2 driftsaways from the epoxy group (images 8 and 9) and physisorbs on topof the graphene lattice in a horizontal configuration (image 10).This reduces the energy of the system, compared to the physisorbedstate, by 0.14 eV. Therefore, we assign a chemisorption binding energyof −0.6 eV. With vdW-DF3, the chemisorption binding energywas found to be −0.69 eV (−0.66 eV) using the OPT1 (OPT2)variant of the functional. Moreover, the free energy correction is+0.12 eV (Supplementary Note S2), bringingthe free energy of adsorption to values of −0.48 eV (vdW-DF2),−0.57 eV (vdW-DF3-OPT1), −0.54 eV (vdW-DF3-OPT2).After reaching the transitionstate, the bond lengthbetween the central O of ONext, from the physisorbed Ographene 2. Detail3 is 3 Å away from the lattice . A projecteddensity of states (PDOS) analysis is provided in Figures S7 and S8 of the Supporting Information to confirmthe physisorbed/chemisorbed natures of the initial/final states, respectively.The physisorbed and chemisorbed states are comparedin  lattice 3a. In th lattice 3b. This 3) to a spin-polarized system (epoxy + O2). To reveal theimportance of the spin-polarized calculation at the transition state,we also carried out the NEB calculations using a spin-neutral system. In this case, chemisorptionis not favored compared to physisorption with an unfavorable bindingenergy. This underlines the importance of capturing the onset of spinin the O orbitals during the transition state for the chemisorptionof the complexes of O3.As discussed above, during the transition statefor chemisorption,the system undergoes a change from a spin-neutral system  is lower thanthat in the para configuration (1.0 Å).In the first configuration, the epoxy groups areseparated by asingle bridge site and are located in the same honeycomb. We referto this as the meta configuration 4a. In th3 will prefer to chemisorb wheneverit can overcome the energy barrier of 0.75 eV. The energy difference(0.14 eV) between the physisorbed and the chemisorbed states yieldsa large equilibrium constant  between the two statessuch that most of the sites on graphene would be in the chemisorbedstate at the equilibrium.The net lower energyof the chemisorbed state compared to the physisorbedstate indicates that Oc, can be predicted by the reciprocal of thechemisorption rate constant, kp→c, which in turn, can be approximatedby the Eyring equation .The chemisorption time scale, τequation 1:1where 3-functionalized graphene samples at various temperatures. The epoxy group coverage was analyzedby high-resolution XPS . For lower reaction temperatures (17 and23 °C), we used a longer (12 h) reaction time to obtain a clearC–O peak in XPS for quantitative analysis. For samples preparedat or above 34 °C, 1 h of reaction was enough to obtain a clearC–O peak. The resulting coverage of epoxy group, indicatedby the C 1s peak at 286.3 eV, was divided by the reaction time toobtain coverage rate, θ. We observed that θ increasedexponentially in the probed temperature range from 0.01 monolayer/hat 17 °C to 0.016, 0.071, 0.123, and 0.185 monolayer/h at 23,34, 43, and 56 °C, respectively  where most site on graphene are availablefor reaction, θ is expected to be proportional to the chemisorptionrate constant, kp→c, as per θ is driven by chemisorption whenever thechemisorption energybarrier, Ep→c from the temperature-dependent data on θ,we plotted ln θ versus 1/T (Ep→c of 0.66 eV, which agrees well with the vdW-DFT predictedenergy barrier of 0.75 eV.To extract rsus 1/T 5b. The o3 with grapheneusing vdW-DFT. We find that O3 strongly physisorbs witha binding energy of −0.46 eV, maximized by the interactionof all three O atoms in the titled configuration with the nearestO atom 3 Å away from graphene. Chemisorption follows this formingepoxy group on graphene. The small energy barrier of chemisorption,0.75 eV, makes O3 highly attractive to functionalize grapheneeven at room temperature, which is also demonstrated by experimentswhere a coverage rate of 0.071 monolayer/h is realized at 23 °C.From a computational point of view, we demonstrate the importanceof nonlocal density functionals, which captures long-range interactionsand improves the understanding of O3 sorption energeticsand related configurations. We also demonstrate the importance ofthe spin-polarized system at the onset of the transition state whereO2 dissociates from O3 to correctly capturethe chemisorption energy. These calculations highlight that O3 is extremely attractive to functionalize graphene even atroom temperature and can evolve as a popular tool to pattern the latticeof graphene for several applications relying on functionalized graphene.Overall, we investigate the reaction ofO


