dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING This work was supported by  Région Pays de la Loire  ( RFI BIOREGATE-CAVEODISC ),  INSERM, Université d'Angers, SFD  and a grant from  Agir Pour les Maladies Chroniques (APMC). This work was supported by a grant from the  Fondation pour la Recherche Médicale  (Équipe FRM  DEQ20180839587 ) and LABEX  SIGNALIFE  (# ANR-11-LABX-0028-01 ) and the  UCAJEDI Investments in the Future project  (# ANR-15-IDEX-01 ). MVG was the recipient of an EMBO short-term fellowship.
Hydrograph separation At each gauging station, daily Q values are filtered in order to separate the quick-and slowflow components. The slow one is traditionally interpreted as baseflow, which is the part of streamflow corresponding to aquifer drainage. Quick-flow corresponds to surface runoff. In the specific case of karst systems, quick-flow may also include a quick component from the springs that feed the river. Several baseflow separation methods exist; traditional ones are based on graphical analysis, like the fixed-interval, sliding-interval, local-minimum, or Wallingford methods  [Gustard et al. , 1992; Sloto and Crouse, 1996; Rutledge, 1998; Piggott et al. , 2005]. Numerical approaches have also been developed [e. g. Lyne and Hollick, 1979; Eckhardt, 2005] ; we used an automation of the one-parameter recursive digital filter proposed by  Lyne and Hollick [1979] , implemented in the HydRun package  [Tang and Carey, 2017].
Assessment of evapotranspiration Here, we distinguish between the two terms of actual evapotranspiration E and E*. E is estimated by the standard L'vovich method (Eq. 4) and E* is assessed using independent data time series at a daily time step using three different approaches, in order to provide a range for this component characterized by major uncertainties. The three approaches, respectively based on the methods of  Thornthwaite [1948] ,  Dingman [2002] , and the GR4J model  [Edijatno et al. , 1999] , are described in Appendix. The final E* value is the mean of those three methods.
Hydrological and geomorphological parameters Hydrological and geomorphological parameters are calculated for studying their correlations at the elementary catchment scale. The studied hydrological parameters are: • S/P and S*/P: part of the quick-flow component normalized by rainfall, obtained without and with IGF compensation, respectively; • S/Q and S*/Q*: proportion of the quick component within total streamflow, obtained without and with IGF compensation, respectively; • Q/P and Q*/P: conventional runoff coefficients, obtained without and with IGF compensation, respectively; • I and |I|: IGF magnitude, respectively considering and ignoring the direction (gain or loss); • α: relative impact of IGF on surface-and underground flow components. Since S is obtained from hydrograph separation of daily-data time series, its value is mainly driven by flood events.
Temporal data Temporal data used in this paper are as follows (see Table  1  for data time-series periods): • Daily rainfall, snowfall and potential evapotranspiration depths are from "Safran" (Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige  [Vidal et al. , 2010] ), edited by the French meteorological service (Météo France); • Daily streamflow measurements are from the French public streamflow database "Banque Hydro" (http://www. hydro. eaufrance. fr/), managed by the French regional environment directorates (Direction Régionale de l'Environnement, de l'Aménagement et du Logement, DREAL). Measurement periods may differ between sites but have no significant influence on the results. All hydrological indexes were calculated over different time periods-excluding or including major flood events-and showed similar results, the time series being long enough to be representative of long-term trends.
Topographic boundaries of catchments from the French national watersheds database (Base Nationale des Bassins Versants, BNBV) edited by the French central service for hydrometeorology and support on floods prediction (Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations, SCHAPI) and the French research institute on science and technology for the environment and agriculture (Institut de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture, IRSTEA); • Drainage networks from "BD Carthage" (http://professionnels. ign. fr/bdcarthage ), edited by the French geographical institute (Institut Géographique National, IGN); • Maps of the index of development and persistence of river networks (Indice de Développement et de Persistence des Réseaux, IDPR) from the French geological survey (Bureau de Recherches Géologiques et Minières, BRGM (see next section for more details); • Map of available soil-water capacity from the French institute of agronomical research (Institut National de la Recherche Agronomique, INRA) [Le Bas, 2018].
