dataesr/scientific-paragraphs-categorization huggingface dataset:
F I G U R E 6 7 67 RHOH reconstitution inhibits the growth of AML tumours in vivo. The AML cell line OCI-RhoH that stably expresses the RHOH vector pMEP-RhoH and its sister line OCI-Empty that stably expresses the same vector empty of RHOH were injected subcutaneously into SHrN ™ Hairless NOD. SCID mice (Harlan Laboratories, Inc. , Indianapolis, Indiana, USA). In one experiment 5 million OCI-Empty cells were injected into 8 mice and in parallel 5 million OCI-RhoH cells were injected into 7 mice. In a second experiment 10 million OCI-Empty or OCI-RhoH cells were each injected into 5 mice. The volumes of palpable tumours were measured 3 weeks after injection. Tumour volumes beyond 1. 5 times the interquartile ranges of the ordered OCI-Empty and OCI-RhoH datasets were removed (Supplemental Table 1).
After fixation and permeabilization (Cytofix/ Cyoperm, BD, Milan, Italy), cells were incubated with anti-IFNg FITC (Life Technologies) and anti-TNF PE (BD) antibodies. Results obtained from cells incubated with media alone (NT) were subtracted from those obtained from cells incubated with the peptide pool (background subtraction). Boolean gates were performed on IFNg, TNF, and CD107. Cytokine responses were background subtracted individually before further analysis. All acquisitions were performed on a FACS CANTO II (BD) upon compensation conducted with antibody capture beads (BD)  (28). Flow cytometry data was analyzed using FlowJo (version 10. 1, Tree Star Inc. ).
Protein Sequences and Alignment Complete protein sequences for the envelope protein (E), membrane glycoprotein (M), nucleocapsid phosphoprotein (N), RNA dependent RNA polymerase (RdRp), spike protein (S), ORF3a, and ORF6 of SARS-CoV-2 and, when applicable, the HCoVs NL63, 229E, OC43, and HKU1, were downloaded from NCBI (accession numbers reported in Supplementary Table  S3 ). MUSCLE algorithm was used to align multiple sequences.
The cultures were tested 10 days after stimulation against a bidimensional peptide matrix system (Supplementary Table  S1 ) by IFNg-ELISpot. Data were first analyzed for the intensity of matrix-specific responses in the two age groups. As shown in Figure  2A , IFNg release upon stimulation with single matrixes was generally higher in middleaged adults. In particular, statistically significant differences were observed against M1 and M7 matrixes (Figure  2B ). However, responses against some matrixes (i. e. M6 and M12) were similar between the age groups in terms of both magnitude and frequency (Figures  2A, B ). To assess the presence of cross-reactive responses due to memory T cells specific for common cold coronaviruses (HCoV), we stimulated PBMCs directly ex vivo with the same matrixes or with a pool of HLA-A2-presented epitopes derived from CMV, EBV, and HSV-1, as a control of recall responses.
ACKNOWLEDGMENTS We are grateful to  Dr. Deepak Paudel  ( Nepal Public Health Association ) and  Dr. Maria Teresa Solis-Soto  ( O'Higgins University ) for fruitful discussion of the results and to the  Ferrara Blood Bank (AVIS) ,  Dr. Maurizio Govoni  ( Ferrara University Hospital ) and  Prof. Roberta Piva  ( University of Ferrara ) for the support on sample collection.
Quantification of Plasma Cytokines, Immunoglobulins and Antigen-Specific Antibodies Serum CXCL13 was detected using Quantikine ® ELISA kits (Bio-Techne, Lille, France) with sensitivity limit of 3. 97 pg/mL. Serum IFNα2 was detected using the Cynomolgus/Rhesus IFNα ELISA kit from PBL Assay Science (Bio-Techne) with a sensitivity limit of 25 pg/mL. Serum BAFF and CXCL10 were detected using a magnetic Luminex Kit (Bio-Techne) with sensitivity limits of 1. 01 pg/mL and 1. 18 pg/mL, respectively. Data were acquired using a Bioplex-200 and analyzed with the Bioplex Manager Software (Bio-Rad Laboratories, Redmond, WA, USA). Anti-SIV Abs (IgM plus IgG) were detected in plasma using Genscreen HIV1/2 ELISA kit, version 2 (Bio-Rad Laboratories, Redmond, WA, USA). Total IgM and IgG were quantified in serum by ELISA as previously described  [45].