The superlattice obtained by aligning a monolayer graphene and boron nitride (BN) inherits from the hexagonal lattice a sixty degrees periodicity with the layer alignment. It implies that, in principle, the properties of the heterostructure must be identical for 0° and 60° of layer alignment. Here, we demonstrate, using dynamically rotatable van der Waals heterostructures, that the moiré superlattice formed in a bilayer graphene/BN has different electronic properties at 0° and 60° of alignment. Although the existence of these non-identical moiré twins is explained by different relaxation of the atomic structures for each alignment, the origin of the observed valley Hall effect remains to be explained. A simple Berry curvature argument is not sufficient to explain the 120° periodicity of this observation. Our results highlight the complexity of the interplay between mechanical and electronic properties in moiré structures and the importance of taking into account atomic structure relaxation to understand their electronic properties. Here, the authors report the unexpected observation of different electronic properties of bilayer graphene/boron nitride heterostructures at 0° and 60° twist angles, showing the complex interplay between lattice relaxation and the electronic properties of moiré structures. This is caused by the combination of two effects: (i) a long-wavelength geometric interference pattern, called a moiré pattern, which effectively acts as a periodic superlattice, and (ii) a local enlargement of the lattice constant of graphene to match the one of BN at the inner part of the moiré pattern, leading to a local commensurate state. Outside of the commensurate areas the accumulated stress, due to the stretching of the lattice constant, is released in the form of out-of-plane corrugations where the stacking order changes rapidly in space5. These corrugations have the same periodicity as the moiré pattern. For monolayer graphene, both the long-wavelength pattern and the commensurate state are observed every time one of the layers is rotated by sixty degrees.When the crystallographic alignment of monolayer graphene and BN is almost perfect (close to zero degrees between layers), the electronic, mechanical and optical properties of graphene are strongly modified5. In monolayer graphene/BN the breaking of inversion symmetry has been proposed as the origin of the opening of an energy gap at the charge neutrality point (CNP)6 and non-trivial quantum geometry characteristics of the electronic band structure7. However, very little is known about how the graphene/BN alignment affects systems with more than one layer, such as bilayer graphene.The commensurate state creates an imbalance of the interaction that the carbon atoms have with the BN substrate breaking the sublattice symmetry4 made of Bernal stacked bilayer graphene and BN. Our measurements reveal distinct behaviors for 0° and 60° which we attribute to different electronic band structures generated by different atomic displacements inside the moiré superlattice. However, the observation of the valley Hall effect, only present for 0° of alignment remains to be explained given that the current theoretical model fail to explain this hundred and twenty degrees periodicity.Here, we demonstrate that the electronic properties of the commensurate state in a bilayer graphene/BN heterostructure have a hundred and twenty degrees periodicity. We present experimental electron transport measurements in dynamically rotatable van der Waals heterostructures4, with the improvement of having a pre-shaped local graphite gate. The latter controls the carrier density only in the central area of our device, and has the same dimensions as the BN structure used to create the moiré pattern. It is important to mention that the bottom BN and graphene layers are intentionally misaligned, to more than 10°, to avoid the formation of a double moiré10. The carrier density of the external parts of graphene is tuned by the global Si gate, acting effectively as a tunable contact resistance11. The angular alignment of the bilayer graphene/BN heterostructure is controlled in situ by means of a pre-shaped BN handle deposited on top of graphene. This handle can be rotated by applying a lateral force with the tip of an atomic force microscope (AFM), Fig. 2V−1s−1 for intermediate densities ± 0.65 x 1012 cm−2 at T < 10 K. The mean free path was calculated to be between 1.2 μm and 2 μm for the same carrier density and temperature range . In this case all occupied states in the valence band contribute to the valley Hall effect23. Using the saturation value of the non-local resistance, dashed horizontal line of Fig. lv = 1.5 μm, in good agreement with previous reports15.Since the Fermi energy is at the CNP, and the temperature of the system is much lower than the energy gap, the valley Hall conductivity is maximal having a value of entclass1pt{minimaπ/2. For T > 40 K we do not have a clear picture of why we observe a nearly quadratic behavior, we attribute this to a mixture of regimes where both the valley Hall effect and ohmic response compete. This regime needs more experimental and theoretical investigation.This behavior, where the non-local signal is independent of the local response, is characteristic of a system where the valley conductivity is larger than the local conductivity, which implies a fully developed valley Hall effect, where the Hall angle becomes RNL ∝ ρ0.9. This behavior is consistent with an ohmic contribution RNL = 4ρeπL/W−/π, and can be adjusted by using only the geometry of our sample, as is expected given its van der Pauw geometry. In contrast with the 0° of alignment this linear behavior can be observed over the full temperature range. This striking difference of the development of the valley Hall effect at 0° but absent at 60° reveals that the consequences of the moiré patterns for the two alignments are non-identical. Changing the alignment further to 120° restores