Acknowledgements The work was funded by the  French General directorate for risk prevention (Direction Générale de la Prévention des Risques, DGPR) of the Ministry of Ecological and Inclusive Transition , the  Central Service for Hydrometeorology  and  Flood Forecasting  ( Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations, SCHAPI ), and the  BRGM, French geological survey, France 's reference public sciences organism for Earth Science application in the management of surface and subsurface resources and risks.
Transcriptome analysis. Sequence alignment and gene quantification. Image analyses and base calling were performed with Illumina HiSeq Control Software and the Real-Time Analysis component. Demultiplexing was performed and Fastq files were generated with Illumina conversion software (Casava 1. 8. 2 for the house mouse data, bcl2fastq 2. 17 for the black rat data). The quality of the raw data was assessed with FastQC from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer). A splice junction mapper, TopHat 51 (v2. 0. 9 for the mouse data, v2. 0. 13 for the black rat data), using Bowtie 52 (v2. 1. 0 for the mouse data, v2. 2. 3 for the black rat data), was used to align the reads to the M. musculus genome (UCSC mm10) or the Rattus norvegicus genome (UCSC rn4) with a set of model gene annotations (genes.
The data were obtained in a strand-specific assay, so the read had to be mapped to the opposite strand. Analysis of differential gene expression between populations. Differentially expressed (DE) genes were identified with the Bioconductor 55 package edgeR v3. 4. 0, under R 3. 0. 2 for the mouse data, v3. 8. 6 under R 3. 2. 3 for the black rat data  56  , and limma  57. Data were normalized with relative log expression (RLE)-normalization factors  58. Genes with fewer than 10 occurrences (reads) were filtered out and removed before the statistical analysis. This made it possible to limit the number of statistical tests and, therefore, to reduce the impact of correction for multiple testing. Two statistical factors were included in the experimental design: the main factor included was the timing of the invasion (two levels: recent and ancient invasion), and the second factor was the site at which the rodent was caught (eight sites in total: four recently invaded sites and four anciently invaded ones).
musculus and brown rat R. norvegicus GO as the background and String software. We corrected p values for multiple testing by the Benjamini and Hochberg false discovery rate (FDR) method. GO terms with a FDR p value < 0. 05 were considered to be significant. The redundancies of significantly enriched GO terms were reduced with REVIGO  61  , using a similarity cutoff of 0. 7. We also performed enrichment analyses on biological processes and biological pathways supplied by KEGG (https ://www. genom e. jp/kegg/pathw ay. html), implemented in String. Finally we performed a global protein network analysis based on DE genes, using a String database including interaction databases (including both direct (physical) and indirect (functional) associations), genetic interactions and shared pathway interactions.
This analysis revealed the interactions between key components of different pathways. Two PPI networks were constructed by mapping all DE genes and immune-related DE genes, respectively, to the STRING database, with confidence scores > 0. 7 (high level). PPI networks were visualized and analyzed in Cytoscape software, via the web interface of String. Enrichment in these protein-protein interactions was assessed by performing Fisher's exact tests with correction for multiple testing (FDR).
Functional analyses of DE genes. For the house mouse dataset, String provided at least one gene ontology (GO) annotation for 345 of the 364 DE genes (Suppl. Table  S1a ). Of the 2098 GO annotations identified, 357 GO terms corresponding to biological processes displayed significant enrichment (FDR < 0. 05) relative to the house mouse reference genome. In particular, 10 immune-related functions were significantly enriched, including inflammatory (GO:0006954), humoral (GO:0002455, GO:0006959), and innate (GO:0045087, GO:0034097) responses or activation/regulation of immunity (GO:0006952, GO:0002253, GO:0050776). We also detected enrichment for two biological processes relating to the response to wounding (GO:0009611, GO:1903035). These enriched GO terms relating to immunity corresponded to 73 DE genes, 20% of all the DE genes (see Suppl.
0e-16, 731 edges found, 54 expected), indicating a larger number of interactions between these proteins than would be expected for a set of proteins of similar size drawn at random from the sequences of the mouse genome. Two main clusters were detected, one including cytochrome P 450 and UDP glucuronosyltransferase proteins, and the other consisting mostly of immune-related proteins, including fibrinogen, serin peptidase inhibitor (serpin), apolipoprotein and complement proteins. The PPI interaction network built upon the restricted dataset including the 73 immune-related genes belonging to the biological processes shown to be overrepresented also displayed significant enrichment (p < 1. 0e-16, 561 edges found, 13 expected). The network had a hierarchical structure, with the main cluster including the fibrinogen and serine peptidase inhibitor (serpin) proteins, and two other clusters of highly connected proteins, one including apolipoprotein A and haptoglobin, and the other, complement proteins (Fig.
