dataesr/scientific-paragraphs-categorization huggingface dataset:
2 NA) objective. Around every 3 s, 10 and 79 frames were recorded before and after photobleaching with a 488 nm laser at a spot of 2 µm diameter. All these settings and manipulations of the microscope were performed through ZEN2008 (Zeiss) software. The cell labeling and acquisition took approximately 1. 5 h in each condition. The image processing and analysis were performed as described in refs. 96,97. In brief, after processing raw data in each measurement, the averaged fluorescence recovery in each experiment was fitted using the program, easyFRAP  97  (stand-alone ver. , http://ccl. med. upatras. gr/tools-easyfrap/) and MATLAB (R2023a, Mathworks, MA) to run the program, and the diffusion coefficient was calculated after calculating half-recovery time in easyFRAP. To draw a recovery curve, the mean and standard deviation at each time point were calculated from three means of the three independent experiments (n = 7-11 in each) and plotted as a function of chase time.
83 (Becker & Hickl). Data analysis. FLIM images were analyzed using a commercial software package (SPCImage V8. 1, Becker & Hickl, Germany). A binning of two (5 × 5 pixels (pixel size was around 230 nm)) was applied before processing the fluorescence decays. The FLIM data were further analyzed to obtain the lists of τ 1 and α 1 in each pixel within a cell and the FLIM diagram plots, using a homemade R script named flimDiagRam (V. 0. 1: https://doi. org/10. 5281/zenodo. 8408457)  79, 103, 104. In the twocomponent analysis, two populations are assumed to contribute to the EGFP-NT-Lys decay profile with one population consisting of nontransferring EGFP-NT-Lys molecules (more than 10 nm apart from an acceptor) and one population of EGFP-NT-Lys molecules with one or several acceptor molecules in close proximity (<10 nm), so that FRET can occur.
These τ 1 and α 1 values were plotted to draw 1D distribution in Figs. E = 1 À τ DA τ D ð2Þ where τ DA and τ D are the lifetimes of the donor in the presence and absence of the acceptor, respectively. To find a correlation between Gag-mTagBFP2 intensity and either lifetime τ 1 and amplitude α 1 , we utilized a homemade imageJ-MATLAB program to extract τ 1 and α 1 values in an ROI corresponding to EGFP-NT-Lys signal from a dataset analyzed by SPCImage. The obtained values were sorted into quartiles of mTagBFP2 intensity and then plotted in every quartile. Pearson correlation coefficient was calculated between 4 means of the τ 1 or α 1 values and ranks of mTagBFP2 intensity using GraphPad Prism 5. 04 (GraphPad Software, MA). In this analysis, we set the ranks (1 to 4) corresponding to 4 quartiles (0-25%, 25-50%, 50-75%, and 75%-100%) in order.
The slides were kept in the dark until the mounting medium hardened. The slide was observed under the microscope FV1000 (Olympus, Japan) equipped with a PlanApo 60×WLSM (1. 0 NA) objective (Olympus). Fluorescent images were acquired using 405, 543 and 633 nm laser for excitation of Hoechst33342, JF549, and AF647, respectively under the control by FV10-ASW4. 2 (Olympus) software. The representative images are shown in Supplementary Fig. 1.
Acknowledgements We are grateful to the  NIH- AIDS Reagent Program   (ARP)  for providing us Gag and Gag mutant plasmids. We acknowledge the  Imaging Center PIQ-QuESt  (https://piq. unistra. fr/), a member of the national infrastructure France-Bioimaging supported by the  French Research Agency  ( ANR-10-INBS-04 ). Y. M. is grateful to the  Institut Universitaire de France (IUF)  for support and for providing additional time to be dedicated to research. This work was supported by  Agence Nationale Recherche sur le Sida et les Hépatites Virale   18365  (T. K. ),  Agence Nationale de la Recherche   A20R417C  (T. K. ), and  RIKEN Glycolipidologue Program  (T. K. ).
Antibiotic audit All data were extracted from electronic patient files, including patients' entire medical history and all laboratory results. Healthcare-associated infections were defined as a diagnosis established ! 48 h after hospital admission, and when observed fewer than 30 days after a urological procedure or after Day30 when observed in association with an underlying urinary catheterization. All other infections were classified as community-acquired  [12]. Co-morbidities were defined by prescription of a specific treatment prior to hospital care, or if the diagnosis was newly established during the hospital stay. Loss of autonomy, defined by ongoing neurological and/or psychiatric diseases limiting postoperative rehabilitation, was specifically reported as it could increase the duration of urinary catheterization and/or intravenous catheter, with the device becoming a source of bacteremia.
