dataesr/scientific-paragraphs-categorization huggingface dataset:
SOFTWARE AVAILABILITY In 2019, De C aceres and colleagues proposed the CTA framework and functions for the calculation of associated metrics. These functions were included in package vegclust. In 2021,  Sturbois et al. extended  the CTA framework with new metrics and figure concepts. New functions were added in package vegclust. The present article goes further in the adaptation of CTA, bridging a gap identified in the consideration of dynamics in SI ecology, and starting the exploration of trajectory analysis beyond the sites Â species matrix. All these recent developments, and the fact that trajectory analysis can be applied to different spaces, claimed a new package specifically devoted to ecological trajectory analysis that would allow taxonomic, functional or SI trajectory analyses within the same tool through complementary space of analysis (Ω).
The package ecotraj (De C aceres [2019],  Sturbois, De C aceres, et al. [2021] ) assists ecologists in the analysis of temporal changes of ecosystems, defined as trajectories on a chosen multivariate space, by providing a set of trajectory metrics and visual representations. It includes functions to perform trajectory plots and to calculate the set of distance and direction-based metrics (length, directionality, angles, etc. ) as well as metrics to relate pairs of trajectories (dissimilarity and convergence). Currently, Trajectory analysis (SITA as well as CTA) can be performed using the "ecotraj" functions available on CRAN and GitHub repositories (https://emf-creaf. github. io/ecotraj/index. html). R codes to create figures are also shared to facilitate the use and the customization of our trajectory chart concepts (Data S1) and a new vignette has been added in the documentation of the package.
The release site was connected through a unique point to an adjacent pond (AP) used as a nursery habitat. The pond strongly differs from the FG in many ecological features such as food availability (zooplankton and fish prey). Fish were continuously recaptured when migrating from the FG to the AP. Recaptured individuals (n = 29) were anesthetized, checked for tags, measured for fork length, F I G U R E 3 Fur seal individual trophic trajectories. Net change time series for males and females of both AFS and SAFS. Arrows connect all whiskers section stable isotope values from t 1 to t 30 (i. e. , most recent to oldest stable isotope values). Colors correspond to trajectory clusters. Data from  Kernaléguen et al. (2012)  fin-clipped to quantify changes in δ 13 C and δ 15 N values  (Sturbois, Cucherousset, et al.
Methods This question was investigated by quantifying the temporal dynamic of the trophic structure of fish communities in a network of gravel pit lakes displaying varying levels of biological invasions  (Zhao et al. , 2019). Fish were sampled in 2014, 2016, and 2018 using a standardized protocol (gill netting and electrofishing) in six gravel pit lakes located along the Garonne river (Figure  9 )  (Alp et al. , 2016; Evangelista et al. , 2017; Zhao et al. , 2019). Fish were identified at the species level, counted, measured, and fin clips were collected for δ 13 C and δ 15 N analyses. The overall data set (all lakes and sampling year pooled) included 19 species. Five species were present in more than 75% of all communities (18 lakes Â years): two non-native species, namely pumpkinseed (Lepomis gibbosus) and black bullhead (Ameiurus melas) and three native species, namely roach (Rutilus rutilus), perch (Perca fluviatilis) and rudd (Scardinius erythrophthalmus).
We subset modeled δ 13 C and δ 15 N values of a 1 Â 1 spatial grid from the original modeled dataset. Isoscapes modeled for 2013, 2015, and 2017 were selected as they were characterized by high SI dynamics and consequently constituted relevant inputs to test our ITM concept. Modeled SI values for which one of the parameter was missing were excluded. Mapping trajectory metrics requires that stations are synchronously surveyed. Consequently, stations where values were missing for one date within each pair of dates  (2013-2015 and 2015-2017)  were also excluded. The subset of stations supporting SITA was finally composed of 489 and 488 stations for the periods  2013 -2015 and 2015 -2017 , respectively. Segment lengths (2013 -2015 and 2015 -2017)) , and angle α were calculated in the modeled 2D Ω δ space (δ 13 C/ δ 15 N) for all stations and periods and used as input in a modeled ITM.
