dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparing epLsar and NewEpLsar The new epLsar parameter (NewEpLsar) offered in MountainsMap's SSFA module is calculated differently from the original epLsar parameter. Both parameters are calculated through a length-scale analysis, done on all extracted profiles of rotated versions of the surface, by default every 5° at the 1. 8 μm-Ivan Calandra et al. scale (i. e. the spacing of the points along these profiles)  (Scott et al. , 2006). The relative-length relL is the length of the profile at a given scale divided by the projected length  (Brown, 2013). At each angle , relL() is calculated with  = i, with  = 5° by default and i being the index of the rotated version of the surface. Then a polar mean vector is calculated with its norm equal to relL(i) and its angle . From this, cartesian coordinates are calculated for each angle: (5)   () = () cos(∆) (6)   () = () sin(∆) The sum of all vectors is calculated as: (7) 𝑆 𝑟𝑒𝑙𝐿 = ∑ 𝑟𝑒𝑙𝐿 𝑖 𝑁-1 𝑖=0 with N being the number of angles (N =  /).
6  and Table  3 ; compare also the plots "Python_analysis/plots/statistical_model_two_factors_filter_strong/treatment_pairs_epLsar. pdf" and "Python_analysis/plots/statistical_model_neweplsar_filter_strong/treatment_pairs_NewEplsar. pdf" on Zenodo https://doi. org/10. 5281/zenodo. 7219855). It should be noted that the two treatments pairs of the lithic dataset (BrushDirt and RubDirt vs. Control) are well discriminated by NewEpLsar, but the diets of the sheep datasets that could be differentiated using epLsar (grass vs. clover/clover+dust and grass+dust vs. clover/clover+dust) cannot be separated with NewEpLsar (Table  3 ). It therefore seems that, due to its new algorithm, this new parameter can be either more or less powerful to discriminate the treatments as compared to the original epLsar parameter, depending on the surfaces.
Implications These results imply that the analyses from both software packages are in general equally powerful to discriminate treatments. Although the results from both software packages look similar (Figs 1-3), our analysis demonstrates that the values from the two software packages cannot be directly compared. Neither of the software packages seems to allow more discrimination among the treatments within any dataset than the other (Tables  2 3 ): in some cases, Toothfrax better discriminates the treatments, while in others, MountainsMap is better on this aspect. Without calibration against a profile or surface with known (nominal) values for the parameters ("Ground truth" approach, e. g. Todhunter et al. , 2017 Todhunter et al. , , 2019 Todhunter et al. , , 2020)) , it is not possible to determine which software package is closer to the "true" values.
Unfortunately, such a calibration is currently impossible for these parameters. Most of these differences are likely due to the corrections and/or further developments of the algorithms in MountainsMap. Such adjustments were and are necessary, but they also introduced differences between the two software packages. Some settings can be adjusted in MountainsMap to make the results closer (but probably still not identical) to those from Toothfrax, although that does not make these results "better". There is therefore a compromise between accuracy/performance of the calculation and fidelity to Toothfrax. While the output produced by the new MountainsMap's SSFA module can be qualitatively compared to the output produced by Toothfrax, our results imply that the outputs cannot be directly combined into a single quantitative analysis.
This could prove problematic for the future of dental microwear texture analysis (DMTA) if studies using MountainsMap's SSFA module cannot use the extensive published data analyzed with Toothfrax. We therefore recommend re-analyzing raw surface data with MountainsMap (or any of its derivatives) before comparing with published Toothfrax data (see also discussion by  Arman et al. , 2016). Even though it is potentially computer-time-intensive, it requires little effort from analysts and seems to be currently the best option. Future updates of MountainsMap's SSFA module might tackle (some of) the discrepancies in the computation of the parameters. Nevertheless, the source of some these discrepancies is due to corrections and developments in the algorithms and, as such, will unlikely be reverted to the original versions.
Additionally, a new body of literature using the current MountainsMap's SSFA module is already being produced (see references in the introduction), and re-analysis of data will still be inevitable. It is therefore of utmost importance that original raw surfaces will be accessible for comparative studies and re-analysis in the future (see also  Arman et al. , 2016). Even though MountainsMap can replace Toothfrax (with the caveats mentioned above), the parameter Tfv is still not available in MountainsMap. So, for the time being, the original Sfrax software cannot be replaced to compute Tfv. Once MountainsMap's SSFA module incorporates Tfv, a comparison like the one presented here will be necessary to check the similarities between the original Sfrax and the future MountainsMap calculations, and potentially, the re-analysis of published data will be also required for this parameter.
