dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Comparisons of fecal microbiota were made (i) between groups of zoo elephants (according to influence factors) and (ii) between the entire group of zoo elephants and the wild elephants, for which the taxonomic tables from both data sets were combined, and collapsed at genera level. α diversity metrics (observed features, Pielou evenness index, and Shannon entropy) and β diversity (Bray Curtis's distance) were compared, based on rarefied tables, using the Kruskal-Wallis test and the Permanova test, respectively. In addition, we determined the "core microbiota" among zoo and wild elephants, which refers to any set of microbial taxa, or the functional attributes associated with those taxa, that are characteristic of a host or environment of interest  (Neu et al. , 2021). We aimed to identify those taxa ubiquitous in almost all the samples from both datasets, and for this we used the method described by  Chong et al.
(2020). This approach identifies the specific taxa by considering their prevalence across the entire set of data, which includes all samples from wild and zoo elephants. Furthermore, this method also determines when the shared taxa are abundant across the dataset by analyzing their relative abundance. In our study, we considered core taxa those genera that were present in at least 70% of the samples. These analyses were performed using the web tool MicrobiomeAnalyst, accessible at: https://www. microbiomeanalyst. ca  (Chong et al. , 2020). The differences in taxonomic composition between groups were tested using a Kruskal-Wallis test as implemented in ALDEx2 method  (Fernandes et al. , 2013)  on R studio (R Foundation for Statistical Computing). The ALDEx2 method entails creating Monte Carlo samples of Dirichlet distributions to account for the uncertainty in the number of reads in each sample, then transforming the data using the centered log ratio (clr) transformation  (Aitchison, 1986) , allowing standard statistical methods to be used.
Bacterial co-occurrence networks and core association network Co-occurrence networks provide a visual representation of the relationships among bacterial genera in a dataset. In these networks, the "nodes" represent the individual genera, and the "edges" symbolize the relationships between them, indicating either positive (weight > 0. 50) or negative (weight < -0. 50) correlations. The significance of these correlations in this study were determined using the Sparse Correlations for Compositional data (SparCC) method  (Friedman and Alm, 2012)  in R studio (R Foundation for Statistical Computing). The topological features of the networks further capture the structure and connectivity of the microbial community. These features include the number of nodes and edges, which describe the complexity of the network; "modularity, " referring to the degree to which the network can be divided into separate communities or clusters of interacting genera; "average degree, " indicating the average number of connections per node; and "weighted degree, " representing the strength of connections.
Modeling of networks robustness To test the robustness of the networks, the connectivity loss of networks was calculated depending on removal of nodes by three methods [i. e. , random removal, high degree removal first and cascading removal (high betweenness centrality, recalculated at each removal first)]. The "Network Strengths and Weaknesses Analysis" ("NetSwan") script was used implemented in Rstudio  (Lhomme, 2015).
, 2019) , FTA cards' primary limitation was lower read counts compared to frozen dung boluses  (Budd et al. , 2020 ). An additional limitation lies in the different sequencing technologies used for the zoo and wild elephants, specifically Ion Torrent and Illumina, respectively. In our study, the Ion Torrent method targeted seven regions of the 16S gene. While Ion Torrent generally has lower sequencing depth and unique base-calling errors compared to Illumina, the multi-region targeting partially counterbalances these technological differences. To further ensure the rigor and validity of our comparative analysis, we implemented a consistent bioinformatics pipeline for both datasets, for instance (i) minimized noise through the DADA2 approach, (ii) standardized taxonomic assignments using the SILVA v138 database, and (iii) conducted network and statistical analyses based on centered log-ratio transformation  (Aitchison, 1986) , which made the data symmetrical.
Acknowledgments The authors like to thank  Marion Courseaux  and  Emeline Rouxel  who processed the samples and prepared the zoo elephants DNA library. The authors would particularly like to thank all the veterinary surgeons who collected boluses of elephant dung:  Vanessa Alerte ,  Margaux Chandenier ,  Sylvie Clavel ,  Maxime Collin ,  Cyril Hue ,  Alexis Maillot ,  Tristan Ningler ,  Hanae Pouillevet ,  Elodie Trunet , and  Estelle Woessner.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www. ncbi. nlm. nih. gov/, PRJNA939400 (SRX19520099, SRX19520098, SRX19520097, SRX19520096, SRX19520095, SRX19520094, SRX19520093, SRX19520092, SRX19520091, SRX19520090, SRX19520089, SRX19520088, SRX19520087, SRX19520086, SRX19520085, SRX19520084, SRX19520083, SRX19520082, SRX19520081, SRX19520080, SRX19520079) https://www. ncbi. nlm. nih. gov/, PRJNA587772.