SITA contributes to achieving the synthetic challenge inherent to high spatio-temporal resolution datasets, independently of the size of the study area and/or the length of the time series. This ability to deal with large datasets seems especially relevant in the context of isoscape dynamics in widespread and/or intensively studies area, as shown for SI dynamics in the northeast Pacific water body (EA6). Differences in SITA metrics can be tested statistically as illustrated with circular statistics for direction-based metrics (EA1). Users are encouraged to use complementary statistical approaches, such as regression or correlation tests, to explore the relationship and the strength of the relationship between distance-based metrics and other explanatory variables under natural, anthropogenic, or experimental conditions.
, 2021; Buckley, Day, Lear, et al. , 2021) , but as a new tool for the SI ecologists that could be useful in situations suitable for trajectory analysis. Similar to all analytical tools, there is a high likelihood that the SITA framework may be applied to datasets to which it is not well suited or that inexperienced users may misinterpret the results. Indeed, there is a considerable history of this in the SI literature concerning, for example, mixing models  (Fry, 2013; Jackson et al. , 2009; Phillips, 2001; Phillips et al. , 2014). Users must consequently be aware of the assumptions, fields of application, and limitations associated with the SITA framework as described in this section. Despite the speed of changes that allows dealing with variations in frequency of surveys, we encourage users to establish sampling strategies, implying synchronous sampling and similar frequency of surveys  (De C aceres et al.
, 2019; Sturbois, De C aceres, et al. , 2021). It is an essential condition to use at best the SITA framework. Multivariate ecological methods are descriptive by nature. Despite SITA providing accurate measurements and representations of dynamics in stable isotope ecology, it shares limitations with CTA  (De C aceres et al. , 2019; Sturbois et al. , 2021). Consequently, SITA outputs must be completed by a strong examination of input datasets (stable isotope raw data or structural and functional indices) to improve the ecological interpretation of observed dynamics. Similarly, SITA may be complemented with additional analyses  (Buckley, Day, Case, et al. , 2021)  to statistically test for other aspects of changes or to provide statistical backgrounds. Note that ordination spaces are specifically constructed for each given data set.
Exporting trajectory analysis to other kinds of ecological data Taking into account the strengths and limitations of the proposed framework, we consider that SITA brings insightful perspectives into the analysis of highresolution temporal datasets using stable isotopes and/or other tracers in integrating organism tissues. Even though the SITA framework has been here defined based on stable isotope data, and builds on the CTA framework for community data, any other multidimensional input data are likely to be suitable for this approach. For instance, using trajectory analysis on multitrace element data, including SI or not, would provide appealing insights into subpopulation migratory patterns in fish populations based on otolith chemistry  (Elsdon et al. , 2008; Trueman et al. , 2012). Similar applications can easily been foreseen at the individual scale from tree rings, in a dendrochronology context  (Sleen et al.
, 2017) , or from environmental signals recorded in bivalves shells  (Butler et al. , 2019) , or bone growth layers  (Merrett et al. , 2021; Turner Tomaszewicz et al. , 2016). As shown at the scale of the northeast Pacific water body (EA 6), sea water SI composition can be tracked to large spatio-temporal scales and may be tested for long-term or seasonal SI trajectories in rain and snowfall  (Aizen et al. , 2005; Tian et al. , 2018) , or ice  (Schotterer et al. , 1997; van Trigt et al. , 2002; Werner et al. , 2018). Other tracers are also increasingly used in association with SI, such as contaminants  (Dietz et al. , 2004 (Dietz et al. , , 2021) )  or fatty acids  (Madgett et al. , 2019) , and the absence of limitation in the number of dimensions supporting SITA constitutes an important strength to improve the analysis of such complex multivariate datasets.
As experimental studies involve very controlled protocols, SITA metrics can also be particularly relevant to depict experimental trajectories (EA3). We hope that trajectory analysis using the new ecotraj package will contribute to the assessment of natural or man-induced environmental changes using the responses of chemical composition in biological archives, both in contemporary or archeological studies at local, to regional and global, scales. On a broader front, there are claims for an extension of the trajectory analysis concept to other fields in ecology. In this perspective, the adaptability to different type of ecological questions  (compositional, functional, structural, trophic, etc. )  given by the choice of the space of analysis Ω (i. e. , raw variables and dissimilarity metric choice) constitutes the major strength of the approach.