4 ). For the R. rattus dataset, GO annotations were available for 75 of the 83 DE genes identified (Suppl. Table  S1b ). Eight of these annotated DE genes related to immune biological processes (i. e. , 10. 66% of all DE genes; Suppl. Table  S1b ). We identified no significantly enriched biological process or pathway, even when redundancies were considered with Revigo. The protein-protein interaction network built from String displayed no significant enrichment (p = 0. 09). Similar conclusions were obtained when the dataset was restricted to the 12 genes found to be differentially expressed, with site considered as a random effect within invasion category.
Acknowledgements We thank  Khalilou Bâ ,  Mamoudou Diallo ,  Mamadou Kane ,  Laurent Granjon ,  Youssoupha Niang ,  Aliou Sow  and  Philippe Gauthier  for their help in collecting rodents, and  J. -M. Duplantier  for invaluable advice regarding field work and rodent invasion history. We are indebted to all the Senegalese people who allowed us to trap rodents in their homes. This work was supported by the  ANR   ENEMI  project ( ANR-11-JSV7-0006 ), and by the  French Embassy in Senegal , which provided the funding for PhD scholarships. This preprint has been reviewed and recommended by  Peer Community  in Ecology (https ://dx. doi. org/10. 24072/pci. ecology. 10001 1).
0%) HIV-uninfected women from general population consulting the CeGIDD for vaccination and STIs screening, prevention, prophy-laxis, and care. After completing the socio-demographic data questionnaire, the included women underwent cervicovaginal swabbing for HPV molecular analyses and genital sampling for Pap smear test. Venipuncture blood sample was obtained from each woman for HIV and STIs screening. Finally, feedback on results was returned to each women and medical treatment, care and follow-up was given appropriately if needed No data is available concerning HPV prevalence in African immigrant women living in France. To estimate the minimum sample size for this study we have used the minimum HPV prevalence reported for immigrant women living in Italy  [14] , a neighboring country of France with sub-Saharan immigration.
The CeGIDD allows fast access to screening and treatment. The main inclusion criteria were originating from sub-Saharan Africa and, never having undergone molecular or cytological screening for HPV infection and precancerous lesions. The included women have signed informed consent, were aged between 25 and 65 years, were sexually active, had no genital troubles at physical examination and had completed the study questionnaire. Exclusion criteria included age less than 25 years and more than 65 years, having previously undergone molecular or cytological screening for cervical HPV infection and precancerous lesions since the arrival in France, not willing to participate to the study or to answer the face-to-face sociodemographic and medical data questionnaire. All women from general population reaching these inclusion criteria were included in the study.
Knowing that our inclusion criteria were particularly specific and selective, the recruitment of women was extended to African immigrant women followed-up at the Centre Hospitalier Régional d'Orléans for known HIV infection. After signed the informed consent form, the included women benefited from free STIs screening including HIV, hepatitis B (HBV) and hepatitis C (HCV) viruses, Chlamydia trachomatis, Neisseria gonorrhoeae, genital herpes and Trichomonas vaginalis. All women received an information session on HIV, STIs and cervical cancer, and completed a face-to-face questionnaire that included socio-demographic characteristics and behavioral data such as age, marital status, social occupation, education level, past-history of STIs, HIV status and also sexual behavioral characteristics such as the number of lifetime sexual partners, frequency of condom use and the age at first sexual intercourse.
Samples and Processing After completing the medical and socio-demographic data collection questionnaire, a nurse performed cervicovaginal sampling using a flocked swab (Copan Diagnostic Inc. , California, USA), frozen at -80 °C. Cervicovaginal swab were transported in frozen ice packs, to the virology laboratory of the Hôpital européen Georges Pompidou, Paris, France, for molecular analyses. After the sample procedure, included women were referred to the gynecologist to undergo Pap smear cytology. Cytological results were classified according to the Bethesda system 2001  [16]. Samples were classified as atypical squamous cell of undetermined significance (ASCUS), lowgrade squamous intraepithelial lesions, high-grade squamous intraepithelial lesions, adenocarcinoma in situ and carcinoma. Cytology was evaluated by cytoscreeners and pathologists blinded to the outcomes of HPV testing.