Conclusions The results of our Bayesian modeling showed that Toothfrax and MountainsMap's SSFA module produce significantly different values for all SSFA parameters investigated (epLsar, Asfc, HAsfc9 and R 2 ); Smfc also seems to be different but it could not be statistically tested. This is to be expected from the differences in computation between the two software packages. Even though the discrimination power among the treatments (different diets for experimental animals and different cleaning procedures for lithics) are similar between the two software packages, the present results have important implications. Indeed, the majority of published data about dental microwear texture analysis (DMTA) has been produced by Toothfrax. If the data produced by MountainsMap are not directly comparable, analysts using MountainsMap (or any of its derivatives) cannot simply combine the data from both software packages, but instead need to re-analyze the existing raw surface data with MountainsMap if they intend to include published Toothfrax data into their analyses.
This requires some (computer-)time investment. This issue comes on top of an existing lack of standardization in acquisition and processing (e. g. Arman et al. , 2016; Calandra, Schunk, Bob, et al. , 2019). More problematic though is the fact that very few of the published studies have made the raw surfaces available to the research community. Therefore, we urge all analysts to share their raw surfaces with every published article, either as uncompressed SUR files, or even better for reproducibility as MNT files including the processing steps, as we did here (on Zenodo https://doi. org/10. 5281/zenodo. 6645445). Ideally, in the future, researchers will even upload the raw surface data directly with each publication. For already published raw data, as well as for future raw data, it might be helpful to first create a central repository to host all these surfaces, their associated data, metadata and paradata, considering a spectrum of open source public copyright licenses in industry and academia (e.
Differences in significance for NewEpLsar against epLsar from the two software packages. Only treatment pairs and parameters for which NewEpLsar disagrees with epLsar from at least one software package are shown. For all comparisons, see frame [55] in the notebook "Statistical_Model_NewEplsar_filter_strong" (folder "Python_analysis/code/" on Zenodo). See Table2for details on how to read the table and for abbreviations, and text for details on surface parameters and treatments.
Concerning the fractions from the Lg-L-EtOH extract, only the inactive ones (score ≤ 15) were considered so that the analysis was as discriminating as possible. This was made possible since the two selected extracts were prepared from the same plant. The samples (Lg-AS-MeTHF extract and its fractions 1-7, fractions 1 -2 and 6 -9 of Lg-L-EtOH) were analyzed by LC-HRMS 2 in positive and negative modes in order to cover as many compounds as possible. This approach also facilitated the identification of compounds detected in both active and negative modes. After processing the data on MZmine2, a total of 993 positive precursor ions and 4735 negative ones were detected in the samples for MS 1. The data obtained in positive and negative ionization were treated separately. Finally, an antibiofilm activity score was assigned to each sample and the area under the curve was calculated for each compound detected.
As a result, nine compounds correlated with antibiofilm activity were found, seven in positive mode (LUg1 to LUg7) and two in negative mode (LUgA and B). Molecular networks were constructed to partially or completely elucidate the structure of these nine ions, represented with their correlation scores and their identification from GNPS databases and/or from manual annotation using in silico fragmentation software MetFrag (Figure  3 ). Of the seven positive ions correlated with the bioactivity (Figure  3a ), the most correlated one called LUg1 (correlation score r at 0. 95, with p-value at 1. 1 × 10 -4 ) was detected at m/z 439. 3581 and a retention time (RT) of 18. 62 min. GNPS databases identified it as an [M-H 2 O+H] + ion with the calculated chemical formula C 30 H 47 O 2 corresponding to the pentacyclic triterpenoid betulinic acid (BA).
In the same network as BA, another triterpenoid matched with the database search: the betulin (C 30 H 50 O 2 ), displaying a structure close to BA with a primary alcohol instead of carboxylic acid function on the 28th carbon (Figure  S2 ). BA was available as commercial standard (Sigma-Aldrich, Saint Louis, MO, USA), and in order to confirm its identification, the HPLC-UV (210 nm) and targeted HPLC-MS/MS profiles of F4 and the Lg-AS-MeTHF extract were compared with the standard. Results showed a peak at the same retention time (Figure  S3a ) and a similar MS/MS fragmentation profile (m/z fragments and their relative intensity) (Figure  S3b ), which supported the BA identification hypothesis. Two other putative bioactive compounds highly correlated, LUg3 (m/z 297. 2426 at 16. 89 min) and LUg6 (m/z 279.