ACKNOWLEDGMENTS We are very grateful to the editor and anonymous reviewers who significantly contributed to improve the quality of the article. We acknowledge the  Agence de l'eau Loire-Bretagne  (grant number  180212501 ), the  Région Bretagne  (grant number  OSIRIS PFEA621219CR0530023 ), the  Europe for the European Maritime and Fisheries Fund  (grant number  FEAMP 621-B ) and the  Ministère de la transition écologique et solidaire  (grant number  EJ No. 2102930123 ) who funded this research as part of the  ResTroph Baie de Saint-Brieuc research program. This work was carried out as part of the PhD thesis of  A. Sturbois  for  Université de Bretagne Occidentale. This research was also funded by the  Spanish Ministry of Economy  ( CGL2017-89149-C2-2-R ) and all EA were supported by complementary funds described in the respective articles.
DATA AVAILABILITY STATEMENT Data are available as follows: Bestion et al. (2019b), Zenodo, https://doi. org/10. 5281/ZENODO. 3475401; Espinasse et al. (2020), Dryad, https://doi. org/10. 5061/DRYAD. D2547D7Z6; Sturbois et al. (2021a), Zenodo, https://doi. org/10. 5281/ ZENODO. 5519696. Code (Sturbois et al. , 2021b) is available on Zenodo at https://doi. org/10. 5281/ZENODO. 5519693.
19 Secondary bile acids are produced by the actions of certain colonic bacteria on primary bile acids. Deconjugation of taurine and glycine from primary bile acids is performed by a broad range of species encoding bile salt hydrolases. Taxa expressing the bai (bile acid-inducible) operon convert primary bile acids to secondary bile acids and this is performed by a narrow set of Clostridium species (Clostridium scindens, Clostridium hylemonae and Clostridium hiranonis). 20 The secondary bile acid deoxycholic acid (DCA), has in particular been associated with sporadic CRC,  19  while low levels of secondary bile acids have been reported in IBD. 17  To address whether the gut microbiota and bile acid metabolites were associated with colitisassociated neoplasia (cancer and its precursor lesions, low-grade dysplasia (LGD) and highgrade dysplasia (HGD)), we conducted a multicentre observational study (ClinicalTrials.
gov Identifier: NCT02726243) of both the bacterial fecal microbiota by 16S rRNA gene amplicon sequencing and bile acid metabolites in IBD patients undergoing surveillance colonoscopy in France, using non-IBD patients undergoing screening colonoscopy as controls. The aims of this study were to investigate the link between the gut microbiota, intestinal inflammation, colorectal cancer, bile acids, and primary sclerosing cholangitis (PSC). To address these aims, we used both direct comparisons between groups of interest and exploratory, unsupervised Dirichlet Multinomial Mixtures (DMM) to identify microbiome clusters within the data.
Clinical and technical confounders in the dataset Consistent with the results of the exploratory triplot and Correspondence analysis (Figures  6(b, c ), respectively), 50% of UC patients in C. 3 had PSC. In contrast, only 23. 4% of patients in C. 1 and 24. 1% of patients in C. 2 had PSC (Chi-squared test P-value = 0. 009). As C. 3 was associated with other risk factors for neoplasia, such as disease activity and PSC, these may act as confounders. Associations of other important clinical variables with cluster assignment are presented in Table  S3. As described in the methods, a subset of samples (15 out of 215) were fresh-frozen rather than being stored in RNAlater®. While these accounted for a small proportion overall IBD patients (6. 97%), they accounted for a large proportion (15/38 (39.
16S rRNA gene sequence analysis Amplicon sequence variants (ASVs) were determined using the dada2 algorithm,  44  applied independently per sequencing run. Taxonomic classification was performed using the Silva reference database (version 138). 45 R scripts are available on github (https:// github. com/ajlavelle/dyscolic-Figures). Data was then imported into the R statistical environment for subsequent analysis (R version 3. 6. 3, 46 phyloseq package (version 1. 28. 0),  47 incorporating the Bioconductor workflow)  [48]  16S rRNA gene sequence data are deposited in the Sequence Read Archive (accession number PRJNA720094). Bile acid metabolomics data and selected metadata are available from the corresponding author on request (harry. sokol@gmail. com).
Statistical and microbiome analysis Continuous data is presented as median and interquartile range (IQR). Between-group differences were assessed for continuous data using the Wilcoxon rank sum test for two groups and the Kruskal-Wallis test for >2 groups and for categorical data using the Chi-squared test or the Fisher exact test. P-values are represented with stars according to the following convention: * <0. 05; ** <0. 01; *** <0. 001; **** <0. 0001. P-values plotted by ggpubr are uncorrected. Filtering of microbiota data was performed initially to remove ASVs that were not assigned to a Phylum or which were present on only a single individual. Microbiota alpha diversity was estimated using the Shannon diversity index. Total-sum scaled (TSS) normalized data was generated for betadiversity assessment using the Bray-Curtis index.