the signatures of the valley Hall effect  and that it will modify the trigonal warping of the electronic band structure. Further combinations of scanning gates and electron transport will be necessary to clarify our observation.Interestingly, neither the valley Hall effect, observed at 0°, or the ohmic behavior, observed at 60°, are reproduced in the fully misaligned case, 30°, Fig. 26, the presence of misaligned hBN substrate induces a small crystal field (≈15 meV/nm) in bilayer graphene, leading to a small correction on the simulated bandgap. This correction was added in calculations of Fig. Dxy, clearly shows that, for the layer that is closer to the BN (layer 1), there is an almost circular symmetry around the center of each moiré superlattice (marked by the pink dashed lines). On the other hand, the second layer shows a breaking of this symmetry into a 2π/3 rotational symmetry. The in-plane atomic displacement of the second layer is also at least one order of magnitude smaller than for the first layer. Additionally, we can see that the in-plane atomic displacement on the second layer is larger in the case of 0°. The differences in the stretching of each layer results in the spatial variation of stacking structure of the bilayer graphene  as illustrated in Supplementary Fig. To understand why these two angular alignments give rise to different behaviors we investigated the in-plane atomic structural relaxation of each layer, Fig. 6. Then the carbon atoms of layer 2 will be sitting on nitrogen atoms. We can see in Fig. The difference of the in-plane atomic structure relaxations for 0° and 60° can be traced back to the Bernal stacked configuration, see Fig. 5. These corrugations are transmitted to the second layer and can be observed in the height sensor of our AFM measurements (PeakForce mode) of a different bilayer graphene/BN aligned sample, Fig. λ ~ 14 nm is clearly observed. Note that the asymmetry on the AFM image and large value on the height of the deformation is an artifact given by the width of the AFM tip being comparable to the size of the features we want to measure (~5 nm). This confirms the existence of a commensurate state and the transmission of the corrugations to the second layer in aligned bilayer graphene/BN heterostructures, supporting our numerical simulations.As we mentioned before, the stress generated by the commensurate state is released in the form of corrugations, as in the case of monolayer graphene27 this represents an unintentional displacement field of ~0.1 V/nm. This is not surprising since our devices do not have a top gate to screen external doping deposited on top of the device. Taking into account this unintentional doping and the atomic structure relaxation we calculated the electronic band structures for 0° and 60°, Fig. The different atomic structure relaxation of the layers results in different electronic band structures for 0° and 60° of alignment  on graphene, our simulations consider a simplified atomic system consisting of a bilayer graphene coupled to a flat monolayer h-BN. The lattice mismatch (~1.8%) between BN and graphene is taken into account. The atomic structure is optimized until all force components are <1 meV/atom. After relaxation, the electronic structure is computed by constructing and solving the corresponding pz tight-binding Hamiltonian36. The large tight-binding Hamiltonian matrix of moiré structures is diagonalized using conventional eigenvalue calculations of large-sparse matrices.The atomic-structure relaxation of h-BN/graphene layers is obtained using molecular dynamics with classical potentials. In particular, intra-layer forces are computed using the optimized Tersoff and Brenner potentialsSupplementary InformationPeer Review File


Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural language processing has been used in some clinical settings and proven useful, the system developed specifically for the Japanese language using this method has not been validated by research. The purpose of this study is to develop a novel drug information provision system for Kampo medicines using a natural language classifier® (NLC®) based on IBM Watson.The target Kampo formulas were 33 formulas listed in the 17th revision of the Japanese Pharmacopoeia. The information included in the system comes from the package inserts of Kampo medicines, Manuals for Management of Individual Serious Adverse Drug Reactions, and data on off-label usage. The system developed in this study classifies questions about the drug information of Kampo formulas input by natural language into preset questions and outputs preset answers for the questions. The system uses morphological analysis, synonym conversion by thesaurus, and NLC®. We fine-tuned the information registered into NLC® and increased the thesaurus. To validate the system, 900 validation questions were provided by six pharmacists who were classified into high or low levels of knowledge and experience of Kampo medicines and three pharmacy students.The precision, recall, and F-measure of the system performance were 0.986, 0.915, and 0.949, respectively. The results were stable even with differences in the amount of expertise of the question authors.We developed a system using natural language classification that can give appropriate answers to most of the validation