Immunohistochemistry and Digital Image Analysis Sections (4µm-thick) were cut from formalin-fixed paraffin-embedded spleen blocks. Sections were subject to dewaxing, Ag retrieval, saturation and staining with various monoclonal and polyclonal Abs (Table  S4 ) on a Leica-Bond III/Max autostainer platform (Leica Biosystems Nanterre, France). Detection of primary Ab binding was performed with bond compact polymer Refine detection (DAB, brown), containing substrate chromogen and hematoxylin counterstain. Images of full section were generated on a Lamina Multilabel Slide Scanner (PerkinElmer, Gif s/Yvette, France), using the brightfield scan mode. Digital images were opened in Pannoramic Viewer software (v1. 11. 4, 3DHistech, Budapest, Hungary), and areas of interest were manually annotated using the drawing tools.
For each section, all (or at least 10) random areas were extracted from the main scan for quantitative analysis. The software automatically calculated the sizes of the whole tissue and of the selected areas (in µm 2 ). Quantification of positively labeled cells was performed with computer assisted image analysis using Inform (v2. 3, 3DHistech, Budapest, Hungary) or Photoshop CS6 (Adobe Systems Inc. , San Jose, CA) software by two independent investigators. Mean values of positive cells per area or per mm 2 of tissue were calculated for each section and each macaque.
Statistical Analysis All data were graphed and analyzed using Graphpad Prism (GraphPad Software, San Diego, CA, USA). Bar graphs represent Mean and SD. For pairwise comparisons, data were analyzed using Mann-Whitney U-test (unpaired, 2-tailed). One and 2-way ANOVA was used for multiple comparisons. Correlation coefficients were calculated using the Spearman rank test. Statistical significance is denoted on each figure.
Follicular regulatory T-cells (T FR , FOXP3 + ) were rare in GC in both settings but higher number per GC and B-cell follicle were found in the Group TT (Figure  5J ). Accordingly, the ratio between T FH and T FR was significantly lower in TT-boosted animals compared to the Group SIV + (6. 1 ± 1. 8% vs. 22. 4 ± 3%) (Figure  5K ). titers were considered at day 4 pb and day 14 pi for the groups TT and SIV + , respectively; b Ten SIV-infected macaques and 6 TT-vaccinated macaques were considered for correlation analysis. Statistically significant values are indicated: * p < 0. 05, ** p < 0. 01 and **** p < 0. 0001; ns, not significant. Viruses 2021, 13, x FOR PEER REVIEW 13 of 20 settings (data not shown). When all macaques were considered, proportions of splenic TFH significantly correlated with those of GC B-cells (Figure  5D ) and with levels of inflammatory cytokines, in particular CXCL13 which can guide TFH recruitment into GC (Table  2 ).
Acknowledgments: The authors would like to thank all members of the ASW and L2I groups of IDMIT. The authors also thank  K. Bailly  and  P. Vallin  from the CYBIO core facility as well as  M. Favier ,  R. Onifarasoaniaina  and  F. Ely-Marius  of the  HisTIM core facility  for their skillful technical assistance.
800 and 530 bp long fragment for sequencing, respectively. When unsuccessful, the V1f-UNIr fragment was used for sequencing. Even if this fragment is short (ca. 350bp long) it contains enough phylogenetic information to assign sequences to a species-level taxon without any ambiguity  (Quiles et al. , 2019, Table   S2 ). Prior to sequencing, the PCR products were cleaned using the EXO-FastAP method (Thermo Scientific). PCR products were purified and sequenced directly with the BigDye technology by Genewiz, Inc. , UK, using the V1f primer. Using Geneious 10. 2. (Kearse et al. , 2012) , raw sequences were edited, trimmed and confirmed as microsporidian sequences using the BlastN search tool available in GenBank/NCBI  (Madden, 2003).