PERMANOVA was performed using the adonis function with 999 permutations (vegan package,  49  version 2. 5-6). Differential abundance between groups was tested at the genus level in genera present in at least 10% of individuals using an ensemble or consensus approach, as suggested by a recent publication. 26 This included a standard Wilcoxon rank sum test on TSS normalized data, an approach using centered-log ratio (clr) transformed data as implemented in the ALDEx2 package,  50  the Wilcoxon test on total sum scaled proportions and a negative binomial count method implemented in the DESeq2 package (version 1. 24. 0). 51 A false discovery rate threshold of 0. 2 was applied and only taxa which were detected concordantly by all three approaches were included and plotted based on their DESeq2 'log2foldchange'.
Plotting was performed using ggplot2 (version 3. 2. 1)  52 and ggpubr (version 0. 2. 3). 53 Triplots were created using the 'seqPCoA' function from the seqgroup package (https://github. com/hallucigenia-sparsa/seqgroup/) which wraps PCoA, PERMA-NOVA and envfit functions from the vegan package. Dirichlet multinomial mixture (DMM) clustering of bacterial genera was performed with the DirichletMultinomial package in R (version 1. 26. 0),  54, 55 using the number of clusters that minimized the Laplace approximation. Due to variability between runs, this procedure was re-run 20 times to ensure a convergent result. For bile acid metabolites, TSS normalized data was compared between groups. For principal component analysis (PCA), bile acids concentrations were normalized and transformed in a similar manner as used in the metaboanalystR package (version 3.
0. 3),  56 replacing undetected values with 1/5 th of the minimum value per feature, followed by log-2 transformation and mean centering. ALDEx2 was used to test for differentially abundant bile acid metabolites. The relative risk of dysplasia between low-risk and high-risk clusters was calculated with 95% confidence intervals  57  and statistical significance tests were performed using the Fisher exact test. For correlation between bacterial genera and bile acid metabolites (cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), lithocholic acid (LCA) and ursodeoxycholic acid (UDCA)), zero values were imputed using the 'cmult' function from the zCompositions package,  58  followed by the clr transformation from the compositions package. 59 A Spearman rank correlation was performed using the 'rcorr' function from the Hmisc package and only values with an FDR-corrected p-value of <0.
Funding This work was supported by  Canceropole and Institut national du cancer, INCa  (grant number  2013-207 ). AL has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/ 2007-2013)  under  REA  grant agreement n. PCOFUND-GA -2013-609102, through the  PRESTIGE programme  coordinated by  Campus France ; Canceropôle PACA.
Protein Preparation The 3D structure of SARS-CoV-2 RdRp-Nsp7-Nsp8 complex (PDB ID: 7AAP) was downloaded from the RCSB PDB database. The homology-modeled protein of SARS-CoV-2 RdRp was obtained from Prof. Hua Li, HUST, China  (Wu et al. , 2020). Energy minimization was performed using YASARA software and protein preparation was performed using the AutoDock Tools Version 1. 5. 6  (Balasubramaniam et al. , 2019). The natural ligands present in the protein structures were isolated by deleting atomic orchestrates of the PDB file. During protein preparation, all the water molecules were deleted, and hydrogen polarities and Kollman charges were allocated, using AutoDock Tools Version 1. 5. 6 platform. Protein structures were then translated from PDB file to PDBQT file format to perform the docking analysis with AutoDock vina.
Molecular Dynamic Simulation Studies To confirm the stability of the ligand-bound complexes of therapeutic targets of SARS-CoV-2, molecular dynamic simulation studies were performed for 100 ns using Gromacs software. The ligand topologies were obtained from the PRODRUG server. All ligand-bound structures were solvated with SPC216 water molecules and periodic boundary conditions were applied to all directions of cubic boxes with 1. 0 and 1. 2 nm dimensions. To obtain the precise electrostatic values during the dynamics, all of the systems (net charge) were neutralized with appropriate Na + ions. Steepest Descent Algorithm (SDA) and Conjugate Gradient Algorithm (CGA) were used to perform energy minimization (50,000 steps) with a tolerance of 1,000 kJ mol -1 nm -1 , followed by a two-step equilibrium phase (NPTparticles, volume and temperature; NVT-particles, pressure and temperature combinations).