The present work was supported by the association Beauval Nature. UMR BIPAR is supported by the French Government's Investissement d' Avenir program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" (grant no. ANR-10-LABX-62-IBEID). AW-C was supported by Programa Nacional de Becas de Postgrado en el Exterior "Don Carlos Antonio López" (grant no. 205/2018). AM is supported by the 'Collectivité de Corse' , grant: 'Formations superieures' (SGCE -RAPPORT N° 0300). 10. 3389/fmicb. 2023. 1247719 Funding Frontiers in Microbiology 12 frontiersin. org.
Computed tomography A CT examination was performed using a 16 multi-slice helical scanner (Siemens, Somatom Sensation 16, Erlangen, Germany) with automatic acquisition parameters (tube voltage 120 kVp, automatically adapted tube current 110-180 mA, pitch 1, and total collimation width 1. 2 mm). Raw data sets were reconstructed using a 512 × 512 matrix with a pixel spacing of 0. 21 mm and a slice thickness of 0. 6-1 mm. Two bone reconstruction windows were used (B70s sharp and U90u very sharp convolution kernels). The angle between a line tangential to the talar ridges drawn at their most dorsal extent and a line tangential to the trabecular/cortical bone interface of the proximal MtIII at approximately mid-height of the proximal suspensory enthesis was calculated based on transverse CT images using standard medical image viewing software (RadiAnt DICOM viewer, Medixant, 2009-2017, Poznan, Poland).
Image analysis DICOM images were viewed on a dedicated workstation using local software for archiving and reading medical images (Impax 6. 6, AGFA, Belgium). MR and CT images and dorsoplantar radiographs were compared side-by-side in DICOM format by two experienced readers (an ECVDI Dip -VB and a last year ECVDI resident -MD). The position and number of the ridges visible on the CT images were noted and compared with the corresponding radiographic and MR images to localize the ridges on the dorsoplantar views and to determine their position relative to the suspensory ligament and its adipose-muscular bundles. To evaluate the sharpest delineation of the plantar cortex, lateromedial and oblique radiographs (56 views) were cropped by an independent operator to exclude the talar ridges and pasted in a random order, in JPEG format, on PowerPoint.
And identification errors cannot be excluded due to the known morphological similarity between Bulinus forskalii and Bulinus senegalensis  [15]. The correct identification of intermediate host snails based on morphological and molecular (sequencing) criteria has limitations  [22]. Morphological identification requires highy skilled experts, a specific documentation (good and updated identification keys) and also intact specimens. DNA-based identification is relatively time consuming costly, and the reference nucleotide sequence databases, notably Genbank, are not exhaustive. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has revolutionized microbial identification in clinical microbiology. It has also used been successfully used in entomology for rapid identification of many arthropods including mosquitoes, ticks, lice, fleas, and bed bugs  [23] [24] [25] [26].
MALDI-TOF MS identification of snails After centrifugation at 2000 rpm for 30 seconds, 1 μl of the supernatant from each homogenate was deposited on a MALDI-TOF MS target plate (Bruker Daltonics, Wissembourg, France) in 10 replicates. Each deposit was covered with one microlitre of a CHCA matrix suspension consisting of saturated α-cyano-4 hydroxycinnamic acid (Sigma, Lyon. France), 50% acetonitrile (v/v), 2. 5% trifluoroacetic acid (v/v) (Aldrich, Dorset, UK), and HPLC grade water to allow co-crystallization. After drying for several minutes at room temperature, the target was introduced into the MALDI-TOF Microflex LT mass spectrometer (Bruker Daltonics, Breman, Germany) for analysis. To confirm the morphological identification of the snails, the MALDI TOF MS spectra obtained from the foot of each specimen were compared to the reference spectra from the in-house database (available at https://doi.
org/10. 35088/f605-3922)  [28]  using MALDI-Biotyper v3. 0 software (Bruker Daltonics). The homemade reference spectra database already contains (n = 64) reference spectra of freshwater gastropod snails from Senegal (S1 Table ). The blind test identification quality is estimated via log score values (LSV) (range 0 from 3), which quantifies the degree of identity between the query and the reference spectra in the database. A sample is considered correctly identified when the LSV value is � 1. 7. A dendrogram was performed using MALDI-Biotyper v. 3. 0 software to visualize the level of heterogeneity of MS spectra from Bulinus forskalii, Bulinus senegalensis and Biomphalaria pfeifferi specimens as out-group. MS protein profiles from one to seven specimens of each snail species were randomly selected and used to create a dendrogram.