Differential gene expression was evaluated by fitting a negative binomial generalized linear model and differential expression was determined using the quasi-likelihood F-test  (41). For further bioinformatics analysis genes were filtered by p value < 0. 05 and log 2 fold change ≥ |1. 5|. Heat maps were design using the excel spreadsheet and the clustvis tool (https://biit. cs. ut. ee/clustvis/), gene grouping was performed using the "Gene list analysis" Panther tool (http://pantherdb. org/). Volcano plot was performed in the R environment using the EnhancedVolcano package. Pathway analysis were performed using "Ingenuity Pathway Analysis" (QIAGEN) software. Data were stored in an NIH repository (https://www. ncbi. nlm. nih. gov/sra) under accession number PRJNA855854.
Analysis of publicly available human tumor datasets Data corresponding to the expression level of IL-17RA, IL-17RC and IL-17D transcripts in human tumor samples were obtained from cBioPortal for Cancer Genomics (https://www. cbioportal. org/). This online tool allows the visualization, analysis and download of tumor genomics datasets from multiple databases. For the present work, mRNA expression data of transcripts from 34 RNA-seq datasets of cancer studies belonging to the TCGA database were downloaded in November 2021. The data were processed in Phyton environment. Overall survival analysis (defined as time to death), harmonized RNA-seq bulk data provided by TCGA, obtained from 472 melanoma samples (TCGA-SKCM) or 151 acute myeloid leukemia samples (TCGA-LAML), were used. The median gene expression level of IL-17RA, IL-17RC or IL-17RD were used as a cut-off point to segregate cancer patients into two groups: low and high expression.
Interestingly, examination of the 49 genes of the "IL-17 pathway" retrieved from the KEGG database revealed that 28 genes (57%) showed contrasting expression between B16. SIY and EL4. SIY cells. From these 28 DEGs, selected genes are highlighted in red in the volcano plot from Supplementary figure  3A , and the complete list is depicted in a clustered heatmap of Supplementary figure 3B. Altogether, these results underscored that cells from different tissue origin show different patterns of expression of cytokines and receptors of the IL-17A/IL-17RA pathway, paralleled with particular activation of the IL-17 related gene signature.
Funding Research reported in this publication was supported by:  Agencia Nacional de la Investigación, el Desarrollo Tecnológico y la Innovación  ( PICT 2018-01791  and  PICT 2020-0487 ),  Secretaría de Ciencia y Técnica-Universidad Nacional de Córdoba  and the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under Award Number  R01AI169482. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. provided the cell lines and protocols for cell culture. CR, SB, and SNB thank  CONICET  for the fellowship awarded. LF, DGC, CM, AG and EVAR are members of the Scientific career  of CONICET. EP and JT are supported by the  LabEx DCBIOL  ( ANR-10-IDEX-0001-02 PSL ;  ANR-11-LABX-0043 );  SIRIC INCa-DGOS-Inserm   1255 ; and  Center of Clinical Investigation (CIC   IGR-Curie 1428 ).
○ Taq High Fidelity polymerase enzyme (ThermoFisher Scientific). The purified PCR products were used for sequencing using the Ion PGM sequencer (Life Technologies). CLC genomics software v. 21 (Qiagen) was used to process reads. Reads were mapped to the reference sequence (GenBank accession no. KP694240) for at least 50% of its length with a minimum of 80% identity. Additionally, a partial genome sequencing of the NSs gene was performed directly on available positive CSF samples as described earlier  [18].
TOSV strain sequencing Whole genome sequencing of the isolated TOSV strain confirmed the identity with 1786 nts for segment S (GenBank accession no. ON165939), 4215 nts for segment M (GenBank accession no. ON165938), and 6371 nts for segment L (GenBank accession no. ON165937). The phylogenetic analysis of the 3 sequences revealed that the isolated strain belonged to lineage A ( Figure  4  ) and was grouped with previous sequences of Algeria, Tunisia, and Italy. Interestingly, the analysis of each segment revealed a high degree of genetic similarity with a previously identified Algerian strain  [11]  , with 100% identity for segment L and 97. 2% for segment M. In contrast, the S segment displayed a lower similarity (90. 8%). The partial NSs sequences obtained from samples 2/16 and 3/16, with respectively, 492 nts (GenBank accession no.
OP850823) and 894 nts (GenBank accession no. OP850824), were blasted and compared to the previous GenBank sequences of the S segment. The phylogenic analysis showed that the two strains belong to lineage A ( Figure  4  ). Interestingly, the strain (GenBank accession no. OP850824) was most closely related to the Tunisian strain, while the strain (GenBank accession no. OP850823) was highly similar to the previously identified strains in Algeria  [11]  and the strain isolated in this study.