MM-PBSA Calculation for Protein-Ligand Complexes In order to calculate the binding-free energies (affinities) of the complexes during the dynamics, MM-PBSA calculation was performed. Here, the trajectories obtained from last 10 ns were considered to calculate Gibbs free energy using g_mmpbsa tool (combined in the GROMACS software) and the following equation  (Mariadasse et al. , 2020). G bind = G complex -(G protein + G ligand ) Where, G complex, is defined as total energy, G protein and G ligand are defined to reach the total binding-free energy of the protein and ligand.
, 2020). The percentage of inhibition of SARS-CoV-2 replication was estimated for each drug concentration as follows: (mean CT drugconcentration -mean CT control0% )/(mean CT control100% -mean CT control0% ) × 100. EC 50 (median effective concentration) and EC 90 (90% effective concentration) were calculated with the inhibitory sigmoid Emax model, which estimated the EC 50 and EC 90 through non-linear regression by using a standard function of the R software (ICEstimator version 1. 2). The EC 50 and EC 90 values resulted in a mean of 7-10 independent experiments.
, 2020). In the current study, the in silico results obtained reveal that doxycycline has the potential to interrupt with the NTP entry channel of SARS-CoV-2 RdRp (Figure  3 ). Furthermore, the binding site of doxycycline with the NTP entry channel of RdRp was confirmed by re-docking with the SARS-CoV-2 RdRp-Nsp7-Nsp8 complex (PDB ID: 7AAP). The result also showed that doxycycline was able to interfere with NTP entry with low-binding energy (-10. 5 Kcal/mol), and hence we believed to arrest the viral replication of SARS-CoV-2. In molecular simulation analysis, the RMSD and RMSF graphs illustrated no major fluctuation for the RdRp-Doxycycline complex (Figure  4 ). The structural stability of the complexes did not exceed 0. 5 nm, suggesting that all the complexes were architecturally highly stable, indicating that the doxycycline could be active against SARS-CoV-2.
FUNDING RA acknowledge financial support from  RUSA, MHRD, Government of India  [F. 24-51/2014-U, Policy (TN  Multi-Gen ), Dept. of Edn, GOI] in the form of RUSA-2. 0Fellowship. The evaluation of the in vitro activity was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection  and the  French National Research Agency  under the " Investissements d'avenir" program  ( Ref :  ANR-10-IAHU-03 ).
TEM characterization A sample volume of 2-4 µl was applied onto a freshly prepared Cu TEM grid covered by a homemade amorphous carbon film of nominal thickness 1. 7 nm. Excess sample was removed using a filter paper. A Gatan 626 single-tilt liquid nitrogen cryo-transfer holder was used for cryo-TEM studies. The grid with the sample was either cooled to liquid nitrogen temperature in the TEM column under vacuum or plunged frozen in liquid ethane cooled by liquid nitrogen using a Thermo Fisher Vitrobot mark IV vitrification machine and cryo-transferred into the microscope. All TEM observations were performed on a Thermo Fisher Tecnai F20 cryo-TEM with a field-emission gun operating at 200 kV. Images were collected on a Thermo Fisher Ceta camera. Low-dose imaging was performed using SerialEM or Thermo Fisher EPU software.
Electron diffraction Electron powder diffraction patterns were collected with a selected area aperture using a Cheetah hybrid pixel direct electron detector (Amsterdam Scientific Instrument). The 2D diffraction image were azimuthally integrated to obtain 1D diffraction pattern using the free software CrysTBox  (Klinger and Jager 2015). Random areas containing hundreds of particles were measured to achieve statistical significance. The evolution of the diffraction pattern with time was studied by taking consecutive images in the span of 0. 5 s. The lattice parameters were calculated by using the interplanar spacings and Miller indices of the peaks (112), (220), and (004) from the diffraction pattern.
Grenoble Instruct-ERIC Center (ISBG; UMS 3518 CNRS CEA-UGA-EMBL) with support from the French Infrastructure for Integrated Structural Biology (FRISBI; ANR-10-INSB-05-02) and GRAL, a project of the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003) within the Grenoble Partnership for Structural Biology. The IBS Electron Microscope facility is supported by the Auvergne Rhône-Alpes Region, the Fonds Feder, the Fondation pour la Recherche Médicale and GIS-IBiSA.