dataesr/scientific-paragraphs-categorization huggingface dataset:
Antigenicity screening of the retrieved protein sequences Using the Vaxijen V2. 0 server program (http://www. ddg-pharmfac. net/vaxijen/VaxiJen/VaxiJen. html)  (Doytchinova and Flower, 2007b) , the antigenicity of the retrieved protein sequences was evaluated at a threshold value of 0. 5. The Vaxijen investigates the antigenic protein's capacity to bind to or interact with B-cell receptors. Prediction of membrane protein topology and signal peptides TMHMM Server 5. 0 (https://services. healthtech. dtu. dk/services/TMHMM-2. 0/) was used to predict the transmembrane structure of proteins. In order to evaluate if the proteins are constituted within the membrane or not, the protein sequences were uploaded into the server window and three regions (S), comprising the inner and outside transmembrane were examined  (Möller et al.
Reverse translation and codon optimization The backtranseq EMBOSS 6. 0. 1 program was used to translate the constructed B-cell epitopes into a sequence of nucleotides, from which the GC content ratio was determined. (https://www. ebi. ac. uk/Tools/st/embossbacktranseq/). Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons  (Oladipo et al. , 2022). Codon optimization tools are online bioinformatics resources that determine the gene expression level of the multipleepitopes in the vectors and consequently, they enhance the optimal expression of a foreign gene (multiepitope)  (Olatunde et al.
Tertiary construct of the B-cell epitopes and structural validation The B-cell epitopes tertiary structure construct was predicted with the I-TASSER servers and are shown in Figure  4. For each of the targets, I-TASSER simulations provide a vast ensemble of structural conformations, known as decoys. I-TASSER uses the SPICKER program to cluster all the decoys based on pair-wise structural similarity, and then reports up to five models that correspond to the five biggest structure clusters in order to choose the final models  (Kimmig et al. , 2021). The relevance of threading template alignments and the convergence parameters of the structure assembly simulations are used to calculate the C-score, which provides a quantitative assessment of each model's confidence. The values of the C-scores for models 1 through 5 are -1.
We determined dFe bioavailability in a comparable way across different sites using six high depth resolution profiles from the upper 500 m of the water column. The selected sites for the vertical profiles include three locations near the Balleny islands, two inside the Mertz glacier polynya, and one in the Ross Sea. The high-resolution nature of our shallow subsurface dataset enables us to investigate the influence of Fe speciation and various physicochemical drivers on dFe bioavailability in contrasting sites in the SO.
In the euphotic zone (0 to 100 m), dFe concentrations varied between 0. 02 nmol/liter (stations TMR15 and TMR16) and 0. 33 nmol/liter (station TMR14) and showed a near-linear trend with values reaching 0. 27 nmol/liter (station TMR12) to 0. 47 nmol/liter (station TMR15) at 500-m depth (Table  1 ). Three operationally defined ligand classes were identified: L 1 (logK 0 Fe 0 L > 12, n = 2), L 2 (11 < logK 0 Fe 0 L < 12, n = 22), and L 3 (logK 0 Fe 0 L < 11, n = 22), and Fe 0 concentrations were calculated as just 0. 01 to 6. 76 pmol/liter across the dataset. The strongest ligands (L 1 ) were only measured at stations TMR16 (open ocean) and TMR13 (islands) at 15-and 75-m depth, respectively. Ligands from the weakest class (L 3 ) were found at all stations but, particularly, at TMR12 (Mertz glacier polynya), where they were found in 9 of the 11 sampled depths.
dFe bioavailability cannot be inferred from conventional Fe speciation measurements To better understand the large range observed in dFe bioavailability among our samples, we further explored what factors are correlated in our dataset. The magnitude, direction, and significance of the correlation between selected variables are summarized in Fig. 5. Overall, we did not obtain any significant correlation between Fe uptake by P. antarctica and additional variables listed in table  S2  other than a spurious correlation with dFe concentration (computationally related; see Material and Methods for Fe uptake calculation). However, we did obtain a significant negative correlation between dFe bioavailability and eHS concentrations (r = -0. 33, P = 0. 04, n = 33). The absence of correlation between depth and Fe uptake rates further supports the argument that dFe bioavailability was not primarily driven by dFe concentration but instead was more strongly related to the nature of Fe sources and ligands.