2319 at 15. 72 min), were identified in the GNPS database as epoxidized fatty acids, respectively, as [M+H] + of 12,13-epoxy-9-octadecenoic acid and [M-H 2 O+H] + 9,10-epoxy-12-octadecenoic acid. LUg3 was networked with three ions with the same m/z of 297. 2426, including a hypothetical stereoisomer (RT at 17. 1 min), [M-H 2 O+H] + 9,10-dihydroxy-12-octadecenoic acid (RT at 16. 95 min) and [M+H] + 9,10epoxy-12-octadecenoic acid (RT at 13. 63 min) (Table  S1 ). Lug6 was also networked with identified compounds on GNPS: as probably a stereoisomer ([M-H 2 O+H] + 9,10epoxy-12-octadecenoic acid, RT at 15. 44   The remaining bioactivity correlated ions were not identified within the GNPS database but through in silico fragmentation, either by direct comparison on MetFrag with their MS/MS spectra and/or by comparing the MS/MS spectra of their networked ions, thus helping to improve their identifications.
Seven other networked compounds matched with the GNPS databases and were identified as triterpenoïds (Table  S1 ). LUg5 (m/z 419. 2773 at 18. 78 min) showed a low number of peaks in the MS/MS spectrum, but an intense ion at m/z 207. 0995 (100% relative intensity) and another main one at m/z 335. 2196 (7. 5%). The calculated raw formula for the parent ion was C 23 H 40 O 5 Na (∆ m/z = 0. 965 ppm). Comparisons with databases gave no similarity with known natural compounds, suggesting the presence of a new compound. Finally, the last two compounds correlated in the molecular network with the negative ionisation mode, LUgA (m/z 295. 2281 at 15. 44 min) and LUgB (m/z 523. 3410 at 18. 62 min), shown in Figure  3a , which exhibited similarities with the ions already described. LUgA was the [M-H] -adduct of LUg6.
One µL per sample was injected. Column temperature was set at 30 • C. Mass spectra were acquired in a Q-Exactive Plus TM mass spectrometer (Thermo Fischer Scientific) equipped with a heated electrospray ionization (HESI-II) probe. Acquisition was performed using both positive and negative ionization modes, setting spray voltage at 3. 7 kV and 2. 8 kV, respectively, capillary temperature at 310 • C and probe heater temperature at 280 • C. The MS 1 scan range was 150-1200 m/z with a resolution at 70,000 (full width at half maximum (FWHM) at m/z 200). Each full MS scan was followed by data-dependent acquisitions (DDA) selecting the 5 most intense ions and acquiring MS 2 between 50 and 1200 m/z, with a resolution of 17,500 and a normalized collision energy (NCE) of 20%, 35% and 50%. Data were acquired in centroid format.
5 min, minimum relative height of 10%, minimum absolute height of 6E6, min ratio of peak top/edge of 1 and peak duration range from 0 to 0. 5 min. MS 2 scan pairing was set at the m/z range of 0. 02 Da and the RT range of 0. 1 min. Isotopic peaks grouper was applied with an m/z tolerance of 0. 0015 or 5 ppm, with an RT tolerance of 0. 2 min and a maximum charge of 2. Representative isotope was set on most intense. Features alignment step with Join aligner was performed with an m/z tolerance at 0. 0015 or 5 ppm, with a weight for m/z of 75% and a weight for RT of 25%. The RT tolerance was set at 0. 2 min. A final gap-filling step was performed with 10% intensity tolerance, 0. 0015 m/z or 5 ppm tolerance, 0. 2 min RT tolerance and with an RT correction. Exportation of data generated *. csv and *.
Molecular Networks Analysis Molecular networks were created using Global Natural Products Social molecular networking (GNPS, http://gnps. ucsd. edu, accessed on 21 October 2022). The MGF and CSV files of processed data on MZmine 2 were uploaded on GNPS. Files were used to generate an MS/MS molecular network using the GNPS Feature-Based Molecular Networking workflow  [48]. The precursor ion mass tolerance and the product ion mass tolerance were set to 0. 02 Da. Networks were generated using 5 minimum matched peaks and a cosine score of 0. 6. The library search options were set to minimum 5 matched peaks and a score threshold of 0. 6 without search of analogs. In complement to GNPS databases, in silico fragmentation software MetFrag was used with several molecular databases (Pubchem, Human Metabolome Database (HMDB), Coconut).