Sequence dataset and phylogeny reconstruction for Microsporidia Four types of microsporidian SSU rDNA sequences were included in our dataset: (i) sequences newly produced from our collection of infected G. balcanicus; (ii) published sequences of Microsporidia infecting European freshwater or brackish water amphipods, representing parasite diversity and divergence; (iii) published sequences for Microsporidia infecting other taxa closely related to newly produced sequences, prioritizing freshwater or brackish water invertebrates; (iv) published sequences representative of the five Microsporidia clades (Clades I-V), as determined in the integrative phylogenies presented in literature  (Vossbrinck and Debrunner-Vossbrinck, 2005; Williams et al. , 2018). Detailed information on the sequences used (e.
g. GenBank accession numbers, host name, assignment to a fully described species or MOTU) are presented in Table  S2. Sequences were aligned using MAFFT7. 388 software  (Katoh and Standley, 2013) , incorporated in Geneious 10. 2. 2, with the E-IONS-I algorithm using the legacy gap penalty option. Our dataset contained sequences of different lengths, both among the newly produced sequences and the published ones (Table  S2 ). As some sequences were relatively short, reducing the full dataset to a common size would allow, on the one hand, defining haplotypes but, on the other hand, would potentially reduce phylogenetic information content. Therefore, following the strategy defined by  Quiles et al. (2019) , we assigned each sequence to a haplogroup. Sequences belonging to distinct haplogroups possessed at least one variable site in the shared part of the two aligned sequences, therefore generating diagnostic features.
The 50% majority-rule consensus tree was constructed after the removal of 10% ‛burn-inʼ trees. We used Basidiobolus ranarum (GenBank: AY635841) as the outgroup, following  Vossbrinck and Debrunner-Vossbrinck (2005). We identified our sequences to species level when they grouped together with type sequences of fully described Microsporidia in welldefined, supported clade (see results). In cases where our sequences were grouped with undescribed Microsporidia, we referred such groups of sequences as to Molecular Operational Taxonomy Units (MOTUs). The sites reported on Figs. 1 and 3 were plotted on a map using QGIS   S1  (87). See Additional Table  S1  for details (e. g. sampling sizes, GPS coordinates). Countries identified with ISO code. Map created by authors using Qgis 2. 18. 4 (QGIS Development Team 2009).
S1 ), the number of infected populations (=pop. ), and the total number of infected individuals (=ind. ). Labels with accession numbers are parasite sequences taken from GenBank. These labels show the accession number, the parasite name given in the associated publication, the order of the host (except for amphipod hosts where the family is provided). Microsporidia clade numbers are as in  Vossbrinck and Debrunner-Vossbrinck (2005). Branches are collapsed for the two genera Nosema and Dictyocoela (triangle sizes not reflecting actual size). Abbreviation: PP, Bayesian posterior probability. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ).
Acknowledgments The sampling was performed during the series of  Amphi-Balkan Expeditions  in the years 2004-2016 financed from the internal funds of the  University of Lodz  and partially by the  Polish Ministry of Science and Higher Education , grant numbers N  N303 579439  and  3 P06K 030 23. We acknowledge the  French National Research Agency (ANR)  (Grant #  ANR-15-CE32-0006 ) for paying AQ salary, for contribution in designing the study, molecular analyses, statistical analyses, writing the manuscript and paying publication fees; as well as the  'Polonium' exchange program  ( PPN/BIL/2018/1/00138 ), for funding travel between France and Poland, designing the study, analysing the results and writing the manuscript. We would like to thank  Tomasz Mamos  for sharing DNA extracts from G.