questions.The online version contains supplementary material available at 10.1186/s12911-023-02230-3. The widespread use of the Internet has enabled instant access to vast amounts of information related to medications; however, it is necessary to assess the correctness of the information that is obtained . In addiIn Japan, traditional Kampo medicines (漢方薬) are widely used –7. TradiCenters for drug information, where experts are available to answer questions, have been established in each country to help healthcare providers obtain reliable drug information , 14, 15.The purpose of this study is to develop a drug information provision system for Kampo medicines using an NLC® based on IBM Watson. The system developed in this study is a prototype built on limited basic information, and it is highly versatile in actual clinical practice. The performance of this system was evaluated with 900 validation questions provided by six pharmacists (classified into high or low levels of knowledge and experience with Kampo medicines) and three pharmacy students.The target Kampo formulas were 33 prescriptions listed in the 17th revision of the Japanese Pharmacopoeia . The infThe system developed in this study classifies natural-language input questions about the drug information of Kampo formulas into preset questions and then outputs the respective preset answers for these questions Fig. . For devNLC® is IBM’s application programming interface that applies deep learning to NLP to understand the intent of input questions and classify them to predefined answers . The claThe following two steps were necessary in developing the system:indications of Kakkonto ”, “I want to know the treatment of pseudohyperaldosteronism”); questions in which the name of the answer section was not directly included ; and questions that required the system to refer to one section of multiple medications throughout . The total number of answers in the whole NLC® was 2,587.We created and registered answers and corresponding training data questions to the NLC® based on the data from the information sources using the following procedure: Before creating the questions, we divided information on the package inserts and the Manuals into 13 and 18 sections, respectively, and used the information written there as the answers. We instructed the QA authors to prepare three types of questions: questions in which the name of the answer section was directly included in the question  and six downstream NLCs® (Category NLC®) Fig. . Users cThe NLC® calculates the confidence rate in the answer to the input question. The calculation method of the confidence rate is not disclosed, but it considers the similarity between the input question and the registered question. It is designed such that the sum of all the confidence rates of the candidate’s answers will total to 100%. Our system allows setting a threshold value of a confidence rate. The system answers “I do not have an answer” if the confidence rate is below the threshold value. When users input a question for which no question is registered, our system cannot give a correct answer and outputs a wrong answer. This is because the system outputs the registered answer with the highest confidence rate. If a user asks a question that has a registered answer in the system, it will find a registered question with a high degree of similarity that will likely have a higher confidence rate. In contrast, if a user question has no registered answer, the likelihood of finding a registered question with a high degree of similarity is low and the confidence rate is likely to be low. When the system receives user questions for which no registered answers have been input and the confidence rate is below a certain threshold level, having the system say “I do not have an answer” decreases the likelihood of wrong answers being output.In system development using NLC®, it is time-consuming to increase the number of registered questions corresponding to one answer. Registered questions are created by humans, so the more data the system has, the more time-consuming this becomes. To solve this problem, we created a thesaurus. Fluctuations in natural language expressions can be absorbed by NLC®. However, if we create a system using only NLC®, questions with many synonyms need to be registered, the number of questions will be huge, and individual confidence rates will decrease. Therefore, we decided to perform synonym conversion before questions are input to the NLC®. In the thesaurus, 369 keywords corresponded to 1,429 synonyms. The system used the thesaurus to convert the words in the user questions into keywords. The thesaurus was created in the same way as the questions and answers registered with NLC®. It was constructed by three pharmacists  and seven pharmacy students. First, we extracted words from the package inserts and the Manuals. Colloquial expressions describing symptoms were extracted from a book in Japanese regarding patients’ subjective descriptions of side effects [For the system to perform optimally, the answers to the questions entered into the system must match the answers given by the experts after reading the questions. To ensure that the system was optimized, we created multiple questions that might be asked in clinical settings and input them to check whether the