Predictions of active compounds were realized with workflow from Nothias et al. [31]  from an R-based Jupyter notebook available on GitHub, https://github. com/DorresteinLab oratory/Bioactive_Molecular_Networks (accessed on 21 October 2022). Briefly, bioactivity scores of antibiofilm inhibition at 50 µg•mL -1 from CVS tests were calculated to optimize disparity between samples with the Formula (3). Bioactive score = (I -SD) × 100 I max -SD Imax (3) where the standard deviation (SD) from each condition was subtracted to the percentage of inhibition (I). A bioactive score of 100 was attributed to the most active sample (I max ). For the other samples, scores were attributed proportionally relative to inhibition (%). A score of 0 was assigned to the samples with negative values. Scores were added on the spectral features table obtained with MZmine 2 workflow.
HPLC-MS/MS analysis was performed on a Waters system equipped with a time-of-flight XEVO™ G2 Q-TOF analyzer (Waters Corporation, Milford, MA, USA) with an ESI source in positive mode with the same chromatographic method as HPLC-DAD, and a 5 µL injection the samples. MS 2 targeted acquisition was set at m/z 439. 36 and 457. 36, corresponding, respectively, to the [M+H-H 2 O] + and [M+H] + adducts. Source was set at 120 • C at 3. 7 eV, collision energy at 40 eV and acquisition range at m/z 50-1000 with centroid mode. Data were analyzed using MassLynxTM software (V4. 1, 2013) from Waters.
Statistical Analyses All biological experiments were performed at least three times with triplicate for each condition. The Kruskal-Wallis test with Dunn's multiple comparisons test was applied to determine the statistical significance between obtained measures with treated biofilms and controls, using GraphPad Prism ® version 6. 01 (GraphPad Software Inc, San Diego, CA, USA). PCA and OPLS-DA tests on MZmine2 data including QCs were realized to check the method performance of LC-MS acquiring data, on SIMCA 14. 1 software (Sartorius, Goettingen, Germany).
at 16. 95 min), with the chemical formula C 23 H 38 O 4 Na (∆ m/z = 1. 169 ppm), the postanalysis attributed 37 out of 59 similar fragments on its MS/MS spectrum with theoretical MS/MS spectrum of [M+Na] + 2-arachidonoylglycerol (C 23 H 38 O 4 ). On the LUg2 cluster, five putative stereoisomers of [M+H] + monolinolenin (m/z 353. 2687 at 12. 63, 13. 07, 13. 33, 14. 53, 14. 64 min), [M+H] + 1-linoleoylglycerol (m/z 355. 2837 at 14. 08 min) and [M+NH 4 ] + 9,12,15-octadecatrienoic acid, 3-(hexopyranosyloxy)-2-hydroxypropyl ester (m/z 532. 3484 at 14. 61 min) were identified with the GNPS database (Supplementary Materials Table.
Acknowledgments: The authors would like to thank the staff from CBNSA for the scientific discussions and identification of the plants. The authors also thank the staff of the  Green space department of the university of Poitiers, Anaël Lachaise of the Joint Union of the Seugne Basin  and the staff of the  Technical Unit for Natural Environments of Grand Poitiers  for providing the plants. The authors wish also thank the Direction of the green spaces of the city of Poitiers, the  Regional Biodiversity Agency of New Aquitaine (ARBNA)  for the scientific discussions. Authors also would like to thank the  French society of medical mycology (SFMM)  for the awarding of a prize for the part of  Guillaume Hamion 's Ph. D. work. The authors thank  D. Debail  and  M. Maillet  for revising the English text.
Funding: This research was funded by  Region Nouvelle-Aquitaine  ( AAPR2020-2019-8408110-Research Grant ),  University of Poitiers and University of Limoges  ( AAP ARIC 2019 Research grant ) (France). This study was also supported by the  Bordeaux Metabolome Facility  and the  MetaboHUB  project ( ANR-11-INBS-0010 ), the 2015-2020 State-Region Planning Contracts (CPER),  European Regional Development Fund (FEDER)  and intramural funds from the  Centre National de la Recherche Scientifique  and the  University of Poitiers  (Poitiers, France). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Approximately 280 dental students handling about 12,000 patients each year. At the end of each patient visit, the procedures performed, and the skills acquired by the student are recorded in a structured way in an internally developed information system named ECHO. This information system then transmits the required billing information to the administrative department and allows the bi-annual evaluation of students. The aim of this study was to evaluate the suitability of reusing pre-doctoral student activity data from the ECHO for the development of QMs of caries risk.