Our dataset showed that both eHS and TPZT concentrations were anticorrelated with estimates of dFe bioavailability, suggesting that neither is the sole source of bioavailable Fe (Fig. 5 ). We found significant correlations between Fe uptake rates and L T [coefficient of determination (r 2 ) = 0. 65, P = 0. 001, n = 14] or dFe concentrations (r 2 = 0. 72, P < 0. 001, n = 15) only at stations TMR11 and TMR14. These two stations also exhibited the highest dFe bioavailability of all (70 to 165%), which can be attributed to their proximity to local Fe sources. However, they did not show the highest Fe 0 or eHS concentration, suggesting that Fe source matters in defining its bioavailability. While there is no strong relationship between Fe uptake rates and Fe 0 concentrations when considering the entire dataset, certain stations display correlations (Fig.
7%) for other ocean basins [northeast Pacific and the northwest Atlantic from  (29) ]. In our case, despite the clear contribution of eHS to L T (indeed, eHS shows a positive correlation with L T ; Fig. 5 ), other compounds are contributing to the bulk of ligands and may play a bigger role in dFe bioavailability. For instance, in contrast with eHS, biologically mediated ligands (e. g. , TPZT) were significantly correlated with dFe concentration in our dataset but not with L T. This points toward a disproportionate role of biologically mediated ligands on Fe biogeochemistry  (12, 63)  relative to their contribution to the total ligand pool in the SO. It highlights the significance in constraining regulations of dFe bioavailability and not simply dFe or Fe 0 and L T. This also supports recent results indicating that Fe recycling within the microbial loop [aka "ferrous wheel"  (64) ] is highly efficient in the SO, helping to increase the residence time of dFe in surface waters, and that particle-associated microbial respiration generates weak (L 2 ) ligands that form bioavailable FeL complexes  (63).
6‰ for complexation (ligands-dFe) and -0. 5‰ for biological uptake (phytoplankton-dFe) based on laboratory work or inferred from limited open ocean δ 56 Fe data  (9, 66, 67, 70). Our coupled dataset of Fe-binding ligands, dFe bioavailability, and δ 56 Fe offers a chance to tease apart these processes further. We found a negative correlation between dissolved δ 56 Fe and total ligand concentrations (r = -0. 62, P < 0. 0001, n = 38; Fig. 5 ), which might suggest against a role for complexation in driving dissolved δ 56 Fe toward higher values, in contrast to previous assumption  (73, 74). However, this outcome is not entirely unexpected. Different ligands are anticipated to have different binding strengths and fractionation factors, rendering the concentration of the total pool not be the most relevant parameter, as demonstrated above for dFe and bioavailability.
Further, if the isotopically heavy Fe is bound to strong Fe-binding ligands, which would have greater isotopic fractionation factors  (73, 74) , then we might expect this dFe to have lower effective bioavailability compared to Fe 0 or dFe bound to weaker ligands. Overall, our data demonstrate a link between δ 56 Fe, Fe uptake, and ligands, thus reinforcing the utility of isotopic analysis as a tool to identify Fe sources. However, understanding of the processes that control δ 56 Fe is complicated by competing processes and sources across the SO, with  (9)  finding that dataset for surface waters could not easily be described by a single fractionation factor. The only exception to this finding was for sites within the Mertz glacier polynya (TMR11 and TMR12), where δ 56 Fe and dFe in the top 200 m could be described with a closed Rayleigh fractionation factor α of 0.
Aliquots were analyzed the next day following an equilibration time of at least 12 hours. Hydrolysable carbohydrates were quantified by 2,4,6-tripyridyls-triazine (TPTZ) spectroscopy using the method in  (79, 80). Hydrolysable carbohydrates (later referred as TPZT from the analytical methodology used) is part of the labile pool of dissolved organic carbon, and these data are used to explore the nature of Fe ligands. This technique is based on the reduction of Fe 3+ into Fe 2+ , which reacts with TPTZ forming a blue complex absorbing at 595 nm. D-glucose was used as a standard. The instrumental detection and quantification limits were 2. 46 and 4. 29 μmolC/liter, respectively. eHS concentrations, abbreviated eHS and expressed in microgram equivalent per liter using terrestrial standards from the Suwannee River (SRFA), were determined by electrochemistry following standard additions of SRFA [standard 1.
Auxiliary parameters analysis Temperature and salinity were obtained from calibrated CTD (SeaBird SBE11, General Oceanics, Miami, FL, USA) mounted on the TM rosette. A body of chemical and biological parameters was tested for correlation with our analyses of Fe uptake rates (table  S2 ). Full dataset of these parameters and detailed methods can be found in  (9, 46, 57, 81)  for nutrients, TMs, and isotopes and (75) for biological parameters. Estimating the contribution of humic substances to the in situ ligand pool Similar to  (29) , the contribution of humic substances to the pool of in situ ligands was estimated using in situ eHS measurements as the amount of Fe-binding humic substances present. As two borderline situations, humics were considered either as weak (SRFA, 16. 7 nmol of Fe mg -1 , logK Fe 0 L = 10.