system found the correct answers. If it did not, we rechecked the questions in the NLC® and the synonym dictionary. In addition, the developers discussed why the correct answer could not be derived. We modified the words of the registered questions in NLC® and expanded the thesaurus several times by increasing the number of phrasings of questions in the NLC®, registering words used in the questions to the synonym dictionary, and registering words in the thesaurus as one of the wordings of the questions in the NLC®.Validation was conducted to investigate the performance of the system. The 900 validation questions were based on what pharmacists may want to know about using Kampo medicine in a clinical setting, including indications, adverse reactions, and drug interactions. Nine additional people (pharmacists and pharmacy students) who had not participated in any of the previous steps prepared 100 validation questions each. To ensure that the performance of the system was not biased by the pharmacists’ knowledge and experience of Kampo medicines, the nine question-authors were divided based on their knowledge into three groups of three people each: (1) pharmacists with advanced knowledge and experience of Kampo medicines (Advanced group); (2) pharmacists with basic knowledge and experience of Kampo medicines (Basic group); and (3) pharmacy students after practical clinical training based on previous studies . The phaTo confirm the threshold of the confidence rate of the system, we drew a receiver operating characteristic (ROC) curve using a threshold. The threshold value at which the value of “Sensitivity-(1-Specificity)” is maximized was obtained. The system was set to answer “I do not have an answer” if the confidence rate was less than this threshold. Additionally, four thresholds with confidence rates of 50%, 60%, 70%, and 61%, derived from the ROC curve analysis, were considered.The validation questions were classified by the administrator based on whether the question had a correct answer in the system. The questions were put into the system and the confidence rate and answers  were recorded. After all 900 questions were input, the precision, recall, and F-measure were calculated for all validation questions and each group.The true positive (TP), false negative (FN), false positive (FP), and true negative (TN) were calculated as follows: TP was defined as the number of questions for which there was a correct answer in the system and the system gave the correct answer. FN was defined as the number of questions for which there was a correct answer in the system, but the system could not give the correct answer or replied with “I do not have an answer.“ FP was defined as the number of questions that had no correct answer in the system and the system should answer “I do not have an answer” but the system gave some wrong answer. TN was defined as the number of questions for which there was no correct answer in the system and the system was able to answer “I do not have an answer.”Precision is the quotient of TP divided by the sum of the TP and FP. Recall is the quotient of TP divided by the sum of TP and FN. The F-measure is the harmonic mean of precision and recall.The system was completed in July 2021.Table If there is an answer in the system to the question (Yes) and the system answer is the expected one (〇), the system answer is judged as correct (TP). If there is an answer in the system to the question (Yes) but the system answer is unexpected (×) or “I do not have an answer” (△), the result is judged as incorrect (FN). If there is no answer to the question in the system (No) and the system answer is “I do not have an answer” (△), the result is judged as correct (TN). If there is no answer in the system to the question (No) but the system gives another one (×), the result is judged as incorrect (FP).To confirm the threshold of confidence rate, we drew an ROC curve Fig. . The besThe results of the system are shown in Fig. We developed a novel drug information provision system for Kampo medicines using NLP. The developed system showed high performance with precision, recall, and F-measures of approximately 0.9 for all questions created by pharmacists regardless of their level of expertise in Kampo medicines. This NLC-based chatbot system is considered usable by pharmacists with any level of knowledge and experience because of its superior performance.Although Google search is the information search tool most frequently used by medical students  and is aNone of the previous studies that used IBM Watson® to develop a system to provide drug information validated the resulting system , 20. In Moreover, there have been no previous studies using the same Japanese language NLP used in this system. Previous studies using other Japanese language NLPs were meant to detect adverse reactions in electronic medical records, drug histories, articles, and social media –46. The The system worked well at the threshold value of 61% based on the ROC curve, with acceptably high levels of precision, recall, and F-measure. If we increased the threshold of the confidence rate, the system answered questions with “I do not have an answer.” This increased the number of FNs and decreased the recall because FN is included in the denominator. Contrarily, the precision values tended to become