Materials and Methods This retrospective study used student activity data stored in the ECHO information system of the Dental Department of Timone Hospital. This information system is registered in the CIL/AP-HM register under the number #2018-01 and contains, for each student, all the patients under care with the associated care activity and the validated skills. This study was registered in the RGPD/AP-HM register under the number #2021-61 and validated by the Ethics Committee of Aix-Marseille University (2021-06-03-11). Patients were included in the study via ECHO based on the 2021 Dental Quality Alliance specifications of the QM "Percentage of children under age 21 years who have caries risk documented in the reporting year" (CRD-CH-A). Inclusion criteria were being aged between 1 and 21 years and undergoing continuous follow-up defined by at least two visits over 12 months with no interval greater than 31 days between them.
05 and an accuracy of 90%  [12, 13]. Since we could not make an educated guess for sensitivity and specificity, we made the conservative choice of 50%  [12]. The estimated number of EDRs to be reviewed was 321. The EDRs were randomly selected from the list of included patients until the number 321 was reached. As only free text notes were available in the EDRs, these were reviewed by two calibrated evaluators to identify the reported PCR. Data were extracted from ECHO using PHP and MySQL scripts with CSV spreadsheet output. The PCR reported in EDRs were entered manually on a Microsoft Excel® spreadsheet. All analyses were performed with R for Windows® version 4. 1. 1. A two-tailed P value ≤0. 05 was considered statistically significant.
Measures The sociodemographic characteristics of the participants were collected from their medical records and included information on their age, gender, length of stay, and marital status. The medical records also provided information on the presence of chronic diseases and prescribed medications. The results of biological examinations performed in the previous few months to assess nutritional status were evaluated using data from the patients' medical records. Older patients with a serum albumin concentration <35 g/L and with C-reactive protein less than 15 mg/dL were considered to have an inadequate nutritional status  [31, 32]. Additional information was collected from the institution's dietician on the type of feeding route (oral or enteral), food texture (normal or modified texture: liquid, pureed, minced/moistened, or softened), and presence of dietary supplements.
Data Analyses 2. 5. 1. Hypotheses The first hypothesis was that malnutrition in older people with dementia might be related to their dental status. In particular, people presenting a low number of PFUs might be more likely to have a poor nutritional status than those with a good and functional dental status. Given the high prevalence of dementia in the target group, malnutrition and dental status were measured using anthropological indicators, MUAC, and the number of PFUs, respectively. This also involved corroborating whether the anthropometric measurement of malnutrition through MUAC is correlated with biological albumin levels. The second hypothesis was that the choice of the diet texture is influenced by the dental status of the residents.
Statistical Analyses Statistical analyses were carried out using IBM SPSS Statistics version 27. 0 software. The alpha error was set at 0. 05. Sociodemographic characteristics, nutritional status, feeding route and texture, and general and oral health condition were described for the entire sample-and were adapted to the severity of dementia. The mean and standard deviations were calculated for continuous variables, and the frequency and percentage were calculated for the categorical variables. Kolmogorov-Smirnov tests were used to assess the normality distribution of continuous variables. The Chi-square test of independence or Fisher's Exact test, and the Student's t-test or its non-parametric equivalent, the Mann-Whitney test, were applied to compare categorical and continuous variables respectively according to the severity of dementia.
Multiple logistic regression used on clinical data (Table  1 ) between BS and DP showed that DMFT in the BS group (4. 8 ± 4. 6) is significantly lower than that in the DP group (6. 6 ± 3. 9) (p < 0. 05). The presence of BSs in our population seemed not to be directly linked to gender or plaque indices. Clinical and metaproteomics sample group characteristics are statistically similar (Table  S1 ). 2. 2. Plaque Protein Distribution/Diversity. Plaque samples from DP and BS groups were analyzed using LC-MS/MS, and all spectra were searched against the Nextprot database to identify proteins from Homo Sapiens and against the SwissProt database to identify bacteria diversity with "Bacteria" as Taxonomy. Peptide FDR was controlled below 1%. 607 human proteins (4042 peptides) were identified in the BS group and 582 human proteins (3450 peptides) in the DP group.