Statistics and data treatment All data are given as the means and SDs of three biological replicates unless otherwise indicated. Correlation coefficients between Fe chemical speciation, Fe uptake rates, and estimated dFe bioavailability were determined using Pearson Product Moment. Data visualization and statistical analyses (correlations and PCA) were performed using SigmaPlot 11. 0 (SysStat Software, CA, USA) and R Studio and package corrplot  (91).
8 and 75 mM NaCl, and were observed using confocal fluorescence microscopy. Confocal microscopy experiments were performed on a Nikon Eclipse TE2000 inverted microscope equipped with the software EZ-C1, and images were analyzed by ImageJ software. To obtain 3D images, an inverted Eclipse Ti-E (Nikon) confocal microscope equipped with spinning disk CSU-X1 (Yokogawa) and Live-SR (Gataca Systems) integrated with Metamorph software was used. Images were taken by Prime 95B (Photometrics) and were further analyzed using ImageJ.
The blotting was carried out on the opposite side from the liquid drop and plunge frozen in liquid ethane at -181°C using an automated freeze plunging apparatus (EM GP, Leica, Germany). The samples were kept in liquid nitrogen and imaged using a Tecnai G2 (FEI, Eindhoven, Netherlands) transmission electron microscope operated at 200 kV and equipped with a 4000×4000 CMOS camera (F416, TVIPS). For cryo-electron tomography, tilt series were collected in low-dose mode every two degrees from 0 to -34°, then from +2 to 60°, and finally from -36 to -60°to minimize irradiation at the lowest angles. The dose per image was set at 1 electron per Å 2. The imaging was performed at a magnification of 50,000. The consecutive images were aligned using the IMOD software (https://bio3d. colorado. edu/imod/) with the help of gold beads.
Acknowledgements We thank  Patricia Bassereau  and  Daniel Levy  for useful advice and discussions. We thank  Manos Mavrakis  for the kind gifts of the plasmids expressing human septins. We thank the  Labex Cell(n)Scale  ( ANR-11-LABX0038 ) and to Paris Sciences et Lettres ( ANR-10-IDEX-0001-02 ). We thank the  Cell and Tissue Imaging Platform (PICT-IBiSA) ,  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ).
Funding This work benefited from the support of the  Agence Nationale de la Recherche (ANR)  for funding the project ' SEPTIME ' ( ANR-13-JSV8-0002-01 ) and the project ' SEPTSCORT ' ( ANR-20-CE11-0014-01 ). B. C. is funded by the  Ecole Doctorale  ' ED564 :  Physique en Ile de France ' by  Sorbonne Universitéand the Fondation pour la Recherche Medicale. K. N. was supported by  Sorbonne Université(AAP Emergence). Open access funding provided by  Agence Nationale de la Recherche. Deposited in PMC for immediate release.
Tracheal resistance Tracheas were extracted from the mice and fit in Ussing chambers (Physiologic Instruments, San Diego, CA, USA). The spontaneous transepithelial potential difference (PD) was measured, and the tissue was adjusted at zero voltage by continuously introducing an appropriate short-circuit current with an automatic voltage clamp (Physiologic Instruments). Tissue ion resistance (1/G), where G is conductance, was measured from the potential difference and short-circuit current according to Ohm's law using the Acquire and Analyze software (Physiologic Instruments).
Analysis of microbiota composition Bacterial DNA was extracted from BAL pellets according to the manufacturer's instructions (genomic DNA from tissue, NucleoSpin™ Tissue; Macherey-Nagel, Düren, Germany). The microbiota composition was analyzed by 16S sequencing with permission from Biofortis (Saint-Herblain, France). Raw sequencing data were obtained from a single Illumina MiSeq run as 250 bp paired-end reads targeting the V3-V4 region (Primers: Bakt_341F 5′-CCTACGGGNGGCWGCAG-3′, Bakt_805R 5′-GACTACHVGGGTATCTAATCC-3) of the 16S rDNA gene. Reads were processed with microSysMics (https://bio. tools/microSysMics), a workflow built around the QIIME2 toolbox, chaining softwares in order to automatize metabarcoding analysis. PCR primers and remaining Illumina adapters were removed with Cutadapt.