Acknowledgments The authors thank  Dr. Belgacem Mihi  for his critical reading of the manuscript and  Dr. Dahbia Leila Anes-Boulahbal  and his team for their technical assistance,  Dr. Fouzya Benguergoura ,  Dr. Salim Kezzal  for providing us with newborn mice,  Oumar Ndiaye  ( Institut Pasteur of Dakar ) for providing the cell lines. The authors are grateful to all the National Public Health physicians and staff who contributed to this work. The authors also thank  Dr. Djamel Fourar  and  Dr. Samia Hammadi  ( Algerian Health Ministry ) for their collaboration and excellent support.
These data are consistent with a bipartite interaction between Aap lectin domain and both protein and N-glycan moieties of a corneocyte receptor rather than a simple lectin-glycan interaction alone. Previous force spectroscopy studies have revealed the remarkably high mechanostability of the B-repeat regions of both Aap and SasG  29, 44. Given the direct connection between the lectin domain and the start of the B-repeat superdomain (Fig. 3 ), we speculate that the unique mechanical properties of the unusual B-repeat protein fold  26, 36  may also play a role in strengthening the Aap-corneocyte interactions. The structure of SasG lectin is highly similar to Aap lectin, with the same atypical architecture resulting from the trans aspartate residue (D240) that interacts with the amide nitrogen of W392, placing an aromatic residue similar to Y580 at the base of the binding pocket.
7 ). The corneocyte adhesion assays (Figs  6  and 9 ) provided valuable insights into inter-species competition for colonizing the skin niche. Soluble Aap lectin domain inhibited corneocyte adhesion not only by S. epidermidis, but also by S. carnosus-sasG; likewise, soluble SasG lectin domain inhibited S. epidermidis adhesion. These data indicate that both S. epidermidis and S. aureus directly compete for the same corneocyte receptor. Both lectins were inhibited by N-acetyllactosamine and sialyllactosamine with similar dose responses. Given the near-ubiquitous colonization of skin by S epidermidis, S. aureus would need to out-compete S. epidermidis for interaction with the same (or a highly similar) corneocyte receptor (Fig. 10b ). However, S. aureus is known to express other CWA adhesins with specificity for corneocyte or keratinocyte receptors; co-expression of SasG with one or more of these additional adhesins may provide a critical advantage when competing with established S.
Flow through was then applied to a Superdex 75 column and purified lectin fractions were pooled. Likewise, SasG lectin (residues 163-419 from S. aureus 502a) was expressed using the pDest-HisMBP plasmid and purified as described for Aap lectin. Phasing, Refinement, and Validation. Structural determination for Aap lectin was carried out by S-SAD phasing through the detection of three anomalous scattering atoms: sulfur atoms from two methionine residues and a bound calcium. Data was processed by the RAPD server available through NE-CAT (https://rapd. nec. aps. anl. gov/rapd); data indexing, space group assignment, scaling, and integration were carried out in RAPD via XDS  74. Shelx 75 is utilized in the RAPD suite followed by PHENIX AutoSol (HySS, PHASER, RESOLVE, Xtriage, PHENIX-Refinement)  76  to determine anomalous scatterers, scatterer positions, calculation of experimental phases, density modifications, model-building, and refinement.
Protein Crystallization and Data Glycan array screening. Aap lectin was dialyzed into PBS pH 8. 0, concentrated to 2 mg/ml, and labeled with AlexaFluor 488 (10 μl of 10 mg/ml stock solution). The reaction was allowed to incubate at 4 °C for 48 h. Labeled Aap lectin was dialyzed into BisTris pH 6. 5, 150 mM NaCl to quench the labeling reaction and remove free unbound AlexaFluor. Labeled Aap at 0. 5 mg/ml was sent for glycan array screening by the Center for Functional Glycomics, using array version 5. 4 consisting of 585 distinct glycans. The binding buffer contained 20 mM Tris-HCl, pH 7. 4, 150 mM NaCl, 2 mM CaCl2, 2 mM MgCl2, 0. 05% Tween 20, and 1% BSA. Data were reported as average RFU from replicate measurements (4 values averaged from 6 original replicates, after discarding the highest and lowest values per condition) along with standard deviation and %CV (%CV = 100x standard deviation / Mean).