higher with the higher threshold of confidence rate because FP included in the denominator was decreased. These differences were, however, not significant.The two main reasons that cause the system to give incorrect results even for the validation questions with correct answers are Table  problemsAdditionally, ambiguous or indirect wording of a validation question may lead to a wrong answer including FN or FP depending on the threshold of the confidence rate. For example, the validation question, “Can I concomitantly use Hochuekkito in patients who are taking antihypertensive agents?” or “How many grams of licorice can I take in a day when I take Yokukansan?” did not lead to a correct answer because there is no yes/no answer or such direct description on the official information we referred to in the present study.The process inside the NLC® is a black box; hence, similar questions could not be fixed to lead to correct answers. Watson® has knowledge originally trained by IBM and then trained further with data we provided. Because the learning model is created by integrating those data, the user cannot completely control Watson®’s behavior. As an example, consider “Misclassification by NLC®”: even if two questions and answers are learned that are distinguishable from our point of view, there is a possibility that Watson® will interpret them as identical questions and answers with the same meaning. To solve this problem, we are currently developing another artificial intelligence engine that enables us to expand the corpus and control. We hope to use the corpus created in this study to evaluate the usefulness of the newly developed QA system. Our data set is also available from the authors upon reasonable request so that other researchers can evaluate other QA systems.Some validation questions and answers covered more than one category or Kampo formula name; the information on the two medications was in different categories. NLC® cannot classify questions, and it cannot answer questions about information outside the system if it is asked as a closed question. To solve this problem, we plan to adopt a keyword matching system that searches for keywords in the question text; if the keywords are included in the text, the system will display responses tied to predefined keywords. By including a keyword matching system, it will be possible to display the list of indications based on the keyword “indications” and treat the query as an open question on indications for the Kampo formula. Question text structure and ambiguous questions are difficult even for experts to answer. Thus, there are limitations to answering these questions using an NLC®-based system.This study had several limitations that should be addressed in future studies. First, the system we developed can only answer registered questions. The system administrator must add new questions and answers whenever new questions come in. As with information retrieval on the Internet, prior studies have shown that it is necessary for application users to have the ability to judge whether the application is providing the correct information , 47. OurWe developed a novel drug information provision system for Kampo medicines using NLP. The precision, recall, and F-measure were 0.986, 0.915, and 0.949, respectively. We developed a system that gives appropriate answers to questions using natural language, assuming a clinical situation. We plan to confirm the results in the clinical field in the future. Other sources, such as the classic textbooks, will also be included.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4


Excellent dispersion and uniform distribution of the 2 inclusion. Among the coating systems, throughout the immersion time, the graphene/Globally, researchers have devoted consistent efforts to producing excellent coating properties since coating plays an essential role in enhancing electrochemical performance and surface quality. In this study, TiO Corrosion is defined as the process of loss and destruction due to the chemical or electrochemical interaction between the metal substances and corrosive environments, which have resulted in the disintegration and deterioration of metal or their alloy materials ,2,3. TheIn order to control the corrosion-promotion activity of graphene, it is essential to obtain an insight into the mechanism of the corrosion-promotion occurrence . PreviouFurthermore, organic coatings can be easily fabricated on metal substrates, thus playing an essential role in inhibiting the corrosion process . For insxibility ,16. In axibility . In othexibility .In the year 2004, a new generation material, graphene, was first isolated by simple mechanical exfoliation . Graphenon atoms ,22,23. Fectively . Besidesectively . Moreoveectively . Furtherectively . To suppectively . Here, iectively ,15. For ectively . Here, iAt a very low weight percentage, the graphene and GO nanoparticles can be well dispersed and exfoliated in the polymeric matrix due to their high surface area and strong Van der Waals interactions . Howevercoatings . On the atalysis ,39. For ectively . Moreoveectively ,41,42. Bectively . Furthered water . Moreoveed water ,45. Henccoatings .Therefore, in this communication, the organic-inorganic based nanocomposite coating systems consisting of the acrylic-epoxy polymeric matrix, PDMS, 1 wt.