5, CD4-APC Cy7, CD8-FITC and CD25-BV510. The cells were fixed and permeabilized using the Cytofix/Cytoperm Kit (BD Biosciences, Franklin Lakes, NJ, USA) and stained with Fox P3-Alexa647, GATA3-PE-Cy7 and RORγT-PE. The cells were identified by flow cytometry on a Fortessa X20 cytometer (BD Biosciences). Data were acquired using DIVA software (BD Biosciences) and analyzed with FlowJo (TreeStar, V10. 5. 3).
ACKNOWLEDGMENTS The author would like to thank the Cytocell platform for assistance with the flow cytometry analysis, and we would like to thank the  Therassay platform  for the use of respiratory equipment (Flexivent system, Plethysmography). We are grateful to the members of the  UTE IRS-UN animal facility  for the support they provided with the mice. We would also like to thank the MicroPicell platform for performing the histological preparation. We would like to thank  Stallergenes Greer  for kindly providing the HDM total extract. The authors thank the cluster LUNG innOvatiOn ( LUNG O2 ) for logistic support. We thank  M. DeCarvalho ,  A. Moui  and  C. Lebot  for their experimental help. The authors thank the  Genavie Foundation  for funding support. The authors thank the  Mibiogate consortium  for their funding, material and engineering support.
The authors thank  L. Boussamet  for collaborating on in silico microbiota metabolite analysis. The authors thank  M. Croyal  and  S. Crossouard  from the mass spectrometry platform of SFR-sante  F. Bonamy  for their collaboration. This work was supported by a grant from the  Mibiogate consortium  and the  Fondation du souffle, INRAe Transfert  and the  "Institut de Recherche en Santé Respiratoire des Pays de La Loire " IRSRPL. LUNG O2 is supported by the  National Research Agency  under the  Programme d'Investissements d'Avenir  ( ANR-16-IDEX-0007 ), the  Pays de la Loire Region research program  and by the  Institut de Recherche en Santé Respiratoire des Pays de la Loire". The Genavie Foundation  supported this project with a grant. https://doi. org/10. 4168/aair. 2023. 15. e9Sensitization  Route in an Asthma Mouse Model P r o v i s i o n a l P r o v i s i o n a l.
Data Availability The gene sequences of the sequence of ITS1 region obtained in this study were deposited with GenBank accession number: MT023531. 1 and GenBank accession number: MW053328. 1 corresponding L. major and L. donovani respectively. Multiple sequence alignment of ITS1 and nucleotide substitution carried by the best Bayesian Information Criterion score of representative parasite isolates from patients with those of L. donovani complex and L. major reference strains (Supplementary data S-IV). Visceral leishmaniasis (VL) endemic district of Nepal described in National Guideline on Kala-azar Elimination Program 2019 is available in the Nepal Government's EDCD (Epidemiology and Disease Control Division) web page http://edcd. gov. np/uploads/resource/5caca993d18db. pdf. Similarly the Endemicity status of foreign country can be viewed in WHO's webpage https://www.
Demographic and geographic distribution of cutaneous leishmaniasis cases In this study, interestingly a good numbers of suspected cases (n = 40) visited the hospitals on their own initiative in two-year study period. Among them, 65% (n = 26) were males and 35% (n = 14) were females. Among 22 (55%) cutaneous leishmaniasis cases as con rmed by this study using parasitological and/or molecular diagnosis, genderwise disribution showed 59. 09% (n = 13) male while 40. 9% (n = 9) were female. The research data showed that highest (n = 7) number positive cutaneous leishmaniasis cases fell into age group ≤ 20 years and in 41-60 years age group while the lowest (n = 1) number fell into age groups > 80 years (Fig. 1 ). Cutaneous leishmaniasis cases were detected from all the geographical landscapes of Terai (latitude 26 0 ), hill (Latitude 27 0 -29 0 ) and mountain (latitude 29 0 ) regions.
Evolutionary linkage based on sequencing and phylogenetic analysis The sequence of ITS1 region ampli ed by using LITSR-L5. 8S primers of seven tissue samples (CL-17, CL-18, CL-22, CL-23, CL-25, CL-29 and CL-40) and one from the culture (CL-29) showed two different compositions of base pairs (Table  3 ). The length of the sequences from 4 samples (CL17, CL23, CL29Tissue, CL29Culture) were of 337 bp (301 bp without primers) that of L. major, while rest of the 4 samples (CL18, CL22, CL25 and CL40) showed of 321 bp (285 bp without primers) that of L. donovani. NCBI BLAST analysis of these sequences revealed 99. 12% and 99. 47% homology with corresponding L. major (GenBank accession number: MT023531. 1) and L. donovani (GenBank accession number: MW053328. 1) sequences, respectively. The ML tree constructed by MEGA7 (Fig.