Intensity measurements of scattering were taken using a Dectris Pilatus3 X 1M detector. Over a time course of 1 second, samples were exposed for 0. 5 seconds. Brpt1. 5 SAXS data were collected at The Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory using the mail-in HT-SAXS service on SIBYLS beamline 12. 3. 1  [80] [81] [82] [83]. Brpt1. 5 was dialyzed into 50 mM MOPS pH 7. 2, 50 mM NaCl, 2% glycerol and data collected on samples at 5 mg/mL, 6. 7 mg/mL, 10 mg/mL, and 15 mg/mL. SAXS analyses were performed in BioXTAS RAW (version 2. 1. 0)  84. The concentration series collected for Brpt1. 5 was extrapolated to zero concentration in Primus from ATSAS (version 3. 0. 3)  85  and then analyzed in BioXTAS RAW alongside the Lectin and Lectin-Brpt1. 5 datasets. To model flexibility of Lectin-Brpt1.
5, SASSIE's monomer Monte Carlo module 86 was used to generate 50,000 models. After removing clashing models, approximately 12,000 models remained. Flexible regions were allowed to sample up to 30° during each move. Flexible regions included the Lectin-Brpt1. 5 hinge region (604-610) and the interfaces between each G5 and spacer domain of Brpt1. 5 (686-689 and 735-738). CRYSOL 87 in non-fitting mode was used to generate SAXS scattering curves for each model, while also generating input files for the Ensemble Optimization Method (EOM) 88, 89 using a custom python script  43, 90. CRYSOL settings were: max order of harmonics = 99; order of Fibonacci grid = 18; number of points = 101. CRYSOL was also run in fitting mode with the same settings to obtain single-model fits of the experimental dataset.
Nuclear magnetic resonance spectroscopy. Uniformly 2 H, 12 C, 15 N-labeled Aap lectin was expressed and purified as described above. All NMR relaxation experiments (Heteronuclear 91 -15 N NOE, T1, and T1ρ experiments) were collected at 298K on a Bruker AVANCE III 600 MHz spectrometer with 0. 5 mM lectin in NMR buffer (20 mM HEPES, pH 6. 8, 50 mM NaCl) containing 10% D2O. The NMR data was referenced for 1 H chemical shifts by using 2, 2-dimethyl-2-silapentane-5sulphonic acid (DSS) at 298 K as a standard. The 13 C and 15 N chemical shifts were referenced indirectly. The steady-state heteronuclear { 1 H}-15 N NOE was measured as the ratios of the peak intensities with and without proton presaturation period (4 s) applied before the start of the 1 H-15 N TROSY HSQC experiment. The T1 and T1ρ experiments were recorded using a pulse program described by Lakomek et al.
Titrations in the presence of 20 mM lactosamine were collected at 298 K at 600 MHz under similar conditions as described above, using 0. 5 mM 15 N labeled lectin in NMR buffer. Twenty injections of glycan were delivered, the first of which was 2 μl followed by nineteen injections of 14 μl each. Controls experiments were performed with 1 mM glycan in the syringe and used to correct for heat of dilution. All lectin protein concentrations were calculated using either a ThermoScientific NanoDrop or a ThermoScientific BioMate 3S UV Spectrophotometer, where the absorbance at (A280) at 280 nm was measured. Glycan concentrations were calculated based on resuspension of purchased lyophilized glycans. Fitting of all ITC data to binding models was carried out using ORIGIN software.
Acknowledgements The authors gratefully acknowledge expert advice and support from  Drs. Frank Murphy ,  Jesse Hopkins , and  Srinivas Chakravarthy  from  Argonne National Laboratory. Y. F. D. thanks  Dr. Joan Geoghegan  for providing corneocytes for AFM analysis. This research was conducted with  NIH  funding through  NIGMS  ( R01 GM094363  to ABH) and  NIAID  ( R01 AI162964  to PDF, ARH, and ABH;  R01 AI153185  to ARH; and  R01 AI139479  to NCF). Work at UCLouvain was supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement  n°693630 ), and the  National Fund for Scientific Research (FNRS). Glycan array screening was carried out by the  Protein-Glycan Interaction Resource  of the CFG (supported by  NIGMS  grant  R24 GM098791 ) and the  National Center for Functional Glycomics (NCFG) at Beth Israel Deaconess Medical Center, Harvard Medical School  (supported by  NIGMS  grant  P41 GM103694 ).