% of graphene along with different loading rates of Without any further purification, all the listed chemicals were used as received from the suppliers to fabricate the hybrid polymer nanocomposite coatings. Acrylic polyol resin, denoted as A and its curing agent aliphatic poly-isocyanate resin (NCO) were purchased from Synthese, Selangor, Malaysia and Bayer Material Science, Germany, respectively. Epoxy resin, denoted as E, produced from bisphenol A and epichlorohydrin, was purchased from ASA Chemicals, Malaysia. This preparation was conducted to further investigate the effects of graphene nanofillers along with the inclusion of −1 at a resolution of 4 cm−1 and the data was recorded after 32 scans.FTIR analysis was performed to observe cross-linking and qualitative investigations. ATR-Nicolet iS10 Spectrometer  was utilized for this analysis in the range of 4000 to 400 cmBy using Mettler Toledo TGA Q500 equipment that runs dynamically from 30 °C to 800 °C at a heating rate of 10 °C/min under nitrogen gas with a flow rate of 20 mL/min, the TGA tests were employed to evaluate the thermal stability of the hybrid nanocomposite coating systems. The TGA apparatus was purchased from Perkin Elmer, Pyris Diamond, USA and all the TGA thermograms were analyzed by the TARe software, TA Universal Analysis, Version 4.7A.The UV-3101PC  UV-vis spectrometer in transmission mode at a wavelength range of 200 nm to 800 nm on medium-speed scanning was employed to analyze the degree of transparency of the coatings coated on the glass substrates. The objective of this analysis was to evaluate the percentage changes in film transparency after the inclusion of The WCA test was carried out to evaluate the hydrophobic performance of the developed nanocomposite coatings by using an optical contact angle (OCA) 15EC instrument. At different locations, 5 droplets of 5 µL distilled water were dispensed on the surface of the specimens, and the images of static WCAs were captured, respectively. An average of three readings were recorded, and the highest value was reported with less than 2° as a measurement error.The effect of the graphene/The anticorrosion properties of the composite coatings were analyzed by EIS. Gamry potentiostat  was used, and the sinusoidal voltage amplitude was set at 10 mV. Herein, a three-electrode cell system was used for investigation for 90 days, and the electrolyte was 3.5 wt.% NaCl. The electrochemical cell was developed by placing a 2.0 cm inner diameter poly (vinyl chloride) (PVC) tube vertically on the coated substrate surface. On the other hand, the uncoated section was a working electrode, while the platinum as a counter electrode, and the saturated calomel electrode (SCE) was a reference electrode. In the frequency range of 100 kHz to 10 mHz, the EIS analysis was carried out after placing the electrochemical cell in a Faraday cage to minimize the noise.The FESEM was utilized to examine the surface morphology and the homogenous distribution of the nanoparticles within the polymer matrix. All the free film nanocomposite samples were coated with platinum by using a platinum sputter coater  in order to minimize the charging effects. The FEI Quanta 450 FEG with SDD EDS detector up to 5 KV as accelerating voltage in the presence of low vacuum was utilized to characterize the coating films.The FTIR spectrometer was used to identify the appearance of the chemical functional groups of the graphene/400 cm−1 . The banectively . As a re280 cm−1 ,48. For ibration . In addi bending . Also, t680 cm−1 ,50,51. Aectively ,50,51. Fing band ,52. Here2 which is evidenced by the increase in the residue values. In other words, over the temperature range, some changes at each degradation occurred with the increment of the loading rates of coatings . Howeverarticles ,55. WithIn this study, the UV-Vis spectroscopy was used to evaluate the optical properties of the graphene/adiation . Alternao 800 nm . Howeverstigated . Despiteng films . In othee system . From Fie system ,60.As illustrated in o 99.75° . Interesectively . Furthervaluated . Here, ivaluated . Figure In addition, it can be clearly seen that the concentration of ted film .Interestingly, it is noteworthy to mention that at the highest hobicity . Furtherhobicity . To suppo of 1:9 . Alternaely 100° . In shorThe adhesion of the coating film to the metal substrate is considered to be an important factor in order to develop an intact coating system with excellent overall performance. In this analysis, the adhesion properties of the graphene/The corrosion resistance of the graphene-based hybrid polymer nanocomposite coating systems with different inclusion rates of Due to the good corrosion resistance of As the exposure time elapsed, no significant change or bend was observed in the slope of the Bode plot at low-frequency region for all the coating systems after 30 days of immersion, whereby the Interestingly, the After 90 days of immersion, as depicted in Interestingly, after 30 days of immersion, both However, after 90 days of immersion, The surface morphologies of all the graphene/ process . As mentIn general, the graphene nanoplatelets consist of an irregular shape, whereas the (<50 µm) . Additioeristics .Furthermore, the coating samples containing 0.5 wt.%, 1 wt.%, and 2 wt.% of At different loading ratios, the