This distinction is the indication that the ITS1 marker is probably not su cient to separate these extremely closely related species. Lower bootstrap value on the phylogenetic analysis with indication of paraphyletic cluster and 99. 47% homology with L. donovani from the non-endemic state in southern India (GenBank accession number: MW053328. 1) sequences is suggestive that the causative L. donovani species might be a new variant. So, further molecular analysis of these clinical isolates is needed to determine genetic makeup of the emerging atypical L. donovani variants. Our ndings on L. donovani as the causative agent of cutaneous leishmaniasis is similar to other countries in Indian subcontinent like in Srilanka  25  and Bhutan 26 and India  27. It would be an interesting study whether the L.
5. 0 (GeneCodes Corporation, MI, USA) through base calling and sequences were generated by using both strands consensus. For the present study, the ITS1 DNA sequences of the Leishmania spp parasites isolated from the seven PCR-con rmed cutaneous leishmaniasis patients were uploaded to National Center for Biotechnology Information (NCBI) and obtained respective accession numbers. For the identi cation of Leishmania spp. , the nal sequence obtained from each sample was subjected to Nucleotide Basic Local Alignment Search Tool (BLAST) to search similarity with the Leishmania spp. sequences stored on NCBI database (Shrestha et al 2019). Sequence alignment was performed by using the algorithm ClustalW; the best model of nucleotide substitutions was selected, based on the best BIC score (Bayesian Information Criterion), a Maximum Likelihood tree (ML tree) was constructed using the selected model and a statistical phylogeny test based on bootstrap of 500 replicates  43  was applied; all these steps were achieve by the MEGA7 software  44.
Acknowledgements Authors are thankful to  Dr. Christian Barnabe ,  INTERTRYP ,  Univ Montpellier, IRD, CIRAD, Montpellier, France , for helping us to develop Maximum Likelihood method and  Jukes-Cantor  model phylogenetic tree. Thanks to  Dr. Laxman Khadka ,  Central Department of Zoology, Tribhuvan University  for geographic information system (GIS) based Nepal map. Prof. Dr. Shyam Sundar ,  Dr. Rajiv Kumar Shrivashtava  and  Anurag Kumar Kushwaha ,  Banaras Hindu University  are thankful for providing the reference strain LEM 138 Leishmania donovani parasite. Thanks to  Infectious and Viral Disease Research Laboratory (IVDRL ) team members  Bandana Thakur ,  Chetana Khanal ,  Machchhendra Thapa ,  Roji Raut ,  Shishir Gautam ,  Tika Bahadur Budha  and  Ramanuj Rauniyar  for their supportive cooperation during the work.
REAGENT or RESOURCE SOURCE IDENTIFIER The prospective observational EMIC study (Study of microRNAs in Decompensated Cirrhosis; Clinical-Trials. gov ID number NCT03905746) was launched at the University Hospital of Lyon (France) to investigate the alterations of circulating miRs in patients with AD of liver cirrhosis, and notably to investigate their potential role as diagnostic biomarkers of bacterial infection. EMIC sample size was calculated based on an alternative hypothesis of AUC = 0. 80 for the new diagnostic test in discriminating subjects with and without bacterial infection. We used a two-sided test:H1: AUC = 0. 8, vs. H0: AUC = 0. 5, power = 90%, a = 0. 025. The ratio between infected and non-infected AD patients was set at 1:2 in agreement with previous published data. 1. miR profiles were analyzed in 57 AD and 24 CC patients, after exclusion of technical and clinical failures (Figure  1 ).
Demographics, liver disease-related events, biochemical data and biological samples were prospectively collected within the 48 hours of the hospital admission for AD patients and at the time of inclusion for CC patients. Clinical and biochemical data were also collected at day 2, day 7 and 6 months after inclusion. ACLF, defined by AD of chronic liver disease associated with one or more organ failures (renal, coagulation, brain and/or pulmonary), and associated with a high 1-month mortality compared to AD without ACLF, was graded following Moreau at al. 43  Secondary infection was defined as an infection acquired during hospitalization and that was not present at the time of admission. Unproven sepsis was defined as clinical suspicion of infection (fever or leukocytosis) without meeting the established diagnostic criteria described above.