The amplification protocol was carried out with initial denaturation at 95˚C for 15 min, with 39 cycles at 95˚C for 30 s, 58˚C for 1 min (at 56˚C for 1 min 30 s for ITS2), 72˚C for 1 min, and a final step at 72˚C for 7 min. The amplified products were purified using a Macherey Nagel plate (NucleoFast 96 PCR, Du ¨ren, Germany) and sequenced using the same primers. Sequencing was performed using BigDye Terminator v1. 1, v3. 15x Sequencing Buffer (Applied Biosystems, Warrington, UK) and run on an ABI 3100 automated sequencer (Applied Biosystems). The resulting sequences were assembled and corrected using Chromas Pro v. 1. 77 (Technelysium Pty. Ltd, Tewantin, Australia) and BioEdit v. 7. 0. 5. 3 to be used for a Basic Local Alignment Search Tool (BLAST) search on the National Center for Biotechnology Information (NCBI) online database (http:// blast.
Molecular identification of snails and detection of S. haematobium complex infection Bulinus forskalii identification were further confirmed by 100. 00% identity with the COX-Folmer of Bulinus forskalii (GenBank Accession No: OM535893. 1)  [3, 22]. In the 55 Bulinus forskalii specimens, 13 (23. 6%) and 17 (30. 9%) were infected with Schistosoma parasites with the cercarial shedding tests or the Dra1-RT-PCR, respectively (S2 Table ). The percentage of infected snails observed by the cercarial shedding test did not significantly differ from that obtained with real-time PCR (χ2 = 0. 41, df = 1, p = 0. 52). The RT-PCR was positive (Cycle threshold (Ct) � 35) in each snail that shed cercariae (S2 Table ).
DNA sequence-based identification of Schistosoma spp. parasites The COX1 nucleotide sequence further identified six (10. 9%) S. haematobium, and three (5. 5%) S. bovis. In contrast, ITS2 nucleotide sequence based identification yielded three (5. 5%) for both S. haematobium and S. bovis. BLAST query of the NCBI nucleotide database identified either S. haematobium or S. bovis, as detailed in Table  2.
78%) MT579447. 1 NA - forskalii S20BfI10 Bulinus 2. 63 (+) 25. 00 NA - S. haematobium (100. 00%) MT580959. 1 forskalii KSD2BfI1 Bulinus 2. 29 (+) 19. 39 S. bovis (99. 23%) MT159594. 1 S. bovis (100. 00%) MT580958. 1 forskalii KSD2BfI4 Bulinus 2. 38 (+) 25. 41 S. bovis (99. 61%) MT159594. 1 S. bovis (100. 00%) MT580958. 1 forskalii S9BfNI1 Bulinus 2. 31 (-) 34. 94 S. haematobium (100. 00%) MT579447. 1 S. haematobium (100. 00%) MT580959. 1 forskalii SOBfNI1 Bulinus 2. 11 (-) 33. 88 S. bovis (99. 61%) MT159594. 1 S. haematobium (100. 00%) MT580959. 1 forskalii *Cercarial shedding test: (+), positive; (-), negative. ** Accession numbers of the Nucleotide GenBank sequences with the highest identity percentage *** NA: not available, because of sequencing failure https://doi. org/10. 1371/journal.
Cluster analysis showed a clear separation between S. haematobium, S. bovis, S. mansoni and S. rodhaini. Corsican hybrids are classified with those of the parental strain of S. haematobium whereas other hybrids formed a distinct cluster. In blind test analysis the developed MALDI-TOF spectral database permits identification of Schistosoma cercariae with high accuracy (94%) and good specificity (S. bovis: 99. 59%, S. haematobium 99. 56%, S. mansoni and S. rodhaini: 100%). Most misidentifications were between S. haematobium and the Corsican hybrids. The use of machine learning permits to improve the discrimination between these last two taxa, with accuracy, F1 score and Sensitivity/Specificity > 97%. In multivariate analysis the factors associated with obtaining a valid identification score (> 1.