Acknowledgements All the authors are very grateful for the health staff of the  Service des maladies infectieuses et tropicales, Centre Hospitalier Régional d'Orléans, La Source, France  and the  Centre Gratuit d'Information, de Dépistage et de Diagnostic (CeGIDD)  who were strongly involved in the recruitment of women. We are grateful to the women who accepted to participate to the study. We also would like to thank  Ms Christine Ombuna  ( Nairobi University, Kenya ) for her help to edit the English language of the paper.
However, to avoid data duplication in dominance and frequency computations, we excluded four nests sampled in the autumn of 2019 from analyses, as they were obtained from the same setts as four nests sampled in the spring of the same season. Consequently, the final number of nests analysed in this study was 32. Badgers throw away the nest material from the sett and leave it near the entrance-on top of the soil heaps or just below. The volume of nest material differed between setts and its dry mass varied (0. 2-2. 9 kg). Nest material consisted mostly of grass and Scots pine needles, and partially it was mixed with soil excavated from the burrow. All the organic material was sampled and transported to the laboratory. The mites were extracted with Tullgren funnels for 6 days, and preserved in 75% alcohol.
3. 5. 3 software (R Core Team 2019). To obtain a normal or at least symmetric distribution of data, they were transformed with a logarithmic or exponential function. However, extensive analysis of seasonal changes in mite communities was not the aim of this paper. For Uropodina community analysis, the data from both seasons were pooled and subjected to detrended correspondence analysis (DCA, gradient length = 6. 61) performed with CANOCO v. 5 software  (Šmilauer and Lepš 2014). To investigate relationships between badger nests and nests of other mammals that collect plant material in underground chambers, we used earlier published data from nests of moles  (Błoszyk 1985). Numbers of mite individuals from mole and badger nests were standardized for DCA and expressed as proportions of given mite species to all species per sample.
Rate constants of association and dissociation were calculated from reference-corrected sensorgrams by fitting to a global 1:1 Langmuir binding model using BIAevaluation software (BIAcore). The equilibrium dissociation constant (KD value) was calculated as k off /k on. To test competitive binding of the lipocalin variants P3D11, P3A12 and P1E4 to immobilized hCD98hcEDg (cf. Supplementary Information), 100 nM of P3D11 was first injected, followed by an injection of either 100 nM P3A12 or 100 nM P1E4. Injections of HBS-T served as negative control (cf. Figure  S2E ).
Circular dichroism (CD) spectroscopy and thermal denaturation CD spectra and thermal unfolding of purified lipocalin variants were measured using a J-810 spectropolarimeter (Jasco, Pfungstadt, Germany) equipped with a PT-423S Peltier element and controlled by Spectra manager software (ver. 1. 53. 05). Proteins were dialyzed against 20 mM KPi pH 7. 5, 50 mM K 2 SO 4 and applied at a concentration of 1 µM. In order to identify the wavelength with maximum change in CD signal for thermal denaturation studies, spectra were first recorded with a 1 mm path length quartz cuvette (Hellma, Müllheim, Germany) from 190 to 250 nM both at 20 °C and 90 °C. Thermal unfolding of the lipocalin variants was then measured at a wavelength of 214 nm by heating from 20 °C to 90 °C at a rate of 60 K/h. Data were fitted to an equation for a one-step unfolding transition, and the melting temperature (Tm), enthalpy of unfolding (∆H m ) and Gibb's free energy of unfolding (∆G 0 ) at ambient temperature were calculated as previously described  [65].
Radiochemical purity of the protein tracer was confirmed by radio-TLC prior to its use for injections. For the biodistribution study, mice were injected with the protein tracer via the tail vein at a dose of 3. 96 ± 0. 12 MBq. For blocking experiments, a 100-fold or 250-fold molar equivalent of unlabeled D11vs-PAS200 was injected 2 h prior to the tracer injection. Static PET/CT imaging was performed at indicated time points under isoflurane anesthesia in an Inveon PET/CT small-animal scanner (Siemens Medical Solutions, Erlangen, Germany) with 20 min PET and 5 min CT signal acquisition. Data were analyzed with the Inveon Research Workplace software (Siemens Medical Solutions) and reconstructed using the 0. 8 mm high resolution OSEM-3D algorithm. Quantification of regions of interest (ROI) was performed by threshold-based image segmentation with a lower threshold at 50% of the hottest voxel for in vivo PET and a threshold of 10 kBq/mL for ex vivo PET images.