This research used resources of the Advanced Photon Source, a  U. S. Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science  by  Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. Diffraction data were collected at the  Northeastern Collaborative Access Team (NE-CAT)  beamlines, which are funded by the  NIH  through  NIGMS  ( P30 GM124165 ). The  Eiger  16M detector on 24-ID-E is funded by a  NIH-ORIP HEI  grant ( S10OD021527 ). SEC-MALS-SAXS data were collected at the  Biophysics Collaborative Access Team (BioCAT) beamline , supported by  NIH  grant  P41 GM103622  through  NIGMS. Use of the Pilatus 3 1M detector was provided by grant  1S10OD018090  from  NIGMS. Brpt1. 5 SAXS data collection was conducted at the  Advanced Light Source (ALS) , a national user facility operated by  Lawrence Berkeley National Laboratory  on behalf of the  Department of Energy, Office of Basic Energy Sciences , through the  Integrated Diffraction Analysis Technologies (IDAT)   program , supported by  DOE Office of Biological and Environmental Research.
Bioinformatic smORF peptides prediction in imaginal leg discs Imaginal leg discs were dissected in cold PBS 1X on ice from wandering L3 larvae, before the ecdysone peak, visualized by pre-spiracle eversion, and 2 hours After Pupal Formation (APF). Total RNA was extracted with Trizol reagent (Ambion) according to the manufacturer's protocol. Construction of RNA polyA+ bank and sequencing using paired-end 100bp reads were performed by IntegraGen. Kallisto  [55]  was used for pseudo alignement of reads to a reference combining the Ensembl 74 annotations and additional lincRNAs from modENCODE  [56, 57]. We then used Sleuth for differential expression analysis  [58]. Small ORFs were predicted as in Mackowiak et al. [18]  using the transcriptomes generated in this study. The data discussed in this article are available via the Gene Expression Omnibus (GEO) under accession number GSE225561.
Acknowledgments We thank  Jan Yuh Nung ,  Richard Mann ,  Carlos Estella  and  Helène Chanut-Delalande  for sharing antibodies and fly lines. We also thank the  Developmental Studies Hybridoma Bank  for antibodies, the  Bloomington Drosophila Stock and Vienna Drosophila Ressource Centers  for fly strains. We thank LITC platform for imaging (https://www-litc. biotoul. fr/). We thank  Magali Suzanne  for critical reading of the manuscript. We thank  Helène Chanut-Delalande  and  Cedric Polesello  for helpful comments,  Alexia Rivero  for helping visualizing sequencing data and  Philippe Valenti  for experimental assistance.
This work was supported by grants from the  Agence Nationale de la Recherche  to J. Z. ( ANR-17-CE13-0002 ), to Mu. B. ( ANR-16 CE12-0021-01 ) and to Fr. P. ( ANR-14-CE11-0010 ). D. M. was supported by PhD fellowships from  Le ministère de la Recherche et de l 'Enseignement Superieur of Svb transcription factor. stage and to preserve tissue integrity. Therefore, the pleiotropy of Pri peptides enables the activation of distinct targets over time required for the harmonious development of the legs. and  La Ligue Contre le Cancer. A. P. was supported by  ANR-17-CE13-0002  grant and  Association pour la Recherche contre le Cancer  foundation. D. M. , A. P. ,Mu. B. , Fr. P. and J. Z. are supported by institutional basic support from the  Centre National de Recherche Scientifique (CNRS)  and  Toulouse III University.
Surveillance and minimum data elements Surveillance is a critical component of national control programmes. Despite rabies being one of the deadliest diseases known to man, in practice it remains a hidden disease due to poor surveillance and underreporting. Rabies-related data span the One Health spectrum, with human deaths and human vaccination data collected by human health authorities (most often Ministries of Health), whereas animal rabies cases and animal vaccination data are often collected by Veterinary Authorities (often Ministries of Agriculture). This has led to constraints in data sharing, and the development of numerous and overlapping guidance documents and data definitions. Data should be collected for a specific purpose, be that informing interventions (e. g. , Post-Exposure Prophylaxis (PEP) or dog vaccination programmes), monitoring trends, or evaluating the effectiveness of disease programmes.
The SARE assessment helps users to assess the strengths and weaknesses of a national rabies control programme, assists the user with developing a rabies workplan, and provides a SARE score indicating the current rabies situation across a country-a score which provides the basis for the roadmap score. The roadmap builds upon this, by bringing together specific tools and resources, including the aforementioned working group outputs, that are readily available to address pending activities in the country's canine rabies elimination work plan, and thereby provide tangible steps for countries to take to progress to the next milestone. For example, national stakeholders at an early stage of the roadmap with no national strategic plan will be directed to the national strategic plan template, stakeholders identifying a need to improve surveillance will be directed to the minimum data elements document, and specific activities identified within a country workplan will link to tools in the UAR toolbox that can help in implementing these activities.