Spectra analysis Spectral quality and reproducibility assessment. Spectra were visually assessed in FlexAnalysis v3. 4 and imported in Biotyper Compass Explorer v4. 1. 100 for analysis. The assessment of spectral intraspecific reproducibility and of its variation factors was performed by computing the Composite Correlation Index (CCI) using Compass Explorer default settings with the following spectral features: (i) sum of the intensity of all detected peaks, using R scripts adapted from Cuenod et al. [38]  and (ii) signal to noise ratio (SNR) of the spectra calculated by the MALDIQuant detectPeaks function. PCA analyse were performed using the FactoMineR package  [39]. MSP database creation and validation. The Bruker "MALDI Biotyper Preprocessing Standard Method" was used with slight modification namely the use of 2000 and 20,000 Da as lower and upper bound limits for spectra trimming.
High quality spectra from fresh cercariae were selected for addition into the database using the Main Spectra Profile (MSP) creation tool of Compass Explorer using Bruker guidelines. Eight to 22 spectra were used for MSP creation. Hierarchical Cluster Analysis (HCA) was performed using correlation method and the Ward algorithm for clustering with the MSP dendrogram tool of Compass Explorer. Spectra used for MSP database validation came from Schistosoma cercariae from different emissions than those chosen for database construction. Both fresh and ethanol-stored cercariae were used. According to our previous work on Trematoda's cercariae, identifications were considered interpretable when the first best match Log-Score Value (LSV1) was � 1. 7 (ROC curve presented on S1 Fig)  [28]. Accuracy (percentage of correct identifications) and specificity were calculated in R (version 4.
0. 5) using the caret package  [40]. For assessing the effects of various parameters on spectral quality and identification LSV1 value, spectra of hybrids were removed from the blind test validation dataset. Statistical analysis was performed in R (version 4. 0. 5) using base R with the rstatix, pROC, and forestmodel packages  [41] [42] [43]. Student's t-test and Wilcoxon rank-test were used in univariate analysis; a logistic regression model was built for multivariate analysis. Bonferroni correction was used to adjust p-values in multiple comparisons. Differentiation between Schistosoma haematobium and Corsican hybrid strains using machine learning approach. Four machine learning algorithms were evaluated to discriminate between Schistosoma haematobium and hybrid strains: k nearest neighbour (KNN), Support Vector Machine linear classifier (SVM), Partial Least-Square Discriminant Analysis (PLS) and Random Forest Analysis (RF) using custom R scripts based on the caret package  [40].
Briefly, Bruker FID spectra files from Schistosoma haematobium and Corsican hybrid strains were imported using the MALDIQuantForeign package. Selection of high quality spectra was performed by using the screenSpectra function from the MALDIrppa package  [44]. Spectra preprocessing and peak detection was performed with the MALDIquant package  [45]. Intensity peak matrix was imported as a specmine object  [46]  and randomly split into "training" and "first validation" datasets. A second dataset containing both ethanol preserved and fresh specimens was created. Model training was performed on the "training" dataset with the "knn", "svmLinear", "pls" and "rf" caret  [40]  methods with repeated cross validation (10 folds and 10 repeats). ROC metrics were used to evaluate model performance.
Database construction and MSP classification 5408 spectra were collected. Representative spectra are shown in Fig 1. Spectra were visually different between species and reproducible across replicates of the same species. Interestingly, spectra of F1 and F1' hybrids are visually different while spectra of S. haematobium and of the Corsican hybrid seem very similar. For database construction, between 9 and 24 high-quality spectra were chosen to create MSP for each strains. They were added to the in-house Trematode's MSP database  [28]  (database structure is listed in S1 Table ). Cluster analysis showed a clear separation between S. haematobium, S. bovis, S. mansoni and S. rodhaini (Fig  2 ). All spectra obtained from specimens belonging to the same species are grouped together within the same cluster.
Database validation Among the 5408 spectra, 3277 were selected for database validation (2166 from fresh specimens and 1111 from ethanol stored specimens). The number of spectra and the species distribution according to experimental procedure is given in Table  1. The choice of an LSV cut-off >1. 7 for specific identification was supported by ROC-curve analysis (S1 Fig). We chose not to lower this score in order to possibly detect cercariae of nonschistosomal species  [28]. 1399 spectra (42. 7%) had a LSV > 1. 7. Among them, 80 (5. 7%) non-concordant identifications were observed. The global accuracy of the test was 0. 94 (95%CI [0. 928, 0. 953]). Specificity for identification of Schistosoma at the species level was good: S. bovis: 99. 59%, S. haematobium 99. 56%, S. mansoni and S. rodhaini: 100%.