Competing interests: No authors have competing interests. All sections of this report adhere to the ARRIVE Guidelines for reporting animal research  [20]. Sample size was calculated using G � Power Software  [21]. Based on our previous study  [17] , a difference in colon wall thickness on MRC between control and TNBS group was shown with an effect size of 3. 7, and mean mortality rate was 30%. We estimated that effect size would be 50% lower (= 1. 9) using a dose of TNBS reduced by 50% to take into account higher susceptibility to drugs in young animals. To achieve power = 0. 80 and alpha = 0. 05 to detect this difference would require 6 animals per group, which we increased to 8 based on a predicted attrition rate of 30%.
0 x 5. 3 cm, acquisition time: 10 minutes, RARE factor 8. Parameters for T2 RARE with fat suppression (FAT-SAT): ET 39 ms, RT 6027 ms, RARE factor 8, NEX 3, acquisition time 8 minutes, flip angle 180˚, slice 1 mm, FOV 5. 0 x 5. 3 cm, matrix 320x224. T1 sequence with intragate fast low angle shot (FLASH); RT 413 ms, ET 2. 8 ms, matrix 256x256, slice 1. 1 mm, flip angle 80˚, FOV 4. 5 x 4. 5 cm; acquisition time: 14 minutes. MRC images were analysed in DICOM, using ParaVision 5. 0 software. A senior radiologist (C. S. -C. ) who was blind to allocation group interpreted all images. Image quality taking into account respiratory and bowel wall movements was assessed on a scale of 0 (poor quality) to 3 (excellent quality). Measures were made in the descending colon. In order to assess inflammation, the criteria used were: maximal colon wall thickness (average of 3 measures), minimal colon wall thickness (average of 3 measures), colon wall thickness at splenic angle (average of 3 measures), colon wall signal intensity in Regions of Interest (ROI) on T2w sequences (average of 2 measures), target sign, colon mucosa irregularities suggesting ulcerations, spontaneous enhancement of colon wall T1w signal  [17].
Colon expression of cyclooxygenase-2 (COX-2) by Western Blot Frozen colon samples were homogenized in PBS with 0. 1% protease inhibitor cocktail (Sigma) and phosphatase inhibitor cocktail (Sigma). Homogenates were centrifuged (12 000g, 15 min, 4˚C) and supernatants were collected and stored at -80˚C. Protein concentration was determined following Bradford's colorimetric method and Western blot for COX-2 was performed as previously  [23]. Protein expression was quantified by densitometry with the ImagQuantTL software (GE Healthcare, USA). To check equal loading, the blots were analyzed for glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression.
Data analyses Characteristics of the rats were compared between groups. Since group sizes were < 30, nonparametric methods were used for analyses: qualitative variables were compared using Fisher's exact test, quantitative variables were compared using Wilcoxon Mann-Whitney with exact correction for small samples. Missing data (following premature death of an animal) was excluded from analyses. Statistical analyses were performed using SAS 9. 2 (Cary, NC, USA) and Graph Pad Prism 5 (San Diego, CA, USA). A two-sided p-value < 0. 05 was considered significant. Magnetic resonance colonography. Bowel wall thickness measured in descending colon on axial plane, T2 sequence, was significantly increased in the TNBS group compared to controls (Table  1  and Fig 3  , Panel A ). MRC signs suggestive of inflammation, such as a target sign, wall ulcers, and increased wall signal intensity on T2w images were significantly more prevalent in the TNBS group than in controls (Table  1 ).
Altogether, these data show that Bnl is produced by both MZ and cardiac cells and that both sources are required in the control of lymph gland homeostasis. To determine whether Bnl produced by cardiac cells contributes to the pool of Bnl present in the MZ, we analyzed endogenous Bnl distribution. We used the bnl:GFP endo knock-in allele that recapitulates bnl expression  (Du et al. , 2018). In agreement with in situ bnl detection, bnl:GFP endo was found in cardiac cells and in MZ progenitors (Figure  2s -s'). However, when bnl was knocked-down only in the cardiac tube (hand>bnl RNAi, bnl:GFP endo ; Figure  2   Since the PSC controls lymph gland cell differentiation  (Benmimoun et al. , 2015; Morin-Poulard et al. , 2016; Oyallon et al. , 2016; Tokusumi et al. , 2010) , we also looked at PSC cells by analyzing the expression of the PSC marker Antp  (Mandal et al.