The slide was placed, together with a 1:2 dilution row of the radiotracer, in silicon isolators (Grace Bio-Labs, Bend, OR) on a storage phosphor screen BAS-IP super resolution film (GE Healthcare) for one week, followed by quantification using a CR 25 BIO autoradiography scanner (Dürr Medical, Bietigheim-Bissingen, Germany) and data analysis with AIDA software ver. 4. 24. 036 (Raytest, Straubenhardt, Germany). Pictures chosen show the sections with the highest exposure level in order to allow optimal comparison of blocked and unblocked tumor sections.
Briefly, slides were deparaffinized using deparaffinization solution (Leica Biosystems), pretreated with Epitope retrieval solution 1 (corresponding to citrate buffer, pH 6) for 30 min (CD98hc) or pretreated with Epitope retrieval solution 2 (corresponding to EDTA based buffer, pH 9) for 30 min (CD31). Bound antibody was detected with a polymer refine detection kit without post primary reagent and with an intermediate rabbit anti-rat secondary antibody (diluted 1:400; Leica Biosystems) and signals were developed with 3,3'-diaminobenzidine (DAB). Representative images were collected on an Aperio AT2 digital pathology slide scanner using ImageScope (ver. 12. 3) software (both from Leica Biosystems).
Interestingly, binding of P3D11 causes an induced fit on the side of hCD98hcED. Superposition of the hCD98hcED Cα positions in the complex with P3D11 with those for hCD98hcED crystallized alone (PDB entry 2DH2)  [70] , excluding the epitope loops L1 and L2, revealed an RMSD value of 0. 97 Å (for 375 equivalent Cα positions), whereas both loops L1 and L2 show a larger deviation in this context by on average 1. 6 and 1. 8 Å, respectively (Figure  S5A ). Likewise, P3D11 shows conformational differences among its loops #1-#4 compared with wtLcn2 (PDB code 1L6M); yet, these may also be caused by the many sequence changes (Figure  S5B ), as observed for other Anticalin structures before. Although the selection of P3D11 was performed with the unglycosylated hCD98hcED, its affinity for the glycosylated hCD98hcEDg was indistinguishable (Tables  1  and S3 ).
Concomitant immunohistochemical analysis of vessel distribution (via staining of CD31) as well as hCD98hc abundance, using cognate antibodies, revealed a well-vascularized tumor with high expression of hCD98hc for both PC-3 and Ramos xenografts (Figures  5D  and S11 ). In order to confirm these imaging results and to obtain quantitative data from ex vivo biodistribution experiments at the optimal time point 24 h p. i. , a second study using male CB17-SCID mice bearing PC-3 tumors was conducted (Figure  6 ). Two cohorts, each with five mice, were injected with either 3. 96 ± 0. 12 MBq 89 Zr-labeled D11vs-PAS or with the protein tracer in combination with a 250-fold molar amount of the unlabeled PASylated Anticalin (at t = -2 h). After 24 h, PET/CT scans were performed, tumors were explanted, subjected to an ex vivo PET scan and, finally, a biodistribution analysis was conducted.
Acknowledgements The authors wish to thank the  Helmholtz-Zentrum Berlin, Germany , for allocation of synchrotron radiation beamtime and financial travel support as well as  Christian Feiler  for his support at the beamline 14. 2. Furthermore, we thank  Klaus Wachinger  and  Marie Motyka  ( Lehrstuhl für Biologische Chemie, TUM ),  Birgit Blechert ,  Miriam Bräuer ,  Markus Mittelhäuser  and  Sandra Schäfer  ( Klinik für Nuklearmedizin, TUM ) as well as  Olga Seelbach  ( Institute of Pathology, TUM ) for excellent technical assistance. We are grateful to  Profs. Bernhard Küster  and  Hannelore Daniel  ( TUM ) for providing the cell lines Raji, SU-DHL-4 and Caco-2 and to  Prof. Manuel Palacín  and  Sara Cano  (both at  IRB Barcelona)  for generously providing the  HEK293T  and its CD98hc knock-out cell lines.