For S. bovis (except for SB3) and S. rodhaini, spectra were dispersed along the PC1 and PC2 (Fig 3C  and 3D ). The analysis of the whole reproducibility dataset showed a clustering by species with an overlap between some S. mansoni and S. rodhaini spectra (Fig  3E ). These overlapping spectra correspond to the SR3 mollusc (Fig  3F ). In each case, only less than 15% of variance is explained by the first 2 components of PCA showing that a large part of the spectral diversity is not explained by the emitting snail or by the species to which the cercaria belongs. In order to evaluate the factors influencing quality of spectra, we compared spectra of the same mollusc at day 0 (with and without ethanol fixation) and at day 22 (after ethanol fixation). As previously described  [28] , the intensity of spectra decreased significantly with ethanol fixation (mean 893889.
8 vs. 865683. 4 in case of ethanol fixation p < 0. 001) Fig 4A. This difference was not significant between fresh specimens and day 22 fixed specimens (mean total intensity 893889. 8 vs 865432. 9 p = 0. 33). The SNR slightly increase with ethanol fixation (mean SNR 4. The influence of ethanol preservation, the number of deposited cercariae and species on identification success was then evaluated on a larger dataset: 2212 spectra were selected, 1101 from fresh specimens and 1111 from ethanol stored specimens (Quality test with LSV in Table  1 ). Distribution of best match LSV (LSV1) according to the number of deposited cercariae is presented in Fig  5 , for fresh and ethanol preserved specimens, according to the number of deposited cercariae (Fig 5A  and 5B ), or the species of Schistosoma (Fig 5C  and 5D ).
Machine learning for discrimination of the Corsican hybrid Most misidentifications come from confusion between S. haematobium and the Corsican hybrids. Machine learning was thus tested in order to unambiguously distinguish the two species. A dataset containing spectra (n = 649) from fresh cercariae of pure S. haematobium   2 ). The second dataset containing both ethanol preserved and fresh specimens displayed a performance drop for the SVM, PLS and RF models while the KNN model kept a very good accuracy (0. 9914), sensitivity (0. 9896) and specificity (1. 000).
No degradation of spectra quality was observed for proglottids stored at -20˚C in 70% ethanol for 24 weeks. However this methodology needs to be reproduced with other species and life-stages of helminths. In our previous study on the application of MALDI-TOF on identification of Trematoda's cercariae we already noticed a decrease of spectra intensity and identification success with ethanol fixation  [28]. Nevertheless, identification remains possible for approximately 30% of the spectra, which makes it worth trying to attempt the study of specimens preserved in alcohol. It would be necessary to evaluate other means of preservation of cercariae in order to facilitate retrospective studies. Alternatively, the identification of specimens preserved in ethanol could be improved by building a specific database as proposed by Hamlili et al.
In the present study, we confirm the usefulness of MALDI-TOF for cercariae identification, with a great potential for malacological surveys in Schistosoma-endemic areas, enabling the rapid and reliable identification of emitted cercariae at species level and detection of hybrids. Identification of the snails could also be obtain with MALDI-TOF using the database containing nine species published by Hamlili et al. [29] , making it a tool able to identify both the host and the parasite. One of the limitations of MALDI-TOF is its inability to detect cercariae directly from snail's tissues, making it impossible to identify pre-or non-patent infection  [29]. However, the majority of pre-patent snails never realised their cercariae  [60]. Their importance in transmission is probably weak, and interpretation of their epidemiological role is difficult.
Results For the 25 different combinations of constraints (AllLameTime and MLD), the best and second-best results of scenarios are presented in Table  4. For instance, for the lowest classes of MLD and AllLameTime (upper left part of the table), the two optimal scenarios are "CON-CRETE_TRIM_SCRAP>8_LD_2_FB_0" and "CONCRETE_TRIM_SCRAP>8_LD_2_FB_1", corresponding to the scenarios in which the floor is concrete, preventive trimming is performed, daily scraping frequency is above eight times, lameness detection is 80% lower than the standard implemented one  [28] , and the DD prevalence for which a collective FB is applied is 0% or 5%. The color code helps to classify the scenarios from the less acceptable (red) to the most acceptable (green), and helps to make the interpretation easier. More desirable scenario.