Statistical analyses (Mann-Whitney nonparametric test) were performed using GraphPad Prism 5 software. Since the number of lymph gland differentiated blood cells fluctuates depending on the larval stage, and to limit discrepancies in all the experiments, genotypes were analyzed in parallel. Quantification of hhF4-GFP and UAS-GCaMP3 intensityOptimized confocal sections were performed on Leica SPE or SP8 microscopes for 3D reconstruction. For hhF4-GFP, the sum intensities for GFP per PSC labelled by Col and each PSC volume (in μm3) were measured using Volocity 3D Image Anaysis software (PerkinElmer). The intensity of hhF4-GFP corresponds to the sum intensity of hhF4-GFP/the PSC volume. For GCaMP3, the sum intensities for GFP per lymph gland primary lobe labelled by TOPRO and each primary lobe volume (in μm3) were measured using Volocity 3D Image Anaysis software.
The intensity of GCaMP3 corresponds to the sum intensity of GCaMP3/per lymph gland primary lobe volume. At least 15 anterior lobes were scored per genotype, and experiments were reproduced at least three times. Statistical analyses (Mann-Whitney nonparametric test) were performed using GraphPad Prism 5 software. Quantification of the diffusion in the MZ of cytoplasmicBnl::GFP dots, in hand>Bnl::GFP and hand>Bnl::GFP>sar1-RNAi genetic contexts Optimized lymph gland confocal sections were obtained with a Leica SP8 microscope for 3D reconstruction. The maximum projection of 10 slices chosen in the middle of the stack was performed. A parallelepiped with a larger corresponding to 4 nuclei diameter, a width of 10 confocal slices a length corresponding to the distance from the CT to the CZ was designed.
Acknowledgements. We thank  M. Affolter ,  Y. Bellaiche ,  J. Casanova ,  J. Colombani ,  L. Du ,  M. Freeman ,  M. Grammont ,  M. A. Krasnow ,  A. Paululat ,  L. Perrin ,  S. Ricardo ,  S. Roy ,  T. Tanaka ,  P. Thérond ,  Bloomington and Vienna Stock Center  and the  TRiP at Harvard Medical School  for fly strains;  I. Ando ,  A. Moore  and  T. Trenczek  for antibodies;  L. Bataillé ,  A. Davy ,  G. Lebreton ,  M. Meister ,  C. Monod ,  B. Monnier  and  A. Vincent , for critical reading of the manuscript. We are grateful to  B. Ronsin  and  S. Bosch  for assistance with confocal.
Furthermore, comparative gene expression analysis from the microarray dataset of mosquito infected with dengue, yellow fever and West Nile viruses revealed 31 commonly expressed ABC transporters suggesting a potentially conserved transcriptomic signature of arboviral infection. Among these, only a few transporters of ABCA, ABCC and ABCF subfamily were upregulated, while most were downregulated. This indicates the possible involvement of ABC transporters in mosquito immunity.
Genomic Location and Curated Details of Ae. aegypti ABC Transporters We have identified a total of 59 putative ABC transporter genes from Ae. aegypti genome assembly 5 (AaegL5)  [25] , which is in contrast to previous studies performed by  28]  that reported 69 and 53 ABC transporters, respectively from Ae. aegypti genome. These reports included long non-coding RNA, uncharacterized protein, and pseudogenes, which is now curated in the recently updated genome assembly (AaegL5)  [25]. Our analyses revealed that among 59 transporter genes, 47 genes have expression sequence tags (ESTs) support, while the remaining 12 genes are without ESTs, however, they all showed the evidence of expression in at least one of the analyzed transcriptomic datasets (Table  1 ). Based on the An. gambiae transporters classification  [27] , Ae.
Expression Profile of Ae. aegypti ABC Transporters upon Arboviral-Infections During arboviral infection in the mosquito, the virus must disseminate from the midgut (MG) through the body (CC) to the salivary gland (SG). Therefore, the gene expression is likely to be altered in different organs at various time points. To determine the spatial and sequential expression of Ae. aegypti ABC transporters, we analyzed datasets available The qPCR data of the selected genes (Figure  4 ) revealed a similar pattern when compared to the RNA-seq data (Figure  3 ). As mentioned previously, AaeABCC9 showed maximum expression in male carcass which is also observed in the qPCR data for this gene. AaeABCF2 gene shows expression throughout the development of mosquitoes with maximal expression in 1st instar larvae and adult stage (in both male and female) which is also evident in the qPCR data.
Expression Profile of Ae. aegypti ABC Transporters upon Arboviral-Infections During arboviral infection in the mosquito, the virus must disseminate from the midgut (MG) through the body (CC) to the salivary gland (SG). Therefore, the gene expression is likely to be altered in different organs at various time points. To determine the spatial and sequential expression of Ae. aegypti ABC transporters, we analyzed datasets available from microarray experiments on three arboviral infections viz. DENV, YFV and WNV  [7, [46] [47] [48]. Infection kinetics was generated as per virus progression in the mosquito, beginning from its ingestion to readiness for subsequent transmission [on Day1, D2, D7, D10 and D14 post-infection (dpi)] in the whole mosquito as well as in its different organs including CC, MG and SG.
Identification and Classification of Ae. aegypti ABC Transporters To identify putative Ae. aegypti ABC transporters, protein sequences of already reported fifty-five An. gambiae ABC transporters  [24, 27]  were retrieved from VectorBase  [57]  and subjected to BLASTp search against Ae. aegypti genome assembly 5 (AaegL5)  [58]. The retrieved hits from Ae. aegypti genome that exhibited blast e-value <10 -6 were further screened for ABC transporter specific signature domains through the conserved domain database (CDD) tool at NCBI  [59]. Furthermore, the profile hidden markov model was adopted to characterize the NBDs and TMDs in these identified putative Ae. aegypti ABC transporters  [29]. Finally, the presence of ABC transporter sequences in Ae. aegypti was confirmed through BLASTp search in its genome using already identified ABC transporters from D.
Phylogenetic Analysis of Ae. aegypti ABC Transporters To understand the evolutionary relationship of putative Ae. aegypti ABC transporters, we performed their phylogenetic analyses with An. gambiae, Cx. quinquefasciatus, D. melanogaster, and Ae. albopictus ABC transporters, using the neighbor-joining (NJ) method in MEGAX program  [33]. The evolutionary distances were computed using the Poisson correction method and represented as the unit of number of amino acid substitutions per site. All positions with less than 95% site coverage were eliminated, i. e. , fewer than 5% alignment gaps, missing data, and ambiguous bases were only allowed at any position (pairwise deletion). The tree topology confidence was assessed through bootstrap analysis using 1000 replicates.
Transcriptomic Data Analysis of Ae. aegypti ABC Transporters To analyze the expression patterns of ABC transporters during mosquito development, all 42 RNA-seq libraries initially described by  Akbari et al. [45]  and already remapped to the Ae. aegypti AaegL5 assembly was retrieved from VectorBase (https://vectorbase. org/ vectorbase/app/search/transcript/GenesByRNASeqEvidence#GenesByRNASeqaaegLVP_ AGWG_SRP026319_ebi_rnaSeq_RSRC, accessed on 15 July 2020) and analyzed using R program. First, the raw counts per gene were calculated from RNA-seq data using "fea-tureCounts" [60]. These raw counts were further converted to the fragments per kilobase per million mapped reads (FPKM) using "countToFPKM" module. The obtained FPKM values were log10-transformed (by adding 1 to it) before generating a heatmap by "Com-plexHeatmap" using K-means clustering on